VA Big Data Science: A Model for Improved National Pandemic Response Present and Future

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Changed
Tue, 10/31/2023 - 16:36

The COVID-19 pandemic emphasized the need for rapid response research in health care. The robust enterprise approach used by the US Department of Veterans Affairs (VA), termed VA Research, is meeting these needs by using existing outstanding data resources and interdisciplinary collaborations.1 In the first 7 months of 2021 alone, while many US health care systems struggled with limited data, VA Research published more than 300 unique and instrumental research papers addressing urgent questions about transmission, vaccination, therapeutics, and health impacts of COVID-19 on its high-risk population.1 The ability to leverage the VA electronic health record (EHR) and Corporate Data Warehouse (CDW)—a fully established data system bringing together test results, prescriptions, and complete patient health records, readily accessible and updated daily—was substantial.

With more than 9 million veterans enrolled in care at 171 medical centers and 1113 outpatient facilities across the US and its territories, the CDW provides an unprecedented opportunity to examine outcomes in real time. This allowed research groups such as the VA St Louis Health Care System Research and Education Service to build a cohort of 181,280 veterans with diabetes and positive COVID-19 test results within a 6-month period in 2021 to study the incidence of new diagnoses of diabetes after COVID-19 infection.2 Similarly, the Clinical Epidemiology Program (CEP) at VA White River Junction Health Care System built a cohort of 1,363,180 veterans who received at least 1 COVID-19 vaccine by March 7, 2021, to analyze coverage and effectiveness of those vaccines.3 This time-sensitive research was possible because the VA had the data and tools in place. Moreover, the the CEP quickly built an infrastructure to make its cohort and programming codes available to researchers in and outside the VA, resulting in additional significant research.4

The innovation and speed of COVID-19 vaccine development and distribution in the US were unprecedented. The rapid discovery and implementation of multiple preventives and therapeutics for COVID-19 could not have been possible without shared information within a competitive industry. VA studies added significantly to understanding the clinical performance of the messenger RNA (mRNA) COVID-19 vaccines, antivirals, and monoclonal treatments in a real-world setting. For example, a vaccine coverage study by VA Research illustrated how successful vaccination for COVID-19 at the VA has been in protecting a diverse community of patients from hospitalization and death, particularly the highly comorbid, racial and ethnic minorities, and other high-risk populations.3 The study demonstrated the power of the VA system to generate robust and compelling clinical endpoint effectiveness data across a broad range of high-risk groups.

This success is promising. However, the COVID-19 pandemic is not over, and the next could prove even more challenging. For example, through a recent partnership with the US Department of Defense (DoD), the VA was able to rapidly analyze the effectiveness of previous smallpox vaccination efforts in the military for preventing mpox infections.5 We should take this opportunity to think creatively about ways to improve our existing infrastructure based on what we have learned.

 

 

A Role for VA Research in Efficacy

The US Food and Drug Administration (FDA) Reauthorization Act of 2017 requires that manufacturers submit evidence establishing a product’s benefits (effectiveness) outweigh its risks (safety) before it can be promoted and distributed.6 As such, the FDA has been obligated by external stakeholders and Congress to be more explicit and transparent about benefit-risk profile supporting its decisions on licensure. This process led to requiring more phase 4 postmarketing observational studies for safety and effectiveness.7 Although the FDA postlicensure system remains vigilant toward safety, effectiveness information is limited due to insufficient reporting (with exceptions of manufacturer studies for new indications or to exhibit superior comparative effectiveness). The agency typically relies on a static set of efficacy data generated prelicensure with a dynamic and evolving set of safety data accrued postlicensure to support its assessment that benefits outweigh risks.

For example, operating in near real time, postauthorization safety monitoring systems, led by the Centers for Disease Control and Prevention and other federal systems, identified a safety signal for thrombosis following the Janssen COVID-19 vaccination. Distribution was quickly paused, the safety signal was investigated, the magnitude of the risk was characterized, new language describing the risk and providing guidance regarding clinical management was included in labeling, and distribution was resumed, all within a few weeks. This remarkable success demonstrated how timely the safety system can operate to evaluate risk.

In contrast, the duration and extent of protection against COVID-19 variants are largely limited to the assessment of immune biomarker surrogates. Such clinical effectiveness data are urgently needed for the FDA’s Center for Biologics Evaluation and Research and Center for Drug Evaluation and Research to make accurate benefit-risk assessments and continue to conclude the balance is favorable. As we prepare for the next pandemic, we must consider plans for monitoring postauthorization/postlicensure effectiveness as well as safety in real time. VA Research is ideally situated for this task.

Published studies on effectiveness at the VA serve as a prototype and could lead the way to initiating those preparations.4,8-11 One of the striking features of the VA system that became apparent in the preparation of the mRNA vaccine study was the speed at which an enormous volume of COVID-19 testing data were produced. This enabled implementation of methodologically sound test-negative and case-control analysis. Analyses sufficiently powered to conclude mRNA vaccines were highly effective when used in real-world conditions among a diverse population from nearly every state and territory during a period in which multiple COVID-19 variants were already circulating.3 This is unique to the VA and would not be possible for any other US health care system. With planning, the VA system could produce product-specific, real-world evidence of effectiveness comparable to the timeliness and quality of the safety data currently produced to support regulatory benefit-risk assessments. For example, the VA conducted an effectiveness study of tixagevimab/cilgavimab for preventing COVID-19 during the initial Omicron surge, which is continually updated while Omicron circulates and repeatable for different subvariants.12

The FDA continues to collaborate with the VA on demonstration projects to evaluate the impact of available vaccines and treatment against COVID-19 variants. The VA has also initiated several large-scale sequencing programs for COVID-19 specimens that will support these efforts, including VA Science and Health Initiative to Combat Infectious and Emerging Life-Threatening Diseases (VA SHIELD), VA Sequencing for Research Clinical and Epidemiology (SeqFORCE), and VA Sequencing Collaborations United for Research and Epidemiology (SeqCURE).13,14 Successful proof-of-concept studies using these data could provide a template for VA and other medical systems/databases to report effectiveness in near real time.

 

 

Interagency Collaboration

The potential advantages of federal agencies working with the VA to build an infrastructure capable of generating real-world evidence effectiveness analyses in near real time is not limited to needs that will arise in the next pandemic. For example, generating randomized, placebo-controlled, clinical trial endpoint data on the effectiveness of new variant vaccines will be difficult from a feasibility and ethical standpoint. Combining the VA’s robust virus sequencing program with preexisting mechanisms, such as expanded access studies (allowed under FDA Investigational New Drug regulations), researchers could enable a large-scale effective evaluation program of vaccination with variant or universal COVID-19 vaccines, using rapidly accruing effectiveness data.

The pandemic created opportunities to advance innovative approaches to medical product development. Some have advocated these innovative approaches should proceed together toward a seamless convergence between the domains of medical research and clinical care. A shift toward expecting, as a matter of routine, effectiveness data to be generated in near real time and made available for benefit-risk assessment would be a useful step in that direction.

Expanding and sharing analytical platforms, including methodology and programming codes, will allow increased access to rapidly refreshed real-world data. A common adaptive platform of complete and continuously updated data will also enable a wider community of researchers to create multiple investigatory groups simultaneously accessing fully de-identified data for concurrent observational studies. In turn, researchers need to have programming, study design, and methodology ready in an open-source platform. An efficient platform would also require the adoption of artificial intelligence, natural language processing, imaging processing, and quantum computing for validation and improved data quality.

COVID-19 has demonstrated the need for open science data synchronization with universal access for faster action and improved outcomes able to gain public confidence. OpenSafely (UK), a software platform for analysis of EHR data that is shared automatically and openly for scientific review and efficient reuse, created a cohort of about 23.4 million records for observational review of monoclonal COVID-19 treatments. To keep pace with the UK, Israel, and other nationalized systems, the US would benefit from duplicating this example of coordination between federal agencies and their data repositories. For example, combining data between the DoD, which captures active military health care data through TRICARE, and VA, which follows postmilitary discharge, would create datasets encompassing complete life spans. Additionally, expanding the National COVID Cohort Collaborative (N3C) program—one of the largest collections of clinical data related to COVID-19 symptoms and patient outcomes in the US—to include EHR data from DoD, VA, Medicare, and Test to Treat initiative partners would further expand research capabilities. This could be accomplished through a framework of anonymized, readily available, harmonized data. EHRs with synchronized datasets from every health care practitioner—independent pharmacies, primary care physicians, and hospitals—could all work to create a de-identified, comprehensive, continuously updated, near real-time dataset accessible to all federal researchers.

Conclusions

The VA has been lauded for its rapid, effective response to the current pandemic. The successful management and prescription of vaccines and treatment to the largely high-risk veteran population was possible because of the existing data framework within the VA. VA Research continues to build and refine infrastructure to improve speed, quality, and value of data analytics. We can do more. Expanding partnerships to use existing VA data strategies in designing a cooperative national data alliance would deliver necessary progress to research and public health.

Acknowledgments

The authors thank Jeff Roberts, MD, for his insight on the US Food and Drug Administration, its responsibilities, and the potential benefit of real world data to its missions.

References

1. US Department of Veterans Affairs, Veterans Health Administration. Third report details VA’s continued efforts addressing COVID-19 pandemic. Accessed August 15, 2023. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=5748

2. Xie Y, Ziyad A. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10(5):311-321. doi:10.1016/S2213-8587(22)00044-4

3. Young-Xu Y, Korves C, Roberts J, et al. Coverage and estimated effectiveness of mRNA COVID-19 vaccines among US veterans. JAMA Netw Open. 2021;4(10):e2128391. doi:10.1001/jamanetworkopen.2021.28391

4. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of BNT162b2 and mRNA-1273 vaccines in U.S. veterans. N Engl J Med. 2022;386(2):105-115. doi:10.1056/NEJMoa2115463

5. Titanji BK, Eick-Cost A, Partan ES, et al. Effectiveness of smallpox vaccination to prevent mpox in military personnel. N Engl J Med. 2023;389(12):1147-1148. doi:10.1056/NEJMc2300805

6. Sarata AK, Dabrowska A, Johnson JA, Thaul S. FDA Reauthorization Act of 2017. Accessed August 15, 2023. https://sgp.fas.org/crs/misc/R44961.pdf

7. US Food and Drug Administration. FDA’s sentinel initiative–background. February 2, 2022. Updated February 4, 2022. Accessed August 15, 2023. https://www.fda.gov/safety/fdas-sentinel-initiative/fdas-sentinel-initiative-background

8. Bajema KL, Dahl RM, Prill MM, et al; SUPERNOVA COVID-19; Surveillance Group. Effectiveness of COVID-19 mRNA vaccines against COVID-19–associated hospitalization—five Veterans Affairs medical centers, United States, February 1–August 6, 2021. MMWR Morb Mortal Wkly. 2021;70(37):1294-1299. doi:10.15585/mmwr.mm7037e3

9. Sharma A, Oda G, Holodniy M. COVID-19 vaccine breakthrough infections in Veterans Health Administration. medRxiv. Posted September 26, 2021. doi:10.1101/2021.09.23.21263864

10. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol. 2023;8(1):55-63. doi:10.1038/s41564-022-01272-z


11. Tang F, Hammel IS, Andrew MK, Ruiz JG. Frailty reduces vaccine effectiveness against SARS-CoV-2 infection: a test-negative case control study using national VA data. J Nutr Health Aging. 2023;27(2):81-88. doi:10.1007/s12603-023-1885-1

12. Young-Xu Y, Epstein L, Marconi VC, et al. Tixagevimab/cilgavimab for preventing COVID-19 during the Omicron surge: retrospective analysis of National Veterans Health Administration electronic data. mBio. 2023;14(4):e0102423. doi:10.1128/mbio.01024-23

13. US Department of Veterans Affairs. VA science and health initiative to combat infectious and emerging life-threatening diseases. Open Forum Infect Dis. 2022;9(12):ofac641. doi:10.1093/ofid/ofac64

14. Bilal MY. Similarity index–probabilistic confidence estimation of SARS-CoV-2 strain relatedness in localized outbreaks. Epidemiologia. 2022;3(2):238-249. doi:10.3390/epidemiologia3020019

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Author and Disclosure Information

Yinong Young-Xu, ScD, MA, MSa,b; Victoria Davey, PhD, MPHc; Vincent C. Marconi, MDd,e; Francesca E. Cunningham, PharmDf

Correspondence:  Yinong Young-Xu  (yinong.young-xu@va.gov)

aWhite River Junction Veterans Affairs Medical Center, Vermont

bGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

cOffice of Research and Development, Department of Veterans Affairs, Washington, DC

dAtlanta Veterans Affairs Medical Center, Decatur, Georgia

eEmory University School of Medicine, Atlanta, Georgia

fCenter for Medication Safety, Pharmacy Benefits Management Services, Department of Veterans Affairs, Hines, Illinois

Author disclosures

Vincent C. Marconi received investigator-initiated research grants (to Emory University) and consultation fees from Eli Lilly, Bayer, Gilead Sciences and ViiV. The grants and fees were unrelated to the work discussed here.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies.

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Author and Disclosure Information

Yinong Young-Xu, ScD, MA, MSa,b; Victoria Davey, PhD, MPHc; Vincent C. Marconi, MDd,e; Francesca E. Cunningham, PharmDf

Correspondence:  Yinong Young-Xu  (yinong.young-xu@va.gov)

aWhite River Junction Veterans Affairs Medical Center, Vermont

bGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

cOffice of Research and Development, Department of Veterans Affairs, Washington, DC

dAtlanta Veterans Affairs Medical Center, Decatur, Georgia

eEmory University School of Medicine, Atlanta, Georgia

fCenter for Medication Safety, Pharmacy Benefits Management Services, Department of Veterans Affairs, Hines, Illinois

Author disclosures

Vincent C. Marconi received investigator-initiated research grants (to Emory University) and consultation fees from Eli Lilly, Bayer, Gilead Sciences and ViiV. The grants and fees were unrelated to the work discussed here.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies.

Author and Disclosure Information

Yinong Young-Xu, ScD, MA, MSa,b; Victoria Davey, PhD, MPHc; Vincent C. Marconi, MDd,e; Francesca E. Cunningham, PharmDf

Correspondence:  Yinong Young-Xu  (yinong.young-xu@va.gov)

aWhite River Junction Veterans Affairs Medical Center, Vermont

bGeisel School of Medicine at Dartmouth, Hanover, New Hampshire

cOffice of Research and Development, Department of Veterans Affairs, Washington, DC

dAtlanta Veterans Affairs Medical Center, Decatur, Georgia

eEmory University School of Medicine, Atlanta, Georgia

fCenter for Medication Safety, Pharmacy Benefits Management Services, Department of Veterans Affairs, Hines, Illinois

Author disclosures

Vincent C. Marconi received investigator-initiated research grants (to Emory University) and consultation fees from Eli Lilly, Bayer, Gilead Sciences and ViiV. The grants and fees were unrelated to the work discussed here.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies.

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Article PDF

The COVID-19 pandemic emphasized the need for rapid response research in health care. The robust enterprise approach used by the US Department of Veterans Affairs (VA), termed VA Research, is meeting these needs by using existing outstanding data resources and interdisciplinary collaborations.1 In the first 7 months of 2021 alone, while many US health care systems struggled with limited data, VA Research published more than 300 unique and instrumental research papers addressing urgent questions about transmission, vaccination, therapeutics, and health impacts of COVID-19 on its high-risk population.1 The ability to leverage the VA electronic health record (EHR) and Corporate Data Warehouse (CDW)—a fully established data system bringing together test results, prescriptions, and complete patient health records, readily accessible and updated daily—was substantial.

With more than 9 million veterans enrolled in care at 171 medical centers and 1113 outpatient facilities across the US and its territories, the CDW provides an unprecedented opportunity to examine outcomes in real time. This allowed research groups such as the VA St Louis Health Care System Research and Education Service to build a cohort of 181,280 veterans with diabetes and positive COVID-19 test results within a 6-month period in 2021 to study the incidence of new diagnoses of diabetes after COVID-19 infection.2 Similarly, the Clinical Epidemiology Program (CEP) at VA White River Junction Health Care System built a cohort of 1,363,180 veterans who received at least 1 COVID-19 vaccine by March 7, 2021, to analyze coverage and effectiveness of those vaccines.3 This time-sensitive research was possible because the VA had the data and tools in place. Moreover, the the CEP quickly built an infrastructure to make its cohort and programming codes available to researchers in and outside the VA, resulting in additional significant research.4

The innovation and speed of COVID-19 vaccine development and distribution in the US were unprecedented. The rapid discovery and implementation of multiple preventives and therapeutics for COVID-19 could not have been possible without shared information within a competitive industry. VA studies added significantly to understanding the clinical performance of the messenger RNA (mRNA) COVID-19 vaccines, antivirals, and monoclonal treatments in a real-world setting. For example, a vaccine coverage study by VA Research illustrated how successful vaccination for COVID-19 at the VA has been in protecting a diverse community of patients from hospitalization and death, particularly the highly comorbid, racial and ethnic minorities, and other high-risk populations.3 The study demonstrated the power of the VA system to generate robust and compelling clinical endpoint effectiveness data across a broad range of high-risk groups.

This success is promising. However, the COVID-19 pandemic is not over, and the next could prove even more challenging. For example, through a recent partnership with the US Department of Defense (DoD), the VA was able to rapidly analyze the effectiveness of previous smallpox vaccination efforts in the military for preventing mpox infections.5 We should take this opportunity to think creatively about ways to improve our existing infrastructure based on what we have learned.

 

 

A Role for VA Research in Efficacy

The US Food and Drug Administration (FDA) Reauthorization Act of 2017 requires that manufacturers submit evidence establishing a product’s benefits (effectiveness) outweigh its risks (safety) before it can be promoted and distributed.6 As such, the FDA has been obligated by external stakeholders and Congress to be more explicit and transparent about benefit-risk profile supporting its decisions on licensure. This process led to requiring more phase 4 postmarketing observational studies for safety and effectiveness.7 Although the FDA postlicensure system remains vigilant toward safety, effectiveness information is limited due to insufficient reporting (with exceptions of manufacturer studies for new indications or to exhibit superior comparative effectiveness). The agency typically relies on a static set of efficacy data generated prelicensure with a dynamic and evolving set of safety data accrued postlicensure to support its assessment that benefits outweigh risks.

For example, operating in near real time, postauthorization safety monitoring systems, led by the Centers for Disease Control and Prevention and other federal systems, identified a safety signal for thrombosis following the Janssen COVID-19 vaccination. Distribution was quickly paused, the safety signal was investigated, the magnitude of the risk was characterized, new language describing the risk and providing guidance regarding clinical management was included in labeling, and distribution was resumed, all within a few weeks. This remarkable success demonstrated how timely the safety system can operate to evaluate risk.

In contrast, the duration and extent of protection against COVID-19 variants are largely limited to the assessment of immune biomarker surrogates. Such clinical effectiveness data are urgently needed for the FDA’s Center for Biologics Evaluation and Research and Center for Drug Evaluation and Research to make accurate benefit-risk assessments and continue to conclude the balance is favorable. As we prepare for the next pandemic, we must consider plans for monitoring postauthorization/postlicensure effectiveness as well as safety in real time. VA Research is ideally situated for this task.

Published studies on effectiveness at the VA serve as a prototype and could lead the way to initiating those preparations.4,8-11 One of the striking features of the VA system that became apparent in the preparation of the mRNA vaccine study was the speed at which an enormous volume of COVID-19 testing data were produced. This enabled implementation of methodologically sound test-negative and case-control analysis. Analyses sufficiently powered to conclude mRNA vaccines were highly effective when used in real-world conditions among a diverse population from nearly every state and territory during a period in which multiple COVID-19 variants were already circulating.3 This is unique to the VA and would not be possible for any other US health care system. With planning, the VA system could produce product-specific, real-world evidence of effectiveness comparable to the timeliness and quality of the safety data currently produced to support regulatory benefit-risk assessments. For example, the VA conducted an effectiveness study of tixagevimab/cilgavimab for preventing COVID-19 during the initial Omicron surge, which is continually updated while Omicron circulates and repeatable for different subvariants.12

The FDA continues to collaborate with the VA on demonstration projects to evaluate the impact of available vaccines and treatment against COVID-19 variants. The VA has also initiated several large-scale sequencing programs for COVID-19 specimens that will support these efforts, including VA Science and Health Initiative to Combat Infectious and Emerging Life-Threatening Diseases (VA SHIELD), VA Sequencing for Research Clinical and Epidemiology (SeqFORCE), and VA Sequencing Collaborations United for Research and Epidemiology (SeqCURE).13,14 Successful proof-of-concept studies using these data could provide a template for VA and other medical systems/databases to report effectiveness in near real time.

 

 

Interagency Collaboration

The potential advantages of federal agencies working with the VA to build an infrastructure capable of generating real-world evidence effectiveness analyses in near real time is not limited to needs that will arise in the next pandemic. For example, generating randomized, placebo-controlled, clinical trial endpoint data on the effectiveness of new variant vaccines will be difficult from a feasibility and ethical standpoint. Combining the VA’s robust virus sequencing program with preexisting mechanisms, such as expanded access studies (allowed under FDA Investigational New Drug regulations), researchers could enable a large-scale effective evaluation program of vaccination with variant or universal COVID-19 vaccines, using rapidly accruing effectiveness data.

The pandemic created opportunities to advance innovative approaches to medical product development. Some have advocated these innovative approaches should proceed together toward a seamless convergence between the domains of medical research and clinical care. A shift toward expecting, as a matter of routine, effectiveness data to be generated in near real time and made available for benefit-risk assessment would be a useful step in that direction.

Expanding and sharing analytical platforms, including methodology and programming codes, will allow increased access to rapidly refreshed real-world data. A common adaptive platform of complete and continuously updated data will also enable a wider community of researchers to create multiple investigatory groups simultaneously accessing fully de-identified data for concurrent observational studies. In turn, researchers need to have programming, study design, and methodology ready in an open-source platform. An efficient platform would also require the adoption of artificial intelligence, natural language processing, imaging processing, and quantum computing for validation and improved data quality.

COVID-19 has demonstrated the need for open science data synchronization with universal access for faster action and improved outcomes able to gain public confidence. OpenSafely (UK), a software platform for analysis of EHR data that is shared automatically and openly for scientific review and efficient reuse, created a cohort of about 23.4 million records for observational review of monoclonal COVID-19 treatments. To keep pace with the UK, Israel, and other nationalized systems, the US would benefit from duplicating this example of coordination between federal agencies and their data repositories. For example, combining data between the DoD, which captures active military health care data through TRICARE, and VA, which follows postmilitary discharge, would create datasets encompassing complete life spans. Additionally, expanding the National COVID Cohort Collaborative (N3C) program—one of the largest collections of clinical data related to COVID-19 symptoms and patient outcomes in the US—to include EHR data from DoD, VA, Medicare, and Test to Treat initiative partners would further expand research capabilities. This could be accomplished through a framework of anonymized, readily available, harmonized data. EHRs with synchronized datasets from every health care practitioner—independent pharmacies, primary care physicians, and hospitals—could all work to create a de-identified, comprehensive, continuously updated, near real-time dataset accessible to all federal researchers.

Conclusions

The VA has been lauded for its rapid, effective response to the current pandemic. The successful management and prescription of vaccines and treatment to the largely high-risk veteran population was possible because of the existing data framework within the VA. VA Research continues to build and refine infrastructure to improve speed, quality, and value of data analytics. We can do more. Expanding partnerships to use existing VA data strategies in designing a cooperative national data alliance would deliver necessary progress to research and public health.

Acknowledgments

The authors thank Jeff Roberts, MD, for his insight on the US Food and Drug Administration, its responsibilities, and the potential benefit of real world data to its missions.

The COVID-19 pandemic emphasized the need for rapid response research in health care. The robust enterprise approach used by the US Department of Veterans Affairs (VA), termed VA Research, is meeting these needs by using existing outstanding data resources and interdisciplinary collaborations.1 In the first 7 months of 2021 alone, while many US health care systems struggled with limited data, VA Research published more than 300 unique and instrumental research papers addressing urgent questions about transmission, vaccination, therapeutics, and health impacts of COVID-19 on its high-risk population.1 The ability to leverage the VA electronic health record (EHR) and Corporate Data Warehouse (CDW)—a fully established data system bringing together test results, prescriptions, and complete patient health records, readily accessible and updated daily—was substantial.

With more than 9 million veterans enrolled in care at 171 medical centers and 1113 outpatient facilities across the US and its territories, the CDW provides an unprecedented opportunity to examine outcomes in real time. This allowed research groups such as the VA St Louis Health Care System Research and Education Service to build a cohort of 181,280 veterans with diabetes and positive COVID-19 test results within a 6-month period in 2021 to study the incidence of new diagnoses of diabetes after COVID-19 infection.2 Similarly, the Clinical Epidemiology Program (CEP) at VA White River Junction Health Care System built a cohort of 1,363,180 veterans who received at least 1 COVID-19 vaccine by March 7, 2021, to analyze coverage and effectiveness of those vaccines.3 This time-sensitive research was possible because the VA had the data and tools in place. Moreover, the the CEP quickly built an infrastructure to make its cohort and programming codes available to researchers in and outside the VA, resulting in additional significant research.4

The innovation and speed of COVID-19 vaccine development and distribution in the US were unprecedented. The rapid discovery and implementation of multiple preventives and therapeutics for COVID-19 could not have been possible without shared information within a competitive industry. VA studies added significantly to understanding the clinical performance of the messenger RNA (mRNA) COVID-19 vaccines, antivirals, and monoclonal treatments in a real-world setting. For example, a vaccine coverage study by VA Research illustrated how successful vaccination for COVID-19 at the VA has been in protecting a diverse community of patients from hospitalization and death, particularly the highly comorbid, racial and ethnic minorities, and other high-risk populations.3 The study demonstrated the power of the VA system to generate robust and compelling clinical endpoint effectiveness data across a broad range of high-risk groups.

This success is promising. However, the COVID-19 pandemic is not over, and the next could prove even more challenging. For example, through a recent partnership with the US Department of Defense (DoD), the VA was able to rapidly analyze the effectiveness of previous smallpox vaccination efforts in the military for preventing mpox infections.5 We should take this opportunity to think creatively about ways to improve our existing infrastructure based on what we have learned.

 

 

A Role for VA Research in Efficacy

The US Food and Drug Administration (FDA) Reauthorization Act of 2017 requires that manufacturers submit evidence establishing a product’s benefits (effectiveness) outweigh its risks (safety) before it can be promoted and distributed.6 As such, the FDA has been obligated by external stakeholders and Congress to be more explicit and transparent about benefit-risk profile supporting its decisions on licensure. This process led to requiring more phase 4 postmarketing observational studies for safety and effectiveness.7 Although the FDA postlicensure system remains vigilant toward safety, effectiveness information is limited due to insufficient reporting (with exceptions of manufacturer studies for new indications or to exhibit superior comparative effectiveness). The agency typically relies on a static set of efficacy data generated prelicensure with a dynamic and evolving set of safety data accrued postlicensure to support its assessment that benefits outweigh risks.

For example, operating in near real time, postauthorization safety monitoring systems, led by the Centers for Disease Control and Prevention and other federal systems, identified a safety signal for thrombosis following the Janssen COVID-19 vaccination. Distribution was quickly paused, the safety signal was investigated, the magnitude of the risk was characterized, new language describing the risk and providing guidance regarding clinical management was included in labeling, and distribution was resumed, all within a few weeks. This remarkable success demonstrated how timely the safety system can operate to evaluate risk.

In contrast, the duration and extent of protection against COVID-19 variants are largely limited to the assessment of immune biomarker surrogates. Such clinical effectiveness data are urgently needed for the FDA’s Center for Biologics Evaluation and Research and Center for Drug Evaluation and Research to make accurate benefit-risk assessments and continue to conclude the balance is favorable. As we prepare for the next pandemic, we must consider plans for monitoring postauthorization/postlicensure effectiveness as well as safety in real time. VA Research is ideally situated for this task.

Published studies on effectiveness at the VA serve as a prototype and could lead the way to initiating those preparations.4,8-11 One of the striking features of the VA system that became apparent in the preparation of the mRNA vaccine study was the speed at which an enormous volume of COVID-19 testing data were produced. This enabled implementation of methodologically sound test-negative and case-control analysis. Analyses sufficiently powered to conclude mRNA vaccines were highly effective when used in real-world conditions among a diverse population from nearly every state and territory during a period in which multiple COVID-19 variants were already circulating.3 This is unique to the VA and would not be possible for any other US health care system. With planning, the VA system could produce product-specific, real-world evidence of effectiveness comparable to the timeliness and quality of the safety data currently produced to support regulatory benefit-risk assessments. For example, the VA conducted an effectiveness study of tixagevimab/cilgavimab for preventing COVID-19 during the initial Omicron surge, which is continually updated while Omicron circulates and repeatable for different subvariants.12

The FDA continues to collaborate with the VA on demonstration projects to evaluate the impact of available vaccines and treatment against COVID-19 variants. The VA has also initiated several large-scale sequencing programs for COVID-19 specimens that will support these efforts, including VA Science and Health Initiative to Combat Infectious and Emerging Life-Threatening Diseases (VA SHIELD), VA Sequencing for Research Clinical and Epidemiology (SeqFORCE), and VA Sequencing Collaborations United for Research and Epidemiology (SeqCURE).13,14 Successful proof-of-concept studies using these data could provide a template for VA and other medical systems/databases to report effectiveness in near real time.

 

 

Interagency Collaboration

The potential advantages of federal agencies working with the VA to build an infrastructure capable of generating real-world evidence effectiveness analyses in near real time is not limited to needs that will arise in the next pandemic. For example, generating randomized, placebo-controlled, clinical trial endpoint data on the effectiveness of new variant vaccines will be difficult from a feasibility and ethical standpoint. Combining the VA’s robust virus sequencing program with preexisting mechanisms, such as expanded access studies (allowed under FDA Investigational New Drug regulations), researchers could enable a large-scale effective evaluation program of vaccination with variant or universal COVID-19 vaccines, using rapidly accruing effectiveness data.

The pandemic created opportunities to advance innovative approaches to medical product development. Some have advocated these innovative approaches should proceed together toward a seamless convergence between the domains of medical research and clinical care. A shift toward expecting, as a matter of routine, effectiveness data to be generated in near real time and made available for benefit-risk assessment would be a useful step in that direction.

Expanding and sharing analytical platforms, including methodology and programming codes, will allow increased access to rapidly refreshed real-world data. A common adaptive platform of complete and continuously updated data will also enable a wider community of researchers to create multiple investigatory groups simultaneously accessing fully de-identified data for concurrent observational studies. In turn, researchers need to have programming, study design, and methodology ready in an open-source platform. An efficient platform would also require the adoption of artificial intelligence, natural language processing, imaging processing, and quantum computing for validation and improved data quality.

COVID-19 has demonstrated the need for open science data synchronization with universal access for faster action and improved outcomes able to gain public confidence. OpenSafely (UK), a software platform for analysis of EHR data that is shared automatically and openly for scientific review and efficient reuse, created a cohort of about 23.4 million records for observational review of monoclonal COVID-19 treatments. To keep pace with the UK, Israel, and other nationalized systems, the US would benefit from duplicating this example of coordination between federal agencies and their data repositories. For example, combining data between the DoD, which captures active military health care data through TRICARE, and VA, which follows postmilitary discharge, would create datasets encompassing complete life spans. Additionally, expanding the National COVID Cohort Collaborative (N3C) program—one of the largest collections of clinical data related to COVID-19 symptoms and patient outcomes in the US—to include EHR data from DoD, VA, Medicare, and Test to Treat initiative partners would further expand research capabilities. This could be accomplished through a framework of anonymized, readily available, harmonized data. EHRs with synchronized datasets from every health care practitioner—independent pharmacies, primary care physicians, and hospitals—could all work to create a de-identified, comprehensive, continuously updated, near real-time dataset accessible to all federal researchers.

Conclusions

The VA has been lauded for its rapid, effective response to the current pandemic. The successful management and prescription of vaccines and treatment to the largely high-risk veteran population was possible because of the existing data framework within the VA. VA Research continues to build and refine infrastructure to improve speed, quality, and value of data analytics. We can do more. Expanding partnerships to use existing VA data strategies in designing a cooperative national data alliance would deliver necessary progress to research and public health.

Acknowledgments

The authors thank Jeff Roberts, MD, for his insight on the US Food and Drug Administration, its responsibilities, and the potential benefit of real world data to its missions.

References

1. US Department of Veterans Affairs, Veterans Health Administration. Third report details VA’s continued efforts addressing COVID-19 pandemic. Accessed August 15, 2023. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=5748

2. Xie Y, Ziyad A. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10(5):311-321. doi:10.1016/S2213-8587(22)00044-4

3. Young-Xu Y, Korves C, Roberts J, et al. Coverage and estimated effectiveness of mRNA COVID-19 vaccines among US veterans. JAMA Netw Open. 2021;4(10):e2128391. doi:10.1001/jamanetworkopen.2021.28391

4. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of BNT162b2 and mRNA-1273 vaccines in U.S. veterans. N Engl J Med. 2022;386(2):105-115. doi:10.1056/NEJMoa2115463

5. Titanji BK, Eick-Cost A, Partan ES, et al. Effectiveness of smallpox vaccination to prevent mpox in military personnel. N Engl J Med. 2023;389(12):1147-1148. doi:10.1056/NEJMc2300805

6. Sarata AK, Dabrowska A, Johnson JA, Thaul S. FDA Reauthorization Act of 2017. Accessed August 15, 2023. https://sgp.fas.org/crs/misc/R44961.pdf

7. US Food and Drug Administration. FDA’s sentinel initiative–background. February 2, 2022. Updated February 4, 2022. Accessed August 15, 2023. https://www.fda.gov/safety/fdas-sentinel-initiative/fdas-sentinel-initiative-background

8. Bajema KL, Dahl RM, Prill MM, et al; SUPERNOVA COVID-19; Surveillance Group. Effectiveness of COVID-19 mRNA vaccines against COVID-19–associated hospitalization—five Veterans Affairs medical centers, United States, February 1–August 6, 2021. MMWR Morb Mortal Wkly. 2021;70(37):1294-1299. doi:10.15585/mmwr.mm7037e3

9. Sharma A, Oda G, Holodniy M. COVID-19 vaccine breakthrough infections in Veterans Health Administration. medRxiv. Posted September 26, 2021. doi:10.1101/2021.09.23.21263864

10. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol. 2023;8(1):55-63. doi:10.1038/s41564-022-01272-z


11. Tang F, Hammel IS, Andrew MK, Ruiz JG. Frailty reduces vaccine effectiveness against SARS-CoV-2 infection: a test-negative case control study using national VA data. J Nutr Health Aging. 2023;27(2):81-88. doi:10.1007/s12603-023-1885-1

12. Young-Xu Y, Epstein L, Marconi VC, et al. Tixagevimab/cilgavimab for preventing COVID-19 during the Omicron surge: retrospective analysis of National Veterans Health Administration electronic data. mBio. 2023;14(4):e0102423. doi:10.1128/mbio.01024-23

13. US Department of Veterans Affairs. VA science and health initiative to combat infectious and emerging life-threatening diseases. Open Forum Infect Dis. 2022;9(12):ofac641. doi:10.1093/ofid/ofac64

14. Bilal MY. Similarity index–probabilistic confidence estimation of SARS-CoV-2 strain relatedness in localized outbreaks. Epidemiologia. 2022;3(2):238-249. doi:10.3390/epidemiologia3020019

References

1. US Department of Veterans Affairs, Veterans Health Administration. Third report details VA’s continued efforts addressing COVID-19 pandemic. Accessed August 15, 2023. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=5748

2. Xie Y, Ziyad A. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10(5):311-321. doi:10.1016/S2213-8587(22)00044-4

3. Young-Xu Y, Korves C, Roberts J, et al. Coverage and estimated effectiveness of mRNA COVID-19 vaccines among US veterans. JAMA Netw Open. 2021;4(10):e2128391. doi:10.1001/jamanetworkopen.2021.28391

4. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of BNT162b2 and mRNA-1273 vaccines in U.S. veterans. N Engl J Med. 2022;386(2):105-115. doi:10.1056/NEJMoa2115463

5. Titanji BK, Eick-Cost A, Partan ES, et al. Effectiveness of smallpox vaccination to prevent mpox in military personnel. N Engl J Med. 2023;389(12):1147-1148. doi:10.1056/NEJMc2300805

6. Sarata AK, Dabrowska A, Johnson JA, Thaul S. FDA Reauthorization Act of 2017. Accessed August 15, 2023. https://sgp.fas.org/crs/misc/R44961.pdf

7. US Food and Drug Administration. FDA’s sentinel initiative–background. February 2, 2022. Updated February 4, 2022. Accessed August 15, 2023. https://www.fda.gov/safety/fdas-sentinel-initiative/fdas-sentinel-initiative-background

8. Bajema KL, Dahl RM, Prill MM, et al; SUPERNOVA COVID-19; Surveillance Group. Effectiveness of COVID-19 mRNA vaccines against COVID-19–associated hospitalization—five Veterans Affairs medical centers, United States, February 1–August 6, 2021. MMWR Morb Mortal Wkly. 2021;70(37):1294-1299. doi:10.15585/mmwr.mm7037e3

9. Sharma A, Oda G, Holodniy M. COVID-19 vaccine breakthrough infections in Veterans Health Administration. medRxiv. Posted September 26, 2021. doi:10.1101/2021.09.23.21263864

10. Dickerman BA, Gerlovin H, Madenci AL, et al. Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans. Nat Microbiol. 2023;8(1):55-63. doi:10.1038/s41564-022-01272-z


11. Tang F, Hammel IS, Andrew MK, Ruiz JG. Frailty reduces vaccine effectiveness against SARS-CoV-2 infection: a test-negative case control study using national VA data. J Nutr Health Aging. 2023;27(2):81-88. doi:10.1007/s12603-023-1885-1

12. Young-Xu Y, Epstein L, Marconi VC, et al. Tixagevimab/cilgavimab for preventing COVID-19 during the Omicron surge: retrospective analysis of National Veterans Health Administration electronic data. mBio. 2023;14(4):e0102423. doi:10.1128/mbio.01024-23

13. US Department of Veterans Affairs. VA science and health initiative to combat infectious and emerging life-threatening diseases. Open Forum Infect Dis. 2022;9(12):ofac641. doi:10.1093/ofid/ofac64

14. Bilal MY. Similarity index–probabilistic confidence estimation of SARS-CoV-2 strain relatedness in localized outbreaks. Epidemiologia. 2022;3(2):238-249. doi:10.3390/epidemiologia3020019

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Leveraging the Million Veteran Program Infrastructure and Data for a Rapid Research Response to COVID-19

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The Million Veteran Program (MVP) was launched in 2011 by the US Department of Veterans Affairs (VA) to enroll at least 1 million veterans in a longitudinal cohort to better understand how genes, lifestyle, military experience, and environmental exposures interact to influence health and illness and ultimately enable precision health care. The MVP has established a national, centralized infrastructure for recruitment and enrollment, biospecimen and data collection and storage, data generation and curation, and secure data access. When the COVID-19 pandemic hit in 2020, the MVP was leveraged to support research utilizing the following key infrastructure components: (1) MVP recruitment and enrollment platform to provide support for COVID-19 vaccine and treatment trials and to collect COVID-19 data from MVP participants; (2) using MVP Phenomics for COVID-19 research data cleaning and curation, assisting with the development of a VA Severity Index for COVID-19, and forming 6 scientific working groups to coordinate COVID-19 research questions; and (3) the VA/MVP and US Department of Energy (DOE) partnership to assist in responding to COVID-19 research questions identified by the US Food and Drug Administration (FDA). This article describes these infrastructure components in more detail and highlights key findings from the MVP COVID-19 research efforts.

MVP Infrastructure

The Veterans Health Administration (VHA) Office of Research and Development (ORD) oversaw efforts to develop the VA Coronavirus Research Volunteer List (the COVID-19 registry). To support the registry, the MVP leveraged its infrastructure to facilitate a rapid response. The MVP is designed as a full-service and centralized recruitment and enrollment platform. This includes MVP office oversight; MVP coordinating centers that manage the centralized platform; an information center that handles inbound and outbound calls; an informatics system built for recruitment and enrollment monitoring and tracking; and a network of more than 70 participating MVP sites with dedicated staff to conduct recruitment and enrollment activities. The MVP used its informatics infrastructure to support secure data storage for the registry volunteer information. MVP coordinating center staff worked with the COVID-19 registry to invite > 125,000 MVP participants from approximately 20 MVP sites. Additionally, MVP information center staff made > 4000 calls to prospective registry volunteers. This work resulted in 1300 volunteers agreeing to be contacted by COVID-19 vaccine clinical trial study teams (including Moderna, Janssen, AstraZeneca, and Novavax). About 20 MVP site staff (spanning 14 MVP sites) also were deployed to support COVID-19 work for clinical care capabilities or vaccine trials.

New Data Collection

The MVP protocol was approved by the VA Central Institutional Review Board (IRB) in 2011. As part of initial enrollment in MVP, participants consented to recontact for additional self-report information along with access to their electronic health record (EHR). This allows for the linkage of EHR and survey response data, thus providing a comprehensive understanding of health history before and after a self-reported COVID-19 diagnosis. Between May 2020 and September 2021, the MVP COVID-19 survey was distributed to existing MVP participants via mail, telephone, and email with the ability to complete the survey by paper and pencil or through the MVP online system. Dissemination of the survey was approved by the VA Central IRB in 2020, with nearly 730,000 eligible MVP participants contacted. As of June 2022, 255,737 MVP participants (35% of the eligible cohort) had completed the survey; 86% completed a paper survey while 14% completed it online. Respondents were primarily older (≥ 65 years); 90% were male; close to 7% reported Hispanic ethnicity, and 11% reported Black race.

Findings from this survey provide insight into pandemic behaviors not consistently captured in EHRs, such as psychosocial aspects, including social and emotional support, loss of tangible and intangible resources, as well as COVID-19–related behaviors, such as social distancing and self-protective practices.1 MVP COVID-19 survey data combined with veteran EHRs, responses to other MVP surveys, and genetic data enable MVP researchers to better understand epidemiological, clinical, and psychosocial aspects of the disease. Future COVID-19 studies may use self-reported survey responses to enrich understanding about the effects of the disease on a veteran’s daily life, and possibly validate existing EHR COVID-19 diagnoses and hospitalization findings. This comprehensive data resource provides a unique opportunity to identify new targets for disease prevention, treatment, and management with an emphasis on individual variability in genes, environment, and lifestyle.

COVID-19 Research

In early 2020, the burden of COVID-19 on the US was unprecedented, and little was known about risk factors for severe COVID-19 and deaths. The MVP Phenomics team quickly responded with a large-scale phenome-wide association study (PheWAS) of > 1800 phenotypes (physical and biochemical traits) and COVID-19 progression. Its goal was to characterize risk factors and outcomes associated with COVID-19 disease progression.2 Data curation and assembly occurred rapidly through integrated efforts led by MVP and VA COVID-19 initiatives. The MVP utilized its phenomics core resource to understand the progression of COVID-19 defined by SARS-CoV-2 infection, hospitalization, intensive care unit admission, and 30-day mortality using VA EHR data.

To broaden disease progression data curation and fit the specific needs of the VA, we operationalized and validated the World Health Organization clinical severity scale and used VA EHR data to create the VA Severity Index for COVID-19 (VASIC).3 The VASIC category is now part of the MVP core data repository, where volumes of data from multiple activities are integrated through an automated process to create monthly research-ready data cubes. These activities include extensive data curation, mapping, phenotyping, and adjudication that are performed to curate oxygen supplementation status and other procedures related to treatment that are processed and understood in real time. The data cubes were provisioned to MVP COVID-19 researchers. In addition, the VASIC scale variable is now integrated within the larger VA system for all researchers to use as part of its wider COVID-19 initiative. The VA Centralized Interactive Phenomics Resource (CIPHER) phenomics library now hosts the details of VASIC, codes, metadata, and related COVID-19 data products for all VA communities. In partnership with CIPHER and other internal and external COVID-19 initiatives, the MVP continues to play an integral part for the VA and beyond in the development of a phenomics algorithm for long COVID, or post-acute COVID-19 syndrome (PACS).

 

 

Host Genetics in COVID-19

As the SARS-CoV-2 virus continued to spread globally, it became clear that the symptoms and severity of infection experienced by patients varied across a broad spectrum, from being asymptomatic carriers to experiencing severe symptoms in 1 or more organ systems in the body, resulting in death. This variability suggested that host genetics and other host factors may play a role in determining the severity of COVID-19 infection. The MVP dataset, with genetic and health information on > 600,000 MVP participants, provided an ideal dataset to explore host contributions to COVID-19.

In late spring 2020, the MVP executive committee issued a call to the MVP research community to propose study aims around the COVID-19 pandemic that could leverage the phenotypic and genetic data and resources. The MVP quickly formed 6 rapid-response scientific working groups. Their mission was to cultivate collaboration and inclusivity and to coordinate COVID-19 research questions. A steering committee composed of the MVP executive committee, staff from computational environments, working group cochairs, and an administrator, who was responsible for daily oversight of the working groups. In addition, the ORD COVID-19 steering committee reviewed and approved research activities to ensure scientific rigor, as well as alignment with overall ongoing research activities.

table

The MVP COVID-19 working groups included dozens of researchers who used MVP data to identify disease mechanisms; understand the impact of host genetics on susceptibility, morbidity, and mortality; and identify potential targets for treatments and therapies. The working groups were further supported by MVP analysts to work cross-functionally on genomics, phenomics, statistical genetics, and PheWAS. Each working group chair was responsible for prioritizing concepts and moving them forward in coordination with the MVP and ORD COVID-19 steering committees. An overview of the MVP COVID-19 working groups follows (Table).4-9

Druggable genome. This working group researched drug-repurposing opportunities to prevent severe COVID-19, defined as hospitalization with oxygen therapy (high flow), intubation, mechanical ventilation, vasopressors, dialysis, or death from COVID-19; and prevent complications in patients hospitalized by COVID-19.

Pharmacogenomics. This working group focused on 2 main aims: the impact of apolipoprotein L1 risk variants on acute kidney injury (AKI) and death in Black veterans with COVID-19; and pharmacogenetic analysis of remdesivir-induced liver chemistry abnormalities.

Disease mechanisms. Understanding the underlying pathways and mechanisms behind COVID-19 has been a difficult but important challenge overall in the scientific community. This working group investigated specific genetic markers and effects on COVID-19, including polygenic predisposition to venous thromboembolism associated with increased COVID-19 susceptibility; renal comorbidities and new AKI and unfavorable outcomes among COVID-19–positive sickle cell trait carriers; and mucin 5B, oligomeric mucus/gel-forming gene polymorphism, and protective effects in COVID-19 infection.

Genomics for risk prediction, polygenic risk scores, and mendelian randomization. Risk prediction for COVID-19 has been widely studied mostly aiming at comorbidities and preexisting conditions. The MVP cohort provided a unique opportunity to understand how genetic information can enhance our understanding of COVID-19 risk. This working group focused on: (1) ABO blood group typing and the protective effects of the O blood group on COVID-19 infection; (2) polygenic risk scores and COVID-19 outcomes; (3) human leukocyte antigen typing and COVID-19 outcomes; and (4) a transcriptome-wide association study of COVID-19–positive MVP participants.

Genome-Wide Association Study (GWAS) and Downstream Analysis. This working group performed GWAS of the main COVID-19 outcomes. Results from GWAS unveiled new genetic loci to suggest further investigation on these candidate genes. The results were used by other MVP COVID-19 working groups for their activities. The results also contributed to external collaborations, such as the COVID-19 Host Genetics Initiative.

COVID-19–Related PheWAS. This working group focused on understanding the potential clinical significance of genetic variants associated with susceptibility to, or outcomes of, COVID-19 infection. They worked to identify traits that share genetic variants associated with severe COVID-19 from the Host Genetics Initiative. The group also studied the phenotypic consequences of acquired mosaic chromosomal alterations with early data linking to COVID-19 susceptibility.

 

 

COVID-19 Research Partnerships

In 2016, the VA and DOE formed an interagency partnership known as Computational Health Analytics for Medical Precision to Improve Outcomes Now (CHAMPION) to demonstrate the power of combining the VA EHR system, MVP genetic data, and clinical research expertise with DOE high-performance computing infrastructure and artificial intelligence expertise. The VA EHR captures longitudinal care information on veterans with records that go back decades. Furthermore, the VA covers the costs of medications and provides a variety of services through the Veterans Benefits Administration. As a result, VA data include medications used by patients before, during, and after COVID-19. Similarly, the VA has comprehensive vital records, whereas other large health systems do not capture events such as death after patients leave the hospital.

The DOE Oak Ridge National Laboratory (ORNL) in Tennessee securely maintains this rich database for the VA. The ORNL Summit supercomputer can complete trillions of calculations per second to provide critical and timely analyses, applying the most advanced and powerful artificial intelligence methods, which would not be possible in more conventional research settings. CHAMPION taught the VA and DOE how to bring their disparate research cultures together for innovative collaborative investigation. Moreover, this collaboration produced a cadre of VA and DOE scientists familiar with VA patient data and experienced in conducting joint research successfully and integrating omics data with clinical data for a better mechanistic understanding. Because of this preexisting collaboration between the VA and DOE, interagency teams were prepared at the start of the COVID-19 pandemic.10-15

During the pandemic, the FDA and VA conducted research together. One joint study found that the bradykinin storm is likely to play a role in many COVID-19 symptoms. Using VA data, researchers compared COVID-19 testing patterns, positive test results, and 30-day mortality rates by race and ethnicity among VA patients.10,11These findings demonstrated the higher burden COVID-19 placed on Black and Hispanic communities, not fully explained by underlying health conditions, access to medical care, or geographic locale.11

Other recently completed studies have developed and validated short-term mortality indices in individuals with COVID-19 based on their preexisting conditions, assessed the generalizability of VA COVID-19 experiences to the US population, and evaluated the effectiveness of hydroxychloroquine with and without azithromycin in VA patients with COVID-19.12,15 A recent study demonstrated the benefit of prophylactic anticoagulation at initial hospitalization.14

The VA also provided the FDA with daily reports on aggregate VA COVID-19 cases and their distribution across the VA system, demographics of VA patients with COVID-19, and analyses of predictive models for positive test results and death. The VA regularly sent the FDA aggregated data showing patterns of medication use and retrospective analyses of the effectiveness of certain medications (including remdesivir and some antithrombotic agents). The FDA used these data along with other data to understand the scope of the pandemic and to predict drug shortages or needs for additional medical equipment, including ventilators. This information was critical at the start of the pandemic.

Limitations

For the most part, MVP infrastructure and partnerships were efficiently leveraged to significantly advance our understanding of the biological basis of COVID-19 and to develop treatments and vaccines. However, there were a few limitations that may have slowed timely and optimal outcomes. An issue not limited to the MVP or VA was the continual evolution of the pandemic and its response. This included evolving definitions of disease, symptomatology, testing, vaccines, and public health recommendations. Keeping pace with the emerging knowledge from these domains was a struggle for the entire scientific community. A more discrete limitation was the number of participants in the MVP with positive COVID-19 test results and positive symptoms; however, this was mitigated by partnering with other groups like the COVID-19 Host Genetics Initiative to increase study participant numbers. Finally, there were logistical and regulatory challenges associated with coordination of national clinical trial recruitment across a VA system with > 100 discrete hospitals.

Conclusions

Having a centralized infrastructure for recruitment and enrollment, including a national research volunteer registry, information center, research staff, and coordinating centers, can allow for expedited enrollment in vaccine and treatment trials in the face of future public health emergencies. VA assets, including its rich EHR and MVP, the world’s largest genomic cohort, have contributed to improving our understanding and management of COVID-19. MVP’s ready-to-respond research infrastructure embedded within the country’s largest national health care system allows for both the facilitation of the research work and applications of the research findings into practice. Findings from the MVP COVID-19 working groups have yielded compelling results, particularly around genetic variants among various racial and ethnic groups. Looking ahead, the VA and DOE are launching a new joint project on long COVID that will include developing a gold-standard definition for long COVID. The ORD has established a Partnered Research Program to facilitate collaborations with industry to speed up clinical trials, and the MVP will continue to contribute toward expanding scientific knowledge to improve the management of COVID-19.

References

1. Whitbourne SB, Nguyen XT, Song RJ, et al. Million Veteran Program’s response to COVID-19: survey development and preliminary findings. PLoS One. 2022;17(4):e0266381. doi:10.1371/journal.pone.0266381

2. Song RJ, Ho YL, Schubert P, et al. Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program. PLoS One. 2021;16(5):e0251651. doi:10.1371/journal.pone.0251651

3. Galloway A, Park Y, Tanukonda V, et al. Impact of COVID-19 severity on long-term events in US veterans using the Veterans Affairs Severity Index for COVID-19 (VASIC). J Infect Dis. 2022;226(12):2113-2117. doi:10.1093/infdis/jiac182

4. Gaziano L, Giambartolomei C, Pereira AC, et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med. 2021;27(4):668-676. doi:10.0138/s41591-021-01310-z

5. Hung AM, Sha SC, Bick AG, et al. APOL1 risk variants, acute kidney injury, and death in participants with African ancestry hospitalized with COVID-19 from the Million Veteran Program. JAMA Intern Med. 2022;182(4):386-395. doi:10.1001/jamainternmed.2021.8538

6. Verma A, Huffman JE, Gao L, et al. Association of kidney comorbidities and acute kidney failure with unfavorable outcomes after COVID-19 in individuals with the sickle cell trait. JAMA Intern Med. 2022;182(8):796-804. doi:10.1001/jamainternmed.2022.2141

7. Verma A, Tsao NL, Thomann LO, et al. A phenome-wide association study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program. PLoS Genet. 2022;18(4):e1010113. doi:10.1371/journal.pgen.1010113

8. Peloso GM, Tcheandjieu C, McGeary JE, et al. Genetic loci associated with COVID-19 positivity and hospitalization in White, Black, and Hispanic Veterans of the VA Million Veteran Program. Front Genetic. 2022;12:777076. doi:10.3389/fgene.2021.777076

9. Verma A, Minnier J, Wan ES, et al. A MUC5B gene polymorphism, rs35705950-T confers protective effects against COVID-19 hospitalization but not severe disease or mortality. Am J Respir Crit Care Med. 2022;182(8):796-804. doi:10.1164/rccm.202109-2166OC

10. Garvin MR, Alvarez C, Miller JI, et al. A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm. Elife. 2020;e59177. doi:10.7554/eLife.59177

11. Rentsch CT, Kidwai-Khan F, Tate JP, et al. Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study. PLoS Med. 2020;17(9):e1003379. doi:10.1371/journal.pmed.1003379

12. King JT, Yoon JS, Rentsch CT, et al. Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: the Veterans Health Administration COVID-19 (VACO) Index. PLoS One. 2020;15(11):e0241825. doi:10.1371/journal.pone.0241825

13. Joubert W, Weighill D, Kainer D, et al. Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Dallas, TX, USA, 2018:717-730. doi:10.1109/SC.2018.00060

14. Rentsch CT, Beckman JA, Tomlinson L, et al. Early initiation of prophylactic anticoagulation for prevention of COVID-19 mortality: a nationwide cohort study of hospitalized patients in the United States. BMJ. 2021;372:n311. doi:10.1136/bmj.n311

15. Gerlovin H, Posner DC, Ho YL, et al. Pharmacoepidemiology, machine learning, and COVID-19: an intent-to-treat analysis of hydroxychloroquine, with or without Azithromycin, and COVID-19 outcomes among hospitalized US Veterans. Am J Epidemiol. 2021;190(11): 2405-2419. doi:10.1093/aje/kwab183

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Stacey B. Whitbourne, PhDa,b,c; Jennifer Moser, PhDd; Kelly Cho, PhD, MPHa,b,c; Jennifer Deend; Lori L. Churbye; Amy C. Justice, MD, PhDf,g; Juan P. Casas, MD, PhDh; Saiju Pyarajan, PhDa; Phil S. Tsao, PhDe,i; J. Michael Gaziano, MD, MPHa,b,c; Sumitra Muralidhar, PhDd

Correspondence:  Sumitra Muralidhar  (sumitra.muralidhar@va.gov)

aVeterans Affairs Boston Healthcare System, Massachusetts

bBrigham and Women’s Hospital, Boston, Massachusetts

cHarvard Medical School, Boston, Massachusetts

dOffice of Research and Development, Department of Veterans Affairs, Washington, DC

eVeterans Affairs Palo Alto Healthcare System, California

fVeterans Affairs Connecticut Healthcare System, West Haven

gYale University School of Medicine and School of Public Health, New Haven, Connecticut

hNovartis Institute for Biomedical Research, Cambridge, Massachusetts

iStanford University School of Medicine, Palo Alto, California

Author disclosures

The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

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Stacey B. Whitbourne, PhDa,b,c; Jennifer Moser, PhDd; Kelly Cho, PhD, MPHa,b,c; Jennifer Deend; Lori L. Churbye; Amy C. Justice, MD, PhDf,g; Juan P. Casas, MD, PhDh; Saiju Pyarajan, PhDa; Phil S. Tsao, PhDe,i; J. Michael Gaziano, MD, MPHa,b,c; Sumitra Muralidhar, PhDd

Correspondence:  Sumitra Muralidhar  (sumitra.muralidhar@va.gov)

aVeterans Affairs Boston Healthcare System, Massachusetts

bBrigham and Women’s Hospital, Boston, Massachusetts

cHarvard Medical School, Boston, Massachusetts

dOffice of Research and Development, Department of Veterans Affairs, Washington, DC

eVeterans Affairs Palo Alto Healthcare System, California

fVeterans Affairs Connecticut Healthcare System, West Haven

gYale University School of Medicine and School of Public Health, New Haven, Connecticut

hNovartis Institute for Biomedical Research, Cambridge, Massachusetts

iStanford University School of Medicine, Palo Alto, California

Author disclosures

The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

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Not applicable.

Author and Disclosure Information

Stacey B. Whitbourne, PhDa,b,c; Jennifer Moser, PhDd; Kelly Cho, PhD, MPHa,b,c; Jennifer Deend; Lori L. Churbye; Amy C. Justice, MD, PhDf,g; Juan P. Casas, MD, PhDh; Saiju Pyarajan, PhDa; Phil S. Tsao, PhDe,i; J. Michael Gaziano, MD, MPHa,b,c; Sumitra Muralidhar, PhDd

Correspondence:  Sumitra Muralidhar  (sumitra.muralidhar@va.gov)

aVeterans Affairs Boston Healthcare System, Massachusetts

bBrigham and Women’s Hospital, Boston, Massachusetts

cHarvard Medical School, Boston, Massachusetts

dOffice of Research and Development, Department of Veterans Affairs, Washington, DC

eVeterans Affairs Palo Alto Healthcare System, California

fVeterans Affairs Connecticut Healthcare System, West Haven

gYale University School of Medicine and School of Public Health, New Haven, Connecticut

hNovartis Institute for Biomedical Research, Cambridge, Massachusetts

iStanford University School of Medicine, Palo Alto, California

Author disclosures

The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.

Ethics and consent

Not applicable.

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The Million Veteran Program (MVP) was launched in 2011 by the US Department of Veterans Affairs (VA) to enroll at least 1 million veterans in a longitudinal cohort to better understand how genes, lifestyle, military experience, and environmental exposures interact to influence health and illness and ultimately enable precision health care. The MVP has established a national, centralized infrastructure for recruitment and enrollment, biospecimen and data collection and storage, data generation and curation, and secure data access. When the COVID-19 pandemic hit in 2020, the MVP was leveraged to support research utilizing the following key infrastructure components: (1) MVP recruitment and enrollment platform to provide support for COVID-19 vaccine and treatment trials and to collect COVID-19 data from MVP participants; (2) using MVP Phenomics for COVID-19 research data cleaning and curation, assisting with the development of a VA Severity Index for COVID-19, and forming 6 scientific working groups to coordinate COVID-19 research questions; and (3) the VA/MVP and US Department of Energy (DOE) partnership to assist in responding to COVID-19 research questions identified by the US Food and Drug Administration (FDA). This article describes these infrastructure components in more detail and highlights key findings from the MVP COVID-19 research efforts.

MVP Infrastructure

The Veterans Health Administration (VHA) Office of Research and Development (ORD) oversaw efforts to develop the VA Coronavirus Research Volunteer List (the COVID-19 registry). To support the registry, the MVP leveraged its infrastructure to facilitate a rapid response. The MVP is designed as a full-service and centralized recruitment and enrollment platform. This includes MVP office oversight; MVP coordinating centers that manage the centralized platform; an information center that handles inbound and outbound calls; an informatics system built for recruitment and enrollment monitoring and tracking; and a network of more than 70 participating MVP sites with dedicated staff to conduct recruitment and enrollment activities. The MVP used its informatics infrastructure to support secure data storage for the registry volunteer information. MVP coordinating center staff worked with the COVID-19 registry to invite > 125,000 MVP participants from approximately 20 MVP sites. Additionally, MVP information center staff made > 4000 calls to prospective registry volunteers. This work resulted in 1300 volunteers agreeing to be contacted by COVID-19 vaccine clinical trial study teams (including Moderna, Janssen, AstraZeneca, and Novavax). About 20 MVP site staff (spanning 14 MVP sites) also were deployed to support COVID-19 work for clinical care capabilities or vaccine trials.

New Data Collection

The MVP protocol was approved by the VA Central Institutional Review Board (IRB) in 2011. As part of initial enrollment in MVP, participants consented to recontact for additional self-report information along with access to their electronic health record (EHR). This allows for the linkage of EHR and survey response data, thus providing a comprehensive understanding of health history before and after a self-reported COVID-19 diagnosis. Between May 2020 and September 2021, the MVP COVID-19 survey was distributed to existing MVP participants via mail, telephone, and email with the ability to complete the survey by paper and pencil or through the MVP online system. Dissemination of the survey was approved by the VA Central IRB in 2020, with nearly 730,000 eligible MVP participants contacted. As of June 2022, 255,737 MVP participants (35% of the eligible cohort) had completed the survey; 86% completed a paper survey while 14% completed it online. Respondents were primarily older (≥ 65 years); 90% were male; close to 7% reported Hispanic ethnicity, and 11% reported Black race.

Findings from this survey provide insight into pandemic behaviors not consistently captured in EHRs, such as psychosocial aspects, including social and emotional support, loss of tangible and intangible resources, as well as COVID-19–related behaviors, such as social distancing and self-protective practices.1 MVP COVID-19 survey data combined with veteran EHRs, responses to other MVP surveys, and genetic data enable MVP researchers to better understand epidemiological, clinical, and psychosocial aspects of the disease. Future COVID-19 studies may use self-reported survey responses to enrich understanding about the effects of the disease on a veteran’s daily life, and possibly validate existing EHR COVID-19 diagnoses and hospitalization findings. This comprehensive data resource provides a unique opportunity to identify new targets for disease prevention, treatment, and management with an emphasis on individual variability in genes, environment, and lifestyle.

COVID-19 Research

In early 2020, the burden of COVID-19 on the US was unprecedented, and little was known about risk factors for severe COVID-19 and deaths. The MVP Phenomics team quickly responded with a large-scale phenome-wide association study (PheWAS) of > 1800 phenotypes (physical and biochemical traits) and COVID-19 progression. Its goal was to characterize risk factors and outcomes associated with COVID-19 disease progression.2 Data curation and assembly occurred rapidly through integrated efforts led by MVP and VA COVID-19 initiatives. The MVP utilized its phenomics core resource to understand the progression of COVID-19 defined by SARS-CoV-2 infection, hospitalization, intensive care unit admission, and 30-day mortality using VA EHR data.

To broaden disease progression data curation and fit the specific needs of the VA, we operationalized and validated the World Health Organization clinical severity scale and used VA EHR data to create the VA Severity Index for COVID-19 (VASIC).3 The VASIC category is now part of the MVP core data repository, where volumes of data from multiple activities are integrated through an automated process to create monthly research-ready data cubes. These activities include extensive data curation, mapping, phenotyping, and adjudication that are performed to curate oxygen supplementation status and other procedures related to treatment that are processed and understood in real time. The data cubes were provisioned to MVP COVID-19 researchers. In addition, the VASIC scale variable is now integrated within the larger VA system for all researchers to use as part of its wider COVID-19 initiative. The VA Centralized Interactive Phenomics Resource (CIPHER) phenomics library now hosts the details of VASIC, codes, metadata, and related COVID-19 data products for all VA communities. In partnership with CIPHER and other internal and external COVID-19 initiatives, the MVP continues to play an integral part for the VA and beyond in the development of a phenomics algorithm for long COVID, or post-acute COVID-19 syndrome (PACS).

 

 

Host Genetics in COVID-19

As the SARS-CoV-2 virus continued to spread globally, it became clear that the symptoms and severity of infection experienced by patients varied across a broad spectrum, from being asymptomatic carriers to experiencing severe symptoms in 1 or more organ systems in the body, resulting in death. This variability suggested that host genetics and other host factors may play a role in determining the severity of COVID-19 infection. The MVP dataset, with genetic and health information on > 600,000 MVP participants, provided an ideal dataset to explore host contributions to COVID-19.

In late spring 2020, the MVP executive committee issued a call to the MVP research community to propose study aims around the COVID-19 pandemic that could leverage the phenotypic and genetic data and resources. The MVP quickly formed 6 rapid-response scientific working groups. Their mission was to cultivate collaboration and inclusivity and to coordinate COVID-19 research questions. A steering committee composed of the MVP executive committee, staff from computational environments, working group cochairs, and an administrator, who was responsible for daily oversight of the working groups. In addition, the ORD COVID-19 steering committee reviewed and approved research activities to ensure scientific rigor, as well as alignment with overall ongoing research activities.

table

The MVP COVID-19 working groups included dozens of researchers who used MVP data to identify disease mechanisms; understand the impact of host genetics on susceptibility, morbidity, and mortality; and identify potential targets for treatments and therapies. The working groups were further supported by MVP analysts to work cross-functionally on genomics, phenomics, statistical genetics, and PheWAS. Each working group chair was responsible for prioritizing concepts and moving them forward in coordination with the MVP and ORD COVID-19 steering committees. An overview of the MVP COVID-19 working groups follows (Table).4-9

Druggable genome. This working group researched drug-repurposing opportunities to prevent severe COVID-19, defined as hospitalization with oxygen therapy (high flow), intubation, mechanical ventilation, vasopressors, dialysis, or death from COVID-19; and prevent complications in patients hospitalized by COVID-19.

Pharmacogenomics. This working group focused on 2 main aims: the impact of apolipoprotein L1 risk variants on acute kidney injury (AKI) and death in Black veterans with COVID-19; and pharmacogenetic analysis of remdesivir-induced liver chemistry abnormalities.

Disease mechanisms. Understanding the underlying pathways and mechanisms behind COVID-19 has been a difficult but important challenge overall in the scientific community. This working group investigated specific genetic markers and effects on COVID-19, including polygenic predisposition to venous thromboembolism associated with increased COVID-19 susceptibility; renal comorbidities and new AKI and unfavorable outcomes among COVID-19–positive sickle cell trait carriers; and mucin 5B, oligomeric mucus/gel-forming gene polymorphism, and protective effects in COVID-19 infection.

Genomics for risk prediction, polygenic risk scores, and mendelian randomization. Risk prediction for COVID-19 has been widely studied mostly aiming at comorbidities and preexisting conditions. The MVP cohort provided a unique opportunity to understand how genetic information can enhance our understanding of COVID-19 risk. This working group focused on: (1) ABO blood group typing and the protective effects of the O blood group on COVID-19 infection; (2) polygenic risk scores and COVID-19 outcomes; (3) human leukocyte antigen typing and COVID-19 outcomes; and (4) a transcriptome-wide association study of COVID-19–positive MVP participants.

Genome-Wide Association Study (GWAS) and Downstream Analysis. This working group performed GWAS of the main COVID-19 outcomes. Results from GWAS unveiled new genetic loci to suggest further investigation on these candidate genes. The results were used by other MVP COVID-19 working groups for their activities. The results also contributed to external collaborations, such as the COVID-19 Host Genetics Initiative.

COVID-19–Related PheWAS. This working group focused on understanding the potential clinical significance of genetic variants associated with susceptibility to, or outcomes of, COVID-19 infection. They worked to identify traits that share genetic variants associated with severe COVID-19 from the Host Genetics Initiative. The group also studied the phenotypic consequences of acquired mosaic chromosomal alterations with early data linking to COVID-19 susceptibility.

 

 

COVID-19 Research Partnerships

In 2016, the VA and DOE formed an interagency partnership known as Computational Health Analytics for Medical Precision to Improve Outcomes Now (CHAMPION) to demonstrate the power of combining the VA EHR system, MVP genetic data, and clinical research expertise with DOE high-performance computing infrastructure and artificial intelligence expertise. The VA EHR captures longitudinal care information on veterans with records that go back decades. Furthermore, the VA covers the costs of medications and provides a variety of services through the Veterans Benefits Administration. As a result, VA data include medications used by patients before, during, and after COVID-19. Similarly, the VA has comprehensive vital records, whereas other large health systems do not capture events such as death after patients leave the hospital.

The DOE Oak Ridge National Laboratory (ORNL) in Tennessee securely maintains this rich database for the VA. The ORNL Summit supercomputer can complete trillions of calculations per second to provide critical and timely analyses, applying the most advanced and powerful artificial intelligence methods, which would not be possible in more conventional research settings. CHAMPION taught the VA and DOE how to bring their disparate research cultures together for innovative collaborative investigation. Moreover, this collaboration produced a cadre of VA and DOE scientists familiar with VA patient data and experienced in conducting joint research successfully and integrating omics data with clinical data for a better mechanistic understanding. Because of this preexisting collaboration between the VA and DOE, interagency teams were prepared at the start of the COVID-19 pandemic.10-15

During the pandemic, the FDA and VA conducted research together. One joint study found that the bradykinin storm is likely to play a role in many COVID-19 symptoms. Using VA data, researchers compared COVID-19 testing patterns, positive test results, and 30-day mortality rates by race and ethnicity among VA patients.10,11These findings demonstrated the higher burden COVID-19 placed on Black and Hispanic communities, not fully explained by underlying health conditions, access to medical care, or geographic locale.11

Other recently completed studies have developed and validated short-term mortality indices in individuals with COVID-19 based on their preexisting conditions, assessed the generalizability of VA COVID-19 experiences to the US population, and evaluated the effectiveness of hydroxychloroquine with and without azithromycin in VA patients with COVID-19.12,15 A recent study demonstrated the benefit of prophylactic anticoagulation at initial hospitalization.14

The VA also provided the FDA with daily reports on aggregate VA COVID-19 cases and their distribution across the VA system, demographics of VA patients with COVID-19, and analyses of predictive models for positive test results and death. The VA regularly sent the FDA aggregated data showing patterns of medication use and retrospective analyses of the effectiveness of certain medications (including remdesivir and some antithrombotic agents). The FDA used these data along with other data to understand the scope of the pandemic and to predict drug shortages or needs for additional medical equipment, including ventilators. This information was critical at the start of the pandemic.

Limitations

For the most part, MVP infrastructure and partnerships were efficiently leveraged to significantly advance our understanding of the biological basis of COVID-19 and to develop treatments and vaccines. However, there were a few limitations that may have slowed timely and optimal outcomes. An issue not limited to the MVP or VA was the continual evolution of the pandemic and its response. This included evolving definitions of disease, symptomatology, testing, vaccines, and public health recommendations. Keeping pace with the emerging knowledge from these domains was a struggle for the entire scientific community. A more discrete limitation was the number of participants in the MVP with positive COVID-19 test results and positive symptoms; however, this was mitigated by partnering with other groups like the COVID-19 Host Genetics Initiative to increase study participant numbers. Finally, there were logistical and regulatory challenges associated with coordination of national clinical trial recruitment across a VA system with > 100 discrete hospitals.

Conclusions

Having a centralized infrastructure for recruitment and enrollment, including a national research volunteer registry, information center, research staff, and coordinating centers, can allow for expedited enrollment in vaccine and treatment trials in the face of future public health emergencies. VA assets, including its rich EHR and MVP, the world’s largest genomic cohort, have contributed to improving our understanding and management of COVID-19. MVP’s ready-to-respond research infrastructure embedded within the country’s largest national health care system allows for both the facilitation of the research work and applications of the research findings into practice. Findings from the MVP COVID-19 working groups have yielded compelling results, particularly around genetic variants among various racial and ethnic groups. Looking ahead, the VA and DOE are launching a new joint project on long COVID that will include developing a gold-standard definition for long COVID. The ORD has established a Partnered Research Program to facilitate collaborations with industry to speed up clinical trials, and the MVP will continue to contribute toward expanding scientific knowledge to improve the management of COVID-19.

The Million Veteran Program (MVP) was launched in 2011 by the US Department of Veterans Affairs (VA) to enroll at least 1 million veterans in a longitudinal cohort to better understand how genes, lifestyle, military experience, and environmental exposures interact to influence health and illness and ultimately enable precision health care. The MVP has established a national, centralized infrastructure for recruitment and enrollment, biospecimen and data collection and storage, data generation and curation, and secure data access. When the COVID-19 pandemic hit in 2020, the MVP was leveraged to support research utilizing the following key infrastructure components: (1) MVP recruitment and enrollment platform to provide support for COVID-19 vaccine and treatment trials and to collect COVID-19 data from MVP participants; (2) using MVP Phenomics for COVID-19 research data cleaning and curation, assisting with the development of a VA Severity Index for COVID-19, and forming 6 scientific working groups to coordinate COVID-19 research questions; and (3) the VA/MVP and US Department of Energy (DOE) partnership to assist in responding to COVID-19 research questions identified by the US Food and Drug Administration (FDA). This article describes these infrastructure components in more detail and highlights key findings from the MVP COVID-19 research efforts.

MVP Infrastructure

The Veterans Health Administration (VHA) Office of Research and Development (ORD) oversaw efforts to develop the VA Coronavirus Research Volunteer List (the COVID-19 registry). To support the registry, the MVP leveraged its infrastructure to facilitate a rapid response. The MVP is designed as a full-service and centralized recruitment and enrollment platform. This includes MVP office oversight; MVP coordinating centers that manage the centralized platform; an information center that handles inbound and outbound calls; an informatics system built for recruitment and enrollment monitoring and tracking; and a network of more than 70 participating MVP sites with dedicated staff to conduct recruitment and enrollment activities. The MVP used its informatics infrastructure to support secure data storage for the registry volunteer information. MVP coordinating center staff worked with the COVID-19 registry to invite > 125,000 MVP participants from approximately 20 MVP sites. Additionally, MVP information center staff made > 4000 calls to prospective registry volunteers. This work resulted in 1300 volunteers agreeing to be contacted by COVID-19 vaccine clinical trial study teams (including Moderna, Janssen, AstraZeneca, and Novavax). About 20 MVP site staff (spanning 14 MVP sites) also were deployed to support COVID-19 work for clinical care capabilities or vaccine trials.

New Data Collection

The MVP protocol was approved by the VA Central Institutional Review Board (IRB) in 2011. As part of initial enrollment in MVP, participants consented to recontact for additional self-report information along with access to their electronic health record (EHR). This allows for the linkage of EHR and survey response data, thus providing a comprehensive understanding of health history before and after a self-reported COVID-19 diagnosis. Between May 2020 and September 2021, the MVP COVID-19 survey was distributed to existing MVP participants via mail, telephone, and email with the ability to complete the survey by paper and pencil or through the MVP online system. Dissemination of the survey was approved by the VA Central IRB in 2020, with nearly 730,000 eligible MVP participants contacted. As of June 2022, 255,737 MVP participants (35% of the eligible cohort) had completed the survey; 86% completed a paper survey while 14% completed it online. Respondents were primarily older (≥ 65 years); 90% were male; close to 7% reported Hispanic ethnicity, and 11% reported Black race.

Findings from this survey provide insight into pandemic behaviors not consistently captured in EHRs, such as psychosocial aspects, including social and emotional support, loss of tangible and intangible resources, as well as COVID-19–related behaviors, such as social distancing and self-protective practices.1 MVP COVID-19 survey data combined with veteran EHRs, responses to other MVP surveys, and genetic data enable MVP researchers to better understand epidemiological, clinical, and psychosocial aspects of the disease. Future COVID-19 studies may use self-reported survey responses to enrich understanding about the effects of the disease on a veteran’s daily life, and possibly validate existing EHR COVID-19 diagnoses and hospitalization findings. This comprehensive data resource provides a unique opportunity to identify new targets for disease prevention, treatment, and management with an emphasis on individual variability in genes, environment, and lifestyle.

COVID-19 Research

In early 2020, the burden of COVID-19 on the US was unprecedented, and little was known about risk factors for severe COVID-19 and deaths. The MVP Phenomics team quickly responded with a large-scale phenome-wide association study (PheWAS) of > 1800 phenotypes (physical and biochemical traits) and COVID-19 progression. Its goal was to characterize risk factors and outcomes associated with COVID-19 disease progression.2 Data curation and assembly occurred rapidly through integrated efforts led by MVP and VA COVID-19 initiatives. The MVP utilized its phenomics core resource to understand the progression of COVID-19 defined by SARS-CoV-2 infection, hospitalization, intensive care unit admission, and 30-day mortality using VA EHR data.

To broaden disease progression data curation and fit the specific needs of the VA, we operationalized and validated the World Health Organization clinical severity scale and used VA EHR data to create the VA Severity Index for COVID-19 (VASIC).3 The VASIC category is now part of the MVP core data repository, where volumes of data from multiple activities are integrated through an automated process to create monthly research-ready data cubes. These activities include extensive data curation, mapping, phenotyping, and adjudication that are performed to curate oxygen supplementation status and other procedures related to treatment that are processed and understood in real time. The data cubes were provisioned to MVP COVID-19 researchers. In addition, the VASIC scale variable is now integrated within the larger VA system for all researchers to use as part of its wider COVID-19 initiative. The VA Centralized Interactive Phenomics Resource (CIPHER) phenomics library now hosts the details of VASIC, codes, metadata, and related COVID-19 data products for all VA communities. In partnership with CIPHER and other internal and external COVID-19 initiatives, the MVP continues to play an integral part for the VA and beyond in the development of a phenomics algorithm for long COVID, or post-acute COVID-19 syndrome (PACS).

 

 

Host Genetics in COVID-19

As the SARS-CoV-2 virus continued to spread globally, it became clear that the symptoms and severity of infection experienced by patients varied across a broad spectrum, from being asymptomatic carriers to experiencing severe symptoms in 1 or more organ systems in the body, resulting in death. This variability suggested that host genetics and other host factors may play a role in determining the severity of COVID-19 infection. The MVP dataset, with genetic and health information on > 600,000 MVP participants, provided an ideal dataset to explore host contributions to COVID-19.

In late spring 2020, the MVP executive committee issued a call to the MVP research community to propose study aims around the COVID-19 pandemic that could leverage the phenotypic and genetic data and resources. The MVP quickly formed 6 rapid-response scientific working groups. Their mission was to cultivate collaboration and inclusivity and to coordinate COVID-19 research questions. A steering committee composed of the MVP executive committee, staff from computational environments, working group cochairs, and an administrator, who was responsible for daily oversight of the working groups. In addition, the ORD COVID-19 steering committee reviewed and approved research activities to ensure scientific rigor, as well as alignment with overall ongoing research activities.

table

The MVP COVID-19 working groups included dozens of researchers who used MVP data to identify disease mechanisms; understand the impact of host genetics on susceptibility, morbidity, and mortality; and identify potential targets for treatments and therapies. The working groups were further supported by MVP analysts to work cross-functionally on genomics, phenomics, statistical genetics, and PheWAS. Each working group chair was responsible for prioritizing concepts and moving them forward in coordination with the MVP and ORD COVID-19 steering committees. An overview of the MVP COVID-19 working groups follows (Table).4-9

Druggable genome. This working group researched drug-repurposing opportunities to prevent severe COVID-19, defined as hospitalization with oxygen therapy (high flow), intubation, mechanical ventilation, vasopressors, dialysis, or death from COVID-19; and prevent complications in patients hospitalized by COVID-19.

Pharmacogenomics. This working group focused on 2 main aims: the impact of apolipoprotein L1 risk variants on acute kidney injury (AKI) and death in Black veterans with COVID-19; and pharmacogenetic analysis of remdesivir-induced liver chemistry abnormalities.

Disease mechanisms. Understanding the underlying pathways and mechanisms behind COVID-19 has been a difficult but important challenge overall in the scientific community. This working group investigated specific genetic markers and effects on COVID-19, including polygenic predisposition to venous thromboembolism associated with increased COVID-19 susceptibility; renal comorbidities and new AKI and unfavorable outcomes among COVID-19–positive sickle cell trait carriers; and mucin 5B, oligomeric mucus/gel-forming gene polymorphism, and protective effects in COVID-19 infection.

Genomics for risk prediction, polygenic risk scores, and mendelian randomization. Risk prediction for COVID-19 has been widely studied mostly aiming at comorbidities and preexisting conditions. The MVP cohort provided a unique opportunity to understand how genetic information can enhance our understanding of COVID-19 risk. This working group focused on: (1) ABO blood group typing and the protective effects of the O blood group on COVID-19 infection; (2) polygenic risk scores and COVID-19 outcomes; (3) human leukocyte antigen typing and COVID-19 outcomes; and (4) a transcriptome-wide association study of COVID-19–positive MVP participants.

Genome-Wide Association Study (GWAS) and Downstream Analysis. This working group performed GWAS of the main COVID-19 outcomes. Results from GWAS unveiled new genetic loci to suggest further investigation on these candidate genes. The results were used by other MVP COVID-19 working groups for their activities. The results also contributed to external collaborations, such as the COVID-19 Host Genetics Initiative.

COVID-19–Related PheWAS. This working group focused on understanding the potential clinical significance of genetic variants associated with susceptibility to, or outcomes of, COVID-19 infection. They worked to identify traits that share genetic variants associated with severe COVID-19 from the Host Genetics Initiative. The group also studied the phenotypic consequences of acquired mosaic chromosomal alterations with early data linking to COVID-19 susceptibility.

 

 

COVID-19 Research Partnerships

In 2016, the VA and DOE formed an interagency partnership known as Computational Health Analytics for Medical Precision to Improve Outcomes Now (CHAMPION) to demonstrate the power of combining the VA EHR system, MVP genetic data, and clinical research expertise with DOE high-performance computing infrastructure and artificial intelligence expertise. The VA EHR captures longitudinal care information on veterans with records that go back decades. Furthermore, the VA covers the costs of medications and provides a variety of services through the Veterans Benefits Administration. As a result, VA data include medications used by patients before, during, and after COVID-19. Similarly, the VA has comprehensive vital records, whereas other large health systems do not capture events such as death after patients leave the hospital.

The DOE Oak Ridge National Laboratory (ORNL) in Tennessee securely maintains this rich database for the VA. The ORNL Summit supercomputer can complete trillions of calculations per second to provide critical and timely analyses, applying the most advanced and powerful artificial intelligence methods, which would not be possible in more conventional research settings. CHAMPION taught the VA and DOE how to bring their disparate research cultures together for innovative collaborative investigation. Moreover, this collaboration produced a cadre of VA and DOE scientists familiar with VA patient data and experienced in conducting joint research successfully and integrating omics data with clinical data for a better mechanistic understanding. Because of this preexisting collaboration between the VA and DOE, interagency teams were prepared at the start of the COVID-19 pandemic.10-15

During the pandemic, the FDA and VA conducted research together. One joint study found that the bradykinin storm is likely to play a role in many COVID-19 symptoms. Using VA data, researchers compared COVID-19 testing patterns, positive test results, and 30-day mortality rates by race and ethnicity among VA patients.10,11These findings demonstrated the higher burden COVID-19 placed on Black and Hispanic communities, not fully explained by underlying health conditions, access to medical care, or geographic locale.11

Other recently completed studies have developed and validated short-term mortality indices in individuals with COVID-19 based on their preexisting conditions, assessed the generalizability of VA COVID-19 experiences to the US population, and evaluated the effectiveness of hydroxychloroquine with and without azithromycin in VA patients with COVID-19.12,15 A recent study demonstrated the benefit of prophylactic anticoagulation at initial hospitalization.14

The VA also provided the FDA with daily reports on aggregate VA COVID-19 cases and their distribution across the VA system, demographics of VA patients with COVID-19, and analyses of predictive models for positive test results and death. The VA regularly sent the FDA aggregated data showing patterns of medication use and retrospective analyses of the effectiveness of certain medications (including remdesivir and some antithrombotic agents). The FDA used these data along with other data to understand the scope of the pandemic and to predict drug shortages or needs for additional medical equipment, including ventilators. This information was critical at the start of the pandemic.

Limitations

For the most part, MVP infrastructure and partnerships were efficiently leveraged to significantly advance our understanding of the biological basis of COVID-19 and to develop treatments and vaccines. However, there were a few limitations that may have slowed timely and optimal outcomes. An issue not limited to the MVP or VA was the continual evolution of the pandemic and its response. This included evolving definitions of disease, symptomatology, testing, vaccines, and public health recommendations. Keeping pace with the emerging knowledge from these domains was a struggle for the entire scientific community. A more discrete limitation was the number of participants in the MVP with positive COVID-19 test results and positive symptoms; however, this was mitigated by partnering with other groups like the COVID-19 Host Genetics Initiative to increase study participant numbers. Finally, there were logistical and regulatory challenges associated with coordination of national clinical trial recruitment across a VA system with > 100 discrete hospitals.

Conclusions

Having a centralized infrastructure for recruitment and enrollment, including a national research volunteer registry, information center, research staff, and coordinating centers, can allow for expedited enrollment in vaccine and treatment trials in the face of future public health emergencies. VA assets, including its rich EHR and MVP, the world’s largest genomic cohort, have contributed to improving our understanding and management of COVID-19. MVP’s ready-to-respond research infrastructure embedded within the country’s largest national health care system allows for both the facilitation of the research work and applications of the research findings into practice. Findings from the MVP COVID-19 working groups have yielded compelling results, particularly around genetic variants among various racial and ethnic groups. Looking ahead, the VA and DOE are launching a new joint project on long COVID that will include developing a gold-standard definition for long COVID. The ORD has established a Partnered Research Program to facilitate collaborations with industry to speed up clinical trials, and the MVP will continue to contribute toward expanding scientific knowledge to improve the management of COVID-19.

References

1. Whitbourne SB, Nguyen XT, Song RJ, et al. Million Veteran Program’s response to COVID-19: survey development and preliminary findings. PLoS One. 2022;17(4):e0266381. doi:10.1371/journal.pone.0266381

2. Song RJ, Ho YL, Schubert P, et al. Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program. PLoS One. 2021;16(5):e0251651. doi:10.1371/journal.pone.0251651

3. Galloway A, Park Y, Tanukonda V, et al. Impact of COVID-19 severity on long-term events in US veterans using the Veterans Affairs Severity Index for COVID-19 (VASIC). J Infect Dis. 2022;226(12):2113-2117. doi:10.1093/infdis/jiac182

4. Gaziano L, Giambartolomei C, Pereira AC, et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med. 2021;27(4):668-676. doi:10.0138/s41591-021-01310-z

5. Hung AM, Sha SC, Bick AG, et al. APOL1 risk variants, acute kidney injury, and death in participants with African ancestry hospitalized with COVID-19 from the Million Veteran Program. JAMA Intern Med. 2022;182(4):386-395. doi:10.1001/jamainternmed.2021.8538

6. Verma A, Huffman JE, Gao L, et al. Association of kidney comorbidities and acute kidney failure with unfavorable outcomes after COVID-19 in individuals with the sickle cell trait. JAMA Intern Med. 2022;182(8):796-804. doi:10.1001/jamainternmed.2022.2141

7. Verma A, Tsao NL, Thomann LO, et al. A phenome-wide association study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program. PLoS Genet. 2022;18(4):e1010113. doi:10.1371/journal.pgen.1010113

8. Peloso GM, Tcheandjieu C, McGeary JE, et al. Genetic loci associated with COVID-19 positivity and hospitalization in White, Black, and Hispanic Veterans of the VA Million Veteran Program. Front Genetic. 2022;12:777076. doi:10.3389/fgene.2021.777076

9. Verma A, Minnier J, Wan ES, et al. A MUC5B gene polymorphism, rs35705950-T confers protective effects against COVID-19 hospitalization but not severe disease or mortality. Am J Respir Crit Care Med. 2022;182(8):796-804. doi:10.1164/rccm.202109-2166OC

10. Garvin MR, Alvarez C, Miller JI, et al. A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm. Elife. 2020;e59177. doi:10.7554/eLife.59177

11. Rentsch CT, Kidwai-Khan F, Tate JP, et al. Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study. PLoS Med. 2020;17(9):e1003379. doi:10.1371/journal.pmed.1003379

12. King JT, Yoon JS, Rentsch CT, et al. Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: the Veterans Health Administration COVID-19 (VACO) Index. PLoS One. 2020;15(11):e0241825. doi:10.1371/journal.pone.0241825

13. Joubert W, Weighill D, Kainer D, et al. Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Dallas, TX, USA, 2018:717-730. doi:10.1109/SC.2018.00060

14. Rentsch CT, Beckman JA, Tomlinson L, et al. Early initiation of prophylactic anticoagulation for prevention of COVID-19 mortality: a nationwide cohort study of hospitalized patients in the United States. BMJ. 2021;372:n311. doi:10.1136/bmj.n311

15. Gerlovin H, Posner DC, Ho YL, et al. Pharmacoepidemiology, machine learning, and COVID-19: an intent-to-treat analysis of hydroxychloroquine, with or without Azithromycin, and COVID-19 outcomes among hospitalized US Veterans. Am J Epidemiol. 2021;190(11): 2405-2419. doi:10.1093/aje/kwab183

References

1. Whitbourne SB, Nguyen XT, Song RJ, et al. Million Veteran Program’s response to COVID-19: survey development and preliminary findings. PLoS One. 2022;17(4):e0266381. doi:10.1371/journal.pone.0266381

2. Song RJ, Ho YL, Schubert P, et al. Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program. PLoS One. 2021;16(5):e0251651. doi:10.1371/journal.pone.0251651

3. Galloway A, Park Y, Tanukonda V, et al. Impact of COVID-19 severity on long-term events in US veterans using the Veterans Affairs Severity Index for COVID-19 (VASIC). J Infect Dis. 2022;226(12):2113-2117. doi:10.1093/infdis/jiac182

4. Gaziano L, Giambartolomei C, Pereira AC, et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med. 2021;27(4):668-676. doi:10.0138/s41591-021-01310-z

5. Hung AM, Sha SC, Bick AG, et al. APOL1 risk variants, acute kidney injury, and death in participants with African ancestry hospitalized with COVID-19 from the Million Veteran Program. JAMA Intern Med. 2022;182(4):386-395. doi:10.1001/jamainternmed.2021.8538

6. Verma A, Huffman JE, Gao L, et al. Association of kidney comorbidities and acute kidney failure with unfavorable outcomes after COVID-19 in individuals with the sickle cell trait. JAMA Intern Med. 2022;182(8):796-804. doi:10.1001/jamainternmed.2022.2141

7. Verma A, Tsao NL, Thomann LO, et al. A phenome-wide association study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program. PLoS Genet. 2022;18(4):e1010113. doi:10.1371/journal.pgen.1010113

8. Peloso GM, Tcheandjieu C, McGeary JE, et al. Genetic loci associated with COVID-19 positivity and hospitalization in White, Black, and Hispanic Veterans of the VA Million Veteran Program. Front Genetic. 2022;12:777076. doi:10.3389/fgene.2021.777076

9. Verma A, Minnier J, Wan ES, et al. A MUC5B gene polymorphism, rs35705950-T confers protective effects against COVID-19 hospitalization but not severe disease or mortality. Am J Respir Crit Care Med. 2022;182(8):796-804. doi:10.1164/rccm.202109-2166OC

10. Garvin MR, Alvarez C, Miller JI, et al. A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm. Elife. 2020;e59177. doi:10.7554/eLife.59177

11. Rentsch CT, Kidwai-Khan F, Tate JP, et al. Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study. PLoS Med. 2020;17(9):e1003379. doi:10.1371/journal.pmed.1003379

12. King JT, Yoon JS, Rentsch CT, et al. Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: the Veterans Health Administration COVID-19 (VACO) Index. PLoS One. 2020;15(11):e0241825. doi:10.1371/journal.pone.0241825

13. Joubert W, Weighill D, Kainer D, et al. Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. Dallas, TX, USA, 2018:717-730. doi:10.1109/SC.2018.00060

14. Rentsch CT, Beckman JA, Tomlinson L, et al. Early initiation of prophylactic anticoagulation for prevention of COVID-19 mortality: a nationwide cohort study of hospitalized patients in the United States. BMJ. 2021;372:n311. doi:10.1136/bmj.n311

15. Gerlovin H, Posner DC, Ho YL, et al. Pharmacoepidemiology, machine learning, and COVID-19: an intent-to-treat analysis of hydroxychloroquine, with or without Azithromycin, and COVID-19 outcomes among hospitalized US Veterans. Am J Epidemiol. 2021;190(11): 2405-2419. doi:10.1093/aje/kwab183

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VA Lessons From Partnering in COVID-19 Clinical Trials

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The US Department of Veterans Affairs (VA), through its Office of Research and Development (ORD), supports an extensive and experienced clinical research enterprise, including the first multisite trials in the US.1 These resources contribute to the ORD support for the largest US integrated health care system, with a primary focus on the care and well-being of veterans. While the history of VA research has facilitated the creation of an experienced and organized research enterprise, the COVID-19 pandemic challenged VA to contribute even more significantly. These challenges became pronounced given the urgency associated with standing up VA sites for both therapeutic and vaccine trials.

VA Clinical Research Enterprise

The VA recognized an early need for an organized research response not only to address operational challenges resulting from COVID-19 but also ensure that the agency would be ready to support new scientific efforts focused specifically on the virus and related outcomes.2 As a result, the ORD took decisive action first by establishing itself as a central headquarters for VA COVID-19 research activities, and second, by leveraging existing resources, initiatives, and infrastructure to develop new mechanisms that would ensure that the VA was well positioned to develop or participate in research endeavors being driven by the VA as well federal, industry, and non-VA partners.

Prior to the pandemic, the ORD, through its Cooperative Studies Program (CSP), had strategies to address challenges associated with clinical trial startup and improved efficient conduct.3 For example, the VA Network of Dedicated Enrollment Sites (NODES) is a consortium of 23 VA medical centers (VAMCs) dedicated to rapid startup and recruitment into VA-sponsored clinical trials. NODES provides site-level expertise on clinical trial management, including troubleshooting challenges that may occur during clinical research execution.4 Another initiative, Access to Clinical Trials (ACT) for Veterans, engaged industry, academic, patient advocacy, and other partners to identify potential regulatory and operational hurdles to efficient startup activities specific to externally sponsored multisite clinical trials. Under ACT for Veterans, stakeholders emphasized the importance of developing a single VA point of contact for external partners to work with to more efficiently understand and navigate the VA system. In turn, such a resource could be designed to facilitate substantive research and long-term relationships with compatible external partners. Targeted to launch in April 2020, the Partnered Research Program (PRP) was expedited to respond to the pandemic.

During the pandemic, new VA efforts included the creation of the VA CoronavirUs Research and Efficacy Studies (VA CURES) network, initially established as a clinical trial master protocol framework to support and maximize VA-funded COVID-19 trial efficiency.5 VA CURES joined the consortium of trials networks funded by the National Heart, Lung, and Blood Institute. It began treatment trials under Accelerating COVID-19 Therapeutic Interventions and Vaccination (ACTIV), specifically ACTIV-4. The VA also partnered with the National Institutes of Allergy and Infectious Diseases (NIAID) by organizing the VA International Coordinating Center (VA ICC) for other ACTIV trials (ACTIV-2 and -3). When approached to startup studies that included veterans and the VA health care system, these capabilities comprised the VA research response.

A Need for a New Approach

As the impact of the pandemic expanded and the need for effective treatments and vaccines grew, national calls were made to assess the capabilities and readiness of available clinical trials networks. Additionally, the US Department of Health and Human Services Biomedical Advanced Research and Development Authority, ACTIV, NIAID Division of Clinical Research and Division of AIDS, and many pharmaceutical companies were starting to roll out trials of new therapeutics and vaccines. These groups approached the VA to help evaluate the safety and efficacy of several therapeutics and vaccines because they recognized several advantages of the VA enterprise, including its position as the nation’s largest integrated health care system, its diverse patient population, and its expertise in conducting clinical trials.

Although the VA was well positioned as an important player in a collaborative investigational approach to COVID-19 research, these trials required startup approaches that were significantly different from those it had employed in traditional, prepandemic, clinical research. Despite the VA being a single federal agency, each VAMC conducting research establishes its own practices to address both operational and regulatory requirements. This structure results in individual units that operate under different standard operating procedures. Efforts must be taken centrally to organize them into a singular network for the entire health care system. During a national crisis, when there was a need for rapid trial startup to answer safety and efficacy questions and participate under a common approach to protocol execution, this variability was neither manageable nor acceptable. Additionally, the intense resource demands associated with such research, coupled with frequent reporting requirements by VA leaders, Congress, and the White House, required that VAMCs function more like a single unit. Therefore, the ORD needed to develop VAMCs’ abilities to work collectively toward a common goal, share knowledge and experience, and capitalize on potential efficiencies concerning legal, regulatory, and operational processes.

table

Beginning August 2020, 39 VAMCs joined 7 large-scale collaborative COVID-19 therapeutic and vaccine trials. Through its COVID-19 Research Response Team, the ORD identified, engaged, and directed appropriate resources to support the VAMC under a centralized framework for study management (Table). Centralized management not only afforded VAMCs the opportunity to work more collectively and efficiently but also provided an important advantage by enabling the VA to collect and organize its experiences (and on occasion data) to provide a base for continual learning and improvement efforts. While others have described efforts undertaken across networks to advance learning health systems, the VA’s national scope and integration of research and clinical care allow greater opportunities to learn in a practical setting.6

 

 

Challenges and Best Practices

Using surveys, webinars, interviews, and observation from site and VA Central Office personnel, the ORD identified specific variables that prevented the VAMCs from quickly starting up as a clinical trial site. We also documented strategies, solutions, and recommendations for improving startup time lines. These were organized into 8 categories: (1) site infrastructure needs and capabilities; (2) study management roles and responsibilities; (3) educational resources and training; (4) local review requirements and procedures; (5) study design demands; (6) contracting and budgeting; (7) central-level systems and processes; and (8) communication between external partners and within the VA.

Site Infrastructure Needs and Capabilities

A primary impediment to rapid study startup was a lack of basic infrastructure, including staff, space, and the agility necessary for the changing demands of high-priority, high-enrolling trials. This observation is not unique to the VA.7 Initially, certain facilities located in hot spots where COVID-19 was more prevalent became high-interest targets for study placement, despite varying degrees of available research infrastructure. Furthermore, pandemic shutdowns and quarantines permitted fewer employees onsite. This resulted in inadequate staffing in personnel needed to support required startup activities and those needed to handle the high volume of study participants who were being recruited, screened, enrolled, and followed. Additionally, as clinical care needs and infection control practices were prioritized, clinical research space was often appropriated for these needs, making it difficult to find the space to conduct trials. Lastly, supply chain issues also posed unique challenges, sometimes making it difficult for participating VAMCs to obtain needed materials, such as IV solution bags of specific sizes and contents, safety injection needles, and IV line filters.

The VA was able to use central purchasing/contracting at coordinating centers or the VA Central Office to support investigators and assist with finding supplies and clinical research space. VAMCs with research operating budgets to cover startup costs were better positioned to handle funding delays. During the pandemic, the ORD further contracted to supply administrative support to research offices to address regulatory and other requirements needed for startup activities. The ability to expand such central contracts to procure clinical research staff and outpatient clinical research space may also prove useful in meeting key needs at a site.

Management Roles and Responsibilities

Ambiguous and variable roles and responsibilities among the various partners and stakeholders represented a challenge given the large-scale, national, or international operations involved in the trials. VA attempts to operate uniformly were further limited given that each sponsor or group had preferred methods for operating and/or organizing work under urgent time lines. For example, one trial involved a coordinating center, a contract research organization, and federal partners that each worked with individual sites. Consequently, VA study teams would receive messages that were conflicting or unclear.

The VA learned that studies need a single “source of truth” and/or central command structure in times of urgency. To mitigate conflicting messages, vaccine trials relied on a clearinghouse through the PRP to interpret requirements or work on behalf of all sites before key actions were taken. For studies with the NIAID, the VA relied on experienced staff at the CSP coordinating center at the Perry Point, Maryland, VAMC before beginning. This approach especially helped with the challenges of understaffing and sites’ lack of familiarity with complex platform trial designs and already-established network practices within the ACTIV-2 and ACTIV-3 studies.

Educational Resources and Training

Since VA participation in externally sponsored, multisite clinical trials traditionally relies on an individual VAMC study team and its local resources, transitioning to centralized approaches for COVID-19 multisite studies created barriers. Many VAMCs were unfamiliar with newer capabilities for more rapid regulatory reviews and approvals involving commercial institutional review boards (IRBs) and central VA information security and privacy reviews. While tools and resources were available to facilitate these processes, real-time use had not been fully tested. As a result, everyone had to learn as they went along.

The simultaneous establishment of workflows required the ORD to centralize operations and provide training and guidance to field personnel. Although many principal investigators and clinical research coordinators had trial experience, training required unlearning previous understandings of requirements to meet urgent time lines. ORD enterprise road maps, central tools, and training materials also were made available on a study-by-study basis. Open communication was vital to train on central study materials while opportunities to discuss, question, and share experiences and ideas were promoted. The ORD also sent regular emails to prepare for upcoming work and/or raise awareness of identified challenges.

 

 

Local Review Requirements/Procedures

The clinical trials were impacted by varying VAMC review requirements and approval processes. Although VA policy defines standard requirements, the timing and procedures are left to the individual facility to determine any local factors to accommodate and/or resource availability. While such an approach is well understood within the VA, external sponsors were not as familiar and assumed a more uniform approach across all sites. In response, some VAMCs established ad hoc research and development committee review procedures, allowing study teams to obtain the necessary reviews in a timely fashion. However, not all VAMCs had the infrastructure (especially when clinical personnel had been redeployed to other priorities) to respond with such agility. One critical role of the VA Central Office coordinating entities was to communicate and manage external sponsor and group expectations surrounding individual site review time lines. However, establishing policies and procedures that focus on streamlining local review processes helped to broadly mitigate the COVID-19 trial challenges.

Study Design Demands

The design of COVID-19 studies combined with the uncertainty of the pandemic required rapid protocol changes and adaptations that were often difficult to deliver. The multinetwork trials that the VA collaborated on were platform or master protocol designs. These designs emphasized overall goals (eg, treating patients requiring intensive care unit care). However, because this trial strategy also introduces complexities that may impact review and execution among those unfamiliar with it, there is a need for increased discussion and understanding of this methodology.8 For example, there can be shared control groups, reliance on specific criteria for halting because of safety or futility concerns, or continuation and expansion applied through an external review board. Delays may arise when changes to study protocols occur rapidly or frequently and necessitate new regulatory reviews, negotiation of new agreements, modifications to contracts, changes to entry criteria, etc.

While the VA has adopted a quality by design framework, VA investigators noted many missed opportunities related to looking at outcomes with new diagnostics, studies of serology, outcomes related to vaccinations, and understanding the natural history of disease in these trials.9 The limited opportunities for investigator input suggested that the advantages offered by platform designs were not maximized during pandemic-focused urgencies. It was unclear whether this barrier was created by a general lack of awareness by sponsors or a lack of opportunities. At the very least, quality by design approaches may help avoid redundancies in documentation or study processes at the central and site levels.

Contracting and Budgeting

Given external sponsorship of COVID-19 trials, efficient contracting and budgeting were critical for a rapid start up. The variability of processes associated with these trials created several challenges that were compounded by issues, such as site sub-agreements and budget documents that did not always go to the correct groups and individuals. Furthermore, the VA’s ability to use contracted resources (eg, tents, trailers, personnel) that external sponsors had built into their contracts was more difficult for VA as a federal agency governed by other statues and policies. This also put VAMCs at a disadvantage from a timing perspective, as the VA often required additional time to find equivalent solutions that met federal regulations.

Although the VA was able to establish contract solutions to some issues, time was still lost while working to secure initial funding. Additionally, for needs such as home phlebotomy—commended for convenience to veterans and research staff—and engaging a specialized research team in the Office of General Counsel, early awareness of protocol needs and sponsor solutions could allow VA to pursue alternatives sooner.

Central-Level Systems and Processes

Not all challenges were at the VAMC level. As the ORD explored solutions, it learned that various tools and study platforms were available but not considered. Applications, such as eConsent, and file-sharing platforms that met existing information security and privacy requirements were needed but had to comply with the Privacy Act of 1974, Federal Information Security Modernization Act, and other requirements. Using sponsor-provided devices, such as drug temperature monitoring equipment, required additional review to ensure that they met system requirements for a national health care system. In addition, the VA uses a clinical trials management review system; however, its implementation was new at the time these trials began. Furthermore, the system engaged with some commercial IRBs but not all. This resulted in additional delays as VAMCs and central resources worked to familiarize themselves with the system and procedures.

The ability to work collaboratively across the VA includes having a framework in which key startup processes are standardized. This allows for efficiency and minimizes variability. Also, all stakeholders should understand the importance of holding discussions to identify appropriate solutions, guidance, and instruction. Finally, the VA must strive to be more nimble when adapting technological, regulatory, and financial processes.

 

 

Internal and External Communication

The value of communication—both internal and external—cannot be understated. Minimizing confusion, managing expectations, and ensuring consistent messaging were essential for rapid trial execution. Despite being the second largest federal agency, the VA did not have a seat at the study leadership table for several protocols. When it joined later, several study aspects were set and/or difficult to revise. Challenges affecting time and securing resources have been noted. The ability to plan and then share expectations and responsibilities across and within the respective participating organizations early in the process was perhaps the single factor that was most addressable. The VA enterprise organization and integration with other units could accentuate key communications that would be essential in time-sensitive activities.

VA as a Partner for Future Research

Before the pandemic, the VA had already undertaken a path to enhance its ability to partner as part of the national biomedical research enterprise. The need for COVID-19 therapeutic and vaccine trials accelerated opportunities to plan and develop processes and capabilities to advance this path. As a key strength for VA scientific activities, clinical trials represent a primary medium by which to develop its partnerships. Learning and development have become part of a culture that expedites opportunities for veterans who actively seek ways to contribute to medical knowledge and treatments for their peers and the nation.

CONCLUSIONS

Challenges associated with rapid startup and completion of clinical trials have been discussed for some time. During the pandemic, needs and barriers were magnified because of the heightened urgency for evidence-based therapeutics and vaccines. While the VA faced similar problems as well as those specific to it as a health care system, it had the opportunity to learn and more systematically implement solutions to help in its partnered efforts.10 As an enterprise, the VA hopes to apply lessons learned, strategies, and best practices to further its goals to enhance veteran access to clinical trials and respond to any future need to quickly establish evidence bases in pandemics and other health emergencies that warrant the rapid implementation of research.

Acknowledgments

The activities reported here were supported by the US Department of Veterans Affairs, Office of Research and Development.

References

1. Hays MT; US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development. A historical look at the establishment of the Department of Veterans Affairs Research & Development Program. Accessed August 28, 2023. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

2. Garcia AP, Huang GD, Arnheim L, Ramoni R, Clancy C. The VA research enterprise: a platform for national partnerships toward evidence building and scientific innovation. Fed Pract. 2023;40(suppl 5):S12-S17. doi:10.12788/fp.0425

3. Johnston SC, Lewis-Hall F, Bajpai A, et al. It’s time to harmonize clinical trial site standards. NAM Perspectives. October 9, 2017. Accessed August 28, 2023. https://nam.edu/wp-content/uploads/2017/10/Its-Time-to-Harmonize-Clinical-Trial-Site-1.pdf

4. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ Network of Dedicated Enrollment Sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

5. US Food and Drug Administration. Master protocols: efficient clinical trial design strategies to expedite development of oncology drugs and biologics guidance for industry. March 2022. Accessed August 23, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/master-protocols-efficient-clinical-trial-design-strategies-expedite-development-oncology-drugs-and

6. IOM Roundtable on Value & Science-Driven Care; Institute of Medicine. Continuous learning and improvement in health care. In: Integrating Research and Practice: Health System Leaders Working Toward High-Value Care: Workshop Summary. National Academies Press (US); 2015:chap 2. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK284654 7. Institute of Medicine (US). Building an infrastructure to support clinical trials. In: Envisioning a Transformed Clinical Trials Enterprise in the United States. National Academies Press (US); 2012:chap 5. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK114656

8. Park JJH, Harari O, Dron L, Lester RT, Thorlund K, Mills EJ. An overview of platform trials with a checklist for clinical readers. J Clin Epidemiol. 2020;125:1-8. doi:10.1016/j.jclinepi.2020.04.025

9. Meeker-O’Connell A, Glessner C, Behm M, et al. Enhancing clinical evidence by proactively building quality into clinical trials. Clin Trials. 2016;13(4):439-444. doi:10.1177/1740774516643491

10. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

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The US Department of Veterans Affairs (VA), through its Office of Research and Development (ORD), supports an extensive and experienced clinical research enterprise, including the first multisite trials in the US.1 These resources contribute to the ORD support for the largest US integrated health care system, with a primary focus on the care and well-being of veterans. While the history of VA research has facilitated the creation of an experienced and organized research enterprise, the COVID-19 pandemic challenged VA to contribute even more significantly. These challenges became pronounced given the urgency associated with standing up VA sites for both therapeutic and vaccine trials.

VA Clinical Research Enterprise

The VA recognized an early need for an organized research response not only to address operational challenges resulting from COVID-19 but also ensure that the agency would be ready to support new scientific efforts focused specifically on the virus and related outcomes.2 As a result, the ORD took decisive action first by establishing itself as a central headquarters for VA COVID-19 research activities, and second, by leveraging existing resources, initiatives, and infrastructure to develop new mechanisms that would ensure that the VA was well positioned to develop or participate in research endeavors being driven by the VA as well federal, industry, and non-VA partners.

Prior to the pandemic, the ORD, through its Cooperative Studies Program (CSP), had strategies to address challenges associated with clinical trial startup and improved efficient conduct.3 For example, the VA Network of Dedicated Enrollment Sites (NODES) is a consortium of 23 VA medical centers (VAMCs) dedicated to rapid startup and recruitment into VA-sponsored clinical trials. NODES provides site-level expertise on clinical trial management, including troubleshooting challenges that may occur during clinical research execution.4 Another initiative, Access to Clinical Trials (ACT) for Veterans, engaged industry, academic, patient advocacy, and other partners to identify potential regulatory and operational hurdles to efficient startup activities specific to externally sponsored multisite clinical trials. Under ACT for Veterans, stakeholders emphasized the importance of developing a single VA point of contact for external partners to work with to more efficiently understand and navigate the VA system. In turn, such a resource could be designed to facilitate substantive research and long-term relationships with compatible external partners. Targeted to launch in April 2020, the Partnered Research Program (PRP) was expedited to respond to the pandemic.

During the pandemic, new VA efforts included the creation of the VA CoronavirUs Research and Efficacy Studies (VA CURES) network, initially established as a clinical trial master protocol framework to support and maximize VA-funded COVID-19 trial efficiency.5 VA CURES joined the consortium of trials networks funded by the National Heart, Lung, and Blood Institute. It began treatment trials under Accelerating COVID-19 Therapeutic Interventions and Vaccination (ACTIV), specifically ACTIV-4. The VA also partnered with the National Institutes of Allergy and Infectious Diseases (NIAID) by organizing the VA International Coordinating Center (VA ICC) for other ACTIV trials (ACTIV-2 and -3). When approached to startup studies that included veterans and the VA health care system, these capabilities comprised the VA research response.

A Need for a New Approach

As the impact of the pandemic expanded and the need for effective treatments and vaccines grew, national calls were made to assess the capabilities and readiness of available clinical trials networks. Additionally, the US Department of Health and Human Services Biomedical Advanced Research and Development Authority, ACTIV, NIAID Division of Clinical Research and Division of AIDS, and many pharmaceutical companies were starting to roll out trials of new therapeutics and vaccines. These groups approached the VA to help evaluate the safety and efficacy of several therapeutics and vaccines because they recognized several advantages of the VA enterprise, including its position as the nation’s largest integrated health care system, its diverse patient population, and its expertise in conducting clinical trials.

Although the VA was well positioned as an important player in a collaborative investigational approach to COVID-19 research, these trials required startup approaches that were significantly different from those it had employed in traditional, prepandemic, clinical research. Despite the VA being a single federal agency, each VAMC conducting research establishes its own practices to address both operational and regulatory requirements. This structure results in individual units that operate under different standard operating procedures. Efforts must be taken centrally to organize them into a singular network for the entire health care system. During a national crisis, when there was a need for rapid trial startup to answer safety and efficacy questions and participate under a common approach to protocol execution, this variability was neither manageable nor acceptable. Additionally, the intense resource demands associated with such research, coupled with frequent reporting requirements by VA leaders, Congress, and the White House, required that VAMCs function more like a single unit. Therefore, the ORD needed to develop VAMCs’ abilities to work collectively toward a common goal, share knowledge and experience, and capitalize on potential efficiencies concerning legal, regulatory, and operational processes.

table

Beginning August 2020, 39 VAMCs joined 7 large-scale collaborative COVID-19 therapeutic and vaccine trials. Through its COVID-19 Research Response Team, the ORD identified, engaged, and directed appropriate resources to support the VAMC under a centralized framework for study management (Table). Centralized management not only afforded VAMCs the opportunity to work more collectively and efficiently but also provided an important advantage by enabling the VA to collect and organize its experiences (and on occasion data) to provide a base for continual learning and improvement efforts. While others have described efforts undertaken across networks to advance learning health systems, the VA’s national scope and integration of research and clinical care allow greater opportunities to learn in a practical setting.6

 

 

Challenges and Best Practices

Using surveys, webinars, interviews, and observation from site and VA Central Office personnel, the ORD identified specific variables that prevented the VAMCs from quickly starting up as a clinical trial site. We also documented strategies, solutions, and recommendations for improving startup time lines. These were organized into 8 categories: (1) site infrastructure needs and capabilities; (2) study management roles and responsibilities; (3) educational resources and training; (4) local review requirements and procedures; (5) study design demands; (6) contracting and budgeting; (7) central-level systems and processes; and (8) communication between external partners and within the VA.

Site Infrastructure Needs and Capabilities

A primary impediment to rapid study startup was a lack of basic infrastructure, including staff, space, and the agility necessary for the changing demands of high-priority, high-enrolling trials. This observation is not unique to the VA.7 Initially, certain facilities located in hot spots where COVID-19 was more prevalent became high-interest targets for study placement, despite varying degrees of available research infrastructure. Furthermore, pandemic shutdowns and quarantines permitted fewer employees onsite. This resulted in inadequate staffing in personnel needed to support required startup activities and those needed to handle the high volume of study participants who were being recruited, screened, enrolled, and followed. Additionally, as clinical care needs and infection control practices were prioritized, clinical research space was often appropriated for these needs, making it difficult to find the space to conduct trials. Lastly, supply chain issues also posed unique challenges, sometimes making it difficult for participating VAMCs to obtain needed materials, such as IV solution bags of specific sizes and contents, safety injection needles, and IV line filters.

The VA was able to use central purchasing/contracting at coordinating centers or the VA Central Office to support investigators and assist with finding supplies and clinical research space. VAMCs with research operating budgets to cover startup costs were better positioned to handle funding delays. During the pandemic, the ORD further contracted to supply administrative support to research offices to address regulatory and other requirements needed for startup activities. The ability to expand such central contracts to procure clinical research staff and outpatient clinical research space may also prove useful in meeting key needs at a site.

Management Roles and Responsibilities

Ambiguous and variable roles and responsibilities among the various partners and stakeholders represented a challenge given the large-scale, national, or international operations involved in the trials. VA attempts to operate uniformly were further limited given that each sponsor or group had preferred methods for operating and/or organizing work under urgent time lines. For example, one trial involved a coordinating center, a contract research organization, and federal partners that each worked with individual sites. Consequently, VA study teams would receive messages that were conflicting or unclear.

The VA learned that studies need a single “source of truth” and/or central command structure in times of urgency. To mitigate conflicting messages, vaccine trials relied on a clearinghouse through the PRP to interpret requirements or work on behalf of all sites before key actions were taken. For studies with the NIAID, the VA relied on experienced staff at the CSP coordinating center at the Perry Point, Maryland, VAMC before beginning. This approach especially helped with the challenges of understaffing and sites’ lack of familiarity with complex platform trial designs and already-established network practices within the ACTIV-2 and ACTIV-3 studies.

Educational Resources and Training

Since VA participation in externally sponsored, multisite clinical trials traditionally relies on an individual VAMC study team and its local resources, transitioning to centralized approaches for COVID-19 multisite studies created barriers. Many VAMCs were unfamiliar with newer capabilities for more rapid regulatory reviews and approvals involving commercial institutional review boards (IRBs) and central VA information security and privacy reviews. While tools and resources were available to facilitate these processes, real-time use had not been fully tested. As a result, everyone had to learn as they went along.

The simultaneous establishment of workflows required the ORD to centralize operations and provide training and guidance to field personnel. Although many principal investigators and clinical research coordinators had trial experience, training required unlearning previous understandings of requirements to meet urgent time lines. ORD enterprise road maps, central tools, and training materials also were made available on a study-by-study basis. Open communication was vital to train on central study materials while opportunities to discuss, question, and share experiences and ideas were promoted. The ORD also sent regular emails to prepare for upcoming work and/or raise awareness of identified challenges.

 

 

Local Review Requirements/Procedures

The clinical trials were impacted by varying VAMC review requirements and approval processes. Although VA policy defines standard requirements, the timing and procedures are left to the individual facility to determine any local factors to accommodate and/or resource availability. While such an approach is well understood within the VA, external sponsors were not as familiar and assumed a more uniform approach across all sites. In response, some VAMCs established ad hoc research and development committee review procedures, allowing study teams to obtain the necessary reviews in a timely fashion. However, not all VAMCs had the infrastructure (especially when clinical personnel had been redeployed to other priorities) to respond with such agility. One critical role of the VA Central Office coordinating entities was to communicate and manage external sponsor and group expectations surrounding individual site review time lines. However, establishing policies and procedures that focus on streamlining local review processes helped to broadly mitigate the COVID-19 trial challenges.

Study Design Demands

The design of COVID-19 studies combined with the uncertainty of the pandemic required rapid protocol changes and adaptations that were often difficult to deliver. The multinetwork trials that the VA collaborated on were platform or master protocol designs. These designs emphasized overall goals (eg, treating patients requiring intensive care unit care). However, because this trial strategy also introduces complexities that may impact review and execution among those unfamiliar with it, there is a need for increased discussion and understanding of this methodology.8 For example, there can be shared control groups, reliance on specific criteria for halting because of safety or futility concerns, or continuation and expansion applied through an external review board. Delays may arise when changes to study protocols occur rapidly or frequently and necessitate new regulatory reviews, negotiation of new agreements, modifications to contracts, changes to entry criteria, etc.

While the VA has adopted a quality by design framework, VA investigators noted many missed opportunities related to looking at outcomes with new diagnostics, studies of serology, outcomes related to vaccinations, and understanding the natural history of disease in these trials.9 The limited opportunities for investigator input suggested that the advantages offered by platform designs were not maximized during pandemic-focused urgencies. It was unclear whether this barrier was created by a general lack of awareness by sponsors or a lack of opportunities. At the very least, quality by design approaches may help avoid redundancies in documentation or study processes at the central and site levels.

Contracting and Budgeting

Given external sponsorship of COVID-19 trials, efficient contracting and budgeting were critical for a rapid start up. The variability of processes associated with these trials created several challenges that were compounded by issues, such as site sub-agreements and budget documents that did not always go to the correct groups and individuals. Furthermore, the VA’s ability to use contracted resources (eg, tents, trailers, personnel) that external sponsors had built into their contracts was more difficult for VA as a federal agency governed by other statues and policies. This also put VAMCs at a disadvantage from a timing perspective, as the VA often required additional time to find equivalent solutions that met federal regulations.

Although the VA was able to establish contract solutions to some issues, time was still lost while working to secure initial funding. Additionally, for needs such as home phlebotomy—commended for convenience to veterans and research staff—and engaging a specialized research team in the Office of General Counsel, early awareness of protocol needs and sponsor solutions could allow VA to pursue alternatives sooner.

Central-Level Systems and Processes

Not all challenges were at the VAMC level. As the ORD explored solutions, it learned that various tools and study platforms were available but not considered. Applications, such as eConsent, and file-sharing platforms that met existing information security and privacy requirements were needed but had to comply with the Privacy Act of 1974, Federal Information Security Modernization Act, and other requirements. Using sponsor-provided devices, such as drug temperature monitoring equipment, required additional review to ensure that they met system requirements for a national health care system. In addition, the VA uses a clinical trials management review system; however, its implementation was new at the time these trials began. Furthermore, the system engaged with some commercial IRBs but not all. This resulted in additional delays as VAMCs and central resources worked to familiarize themselves with the system and procedures.

The ability to work collaboratively across the VA includes having a framework in which key startup processes are standardized. This allows for efficiency and minimizes variability. Also, all stakeholders should understand the importance of holding discussions to identify appropriate solutions, guidance, and instruction. Finally, the VA must strive to be more nimble when adapting technological, regulatory, and financial processes.

 

 

Internal and External Communication

The value of communication—both internal and external—cannot be understated. Minimizing confusion, managing expectations, and ensuring consistent messaging were essential for rapid trial execution. Despite being the second largest federal agency, the VA did not have a seat at the study leadership table for several protocols. When it joined later, several study aspects were set and/or difficult to revise. Challenges affecting time and securing resources have been noted. The ability to plan and then share expectations and responsibilities across and within the respective participating organizations early in the process was perhaps the single factor that was most addressable. The VA enterprise organization and integration with other units could accentuate key communications that would be essential in time-sensitive activities.

VA as a Partner for Future Research

Before the pandemic, the VA had already undertaken a path to enhance its ability to partner as part of the national biomedical research enterprise. The need for COVID-19 therapeutic and vaccine trials accelerated opportunities to plan and develop processes and capabilities to advance this path. As a key strength for VA scientific activities, clinical trials represent a primary medium by which to develop its partnerships. Learning and development have become part of a culture that expedites opportunities for veterans who actively seek ways to contribute to medical knowledge and treatments for their peers and the nation.

CONCLUSIONS

Challenges associated with rapid startup and completion of clinical trials have been discussed for some time. During the pandemic, needs and barriers were magnified because of the heightened urgency for evidence-based therapeutics and vaccines. While the VA faced similar problems as well as those specific to it as a health care system, it had the opportunity to learn and more systematically implement solutions to help in its partnered efforts.10 As an enterprise, the VA hopes to apply lessons learned, strategies, and best practices to further its goals to enhance veteran access to clinical trials and respond to any future need to quickly establish evidence bases in pandemics and other health emergencies that warrant the rapid implementation of research.

Acknowledgments

The activities reported here were supported by the US Department of Veterans Affairs, Office of Research and Development.

The US Department of Veterans Affairs (VA), through its Office of Research and Development (ORD), supports an extensive and experienced clinical research enterprise, including the first multisite trials in the US.1 These resources contribute to the ORD support for the largest US integrated health care system, with a primary focus on the care and well-being of veterans. While the history of VA research has facilitated the creation of an experienced and organized research enterprise, the COVID-19 pandemic challenged VA to contribute even more significantly. These challenges became pronounced given the urgency associated with standing up VA sites for both therapeutic and vaccine trials.

VA Clinical Research Enterprise

The VA recognized an early need for an organized research response not only to address operational challenges resulting from COVID-19 but also ensure that the agency would be ready to support new scientific efforts focused specifically on the virus and related outcomes.2 As a result, the ORD took decisive action first by establishing itself as a central headquarters for VA COVID-19 research activities, and second, by leveraging existing resources, initiatives, and infrastructure to develop new mechanisms that would ensure that the VA was well positioned to develop or participate in research endeavors being driven by the VA as well federal, industry, and non-VA partners.

Prior to the pandemic, the ORD, through its Cooperative Studies Program (CSP), had strategies to address challenges associated with clinical trial startup and improved efficient conduct.3 For example, the VA Network of Dedicated Enrollment Sites (NODES) is a consortium of 23 VA medical centers (VAMCs) dedicated to rapid startup and recruitment into VA-sponsored clinical trials. NODES provides site-level expertise on clinical trial management, including troubleshooting challenges that may occur during clinical research execution.4 Another initiative, Access to Clinical Trials (ACT) for Veterans, engaged industry, academic, patient advocacy, and other partners to identify potential regulatory and operational hurdles to efficient startup activities specific to externally sponsored multisite clinical trials. Under ACT for Veterans, stakeholders emphasized the importance of developing a single VA point of contact for external partners to work with to more efficiently understand and navigate the VA system. In turn, such a resource could be designed to facilitate substantive research and long-term relationships with compatible external partners. Targeted to launch in April 2020, the Partnered Research Program (PRP) was expedited to respond to the pandemic.

During the pandemic, new VA efforts included the creation of the VA CoronavirUs Research and Efficacy Studies (VA CURES) network, initially established as a clinical trial master protocol framework to support and maximize VA-funded COVID-19 trial efficiency.5 VA CURES joined the consortium of trials networks funded by the National Heart, Lung, and Blood Institute. It began treatment trials under Accelerating COVID-19 Therapeutic Interventions and Vaccination (ACTIV), specifically ACTIV-4. The VA also partnered with the National Institutes of Allergy and Infectious Diseases (NIAID) by organizing the VA International Coordinating Center (VA ICC) for other ACTIV trials (ACTIV-2 and -3). When approached to startup studies that included veterans and the VA health care system, these capabilities comprised the VA research response.

A Need for a New Approach

As the impact of the pandemic expanded and the need for effective treatments and vaccines grew, national calls were made to assess the capabilities and readiness of available clinical trials networks. Additionally, the US Department of Health and Human Services Biomedical Advanced Research and Development Authority, ACTIV, NIAID Division of Clinical Research and Division of AIDS, and many pharmaceutical companies were starting to roll out trials of new therapeutics and vaccines. These groups approached the VA to help evaluate the safety and efficacy of several therapeutics and vaccines because they recognized several advantages of the VA enterprise, including its position as the nation’s largest integrated health care system, its diverse patient population, and its expertise in conducting clinical trials.

Although the VA was well positioned as an important player in a collaborative investigational approach to COVID-19 research, these trials required startup approaches that were significantly different from those it had employed in traditional, prepandemic, clinical research. Despite the VA being a single federal agency, each VAMC conducting research establishes its own practices to address both operational and regulatory requirements. This structure results in individual units that operate under different standard operating procedures. Efforts must be taken centrally to organize them into a singular network for the entire health care system. During a national crisis, when there was a need for rapid trial startup to answer safety and efficacy questions and participate under a common approach to protocol execution, this variability was neither manageable nor acceptable. Additionally, the intense resource demands associated with such research, coupled with frequent reporting requirements by VA leaders, Congress, and the White House, required that VAMCs function more like a single unit. Therefore, the ORD needed to develop VAMCs’ abilities to work collectively toward a common goal, share knowledge and experience, and capitalize on potential efficiencies concerning legal, regulatory, and operational processes.

table

Beginning August 2020, 39 VAMCs joined 7 large-scale collaborative COVID-19 therapeutic and vaccine trials. Through its COVID-19 Research Response Team, the ORD identified, engaged, and directed appropriate resources to support the VAMC under a centralized framework for study management (Table). Centralized management not only afforded VAMCs the opportunity to work more collectively and efficiently but also provided an important advantage by enabling the VA to collect and organize its experiences (and on occasion data) to provide a base for continual learning and improvement efforts. While others have described efforts undertaken across networks to advance learning health systems, the VA’s national scope and integration of research and clinical care allow greater opportunities to learn in a practical setting.6

 

 

Challenges and Best Practices

Using surveys, webinars, interviews, and observation from site and VA Central Office personnel, the ORD identified specific variables that prevented the VAMCs from quickly starting up as a clinical trial site. We also documented strategies, solutions, and recommendations for improving startup time lines. These were organized into 8 categories: (1) site infrastructure needs and capabilities; (2) study management roles and responsibilities; (3) educational resources and training; (4) local review requirements and procedures; (5) study design demands; (6) contracting and budgeting; (7) central-level systems and processes; and (8) communication between external partners and within the VA.

Site Infrastructure Needs and Capabilities

A primary impediment to rapid study startup was a lack of basic infrastructure, including staff, space, and the agility necessary for the changing demands of high-priority, high-enrolling trials. This observation is not unique to the VA.7 Initially, certain facilities located in hot spots where COVID-19 was more prevalent became high-interest targets for study placement, despite varying degrees of available research infrastructure. Furthermore, pandemic shutdowns and quarantines permitted fewer employees onsite. This resulted in inadequate staffing in personnel needed to support required startup activities and those needed to handle the high volume of study participants who were being recruited, screened, enrolled, and followed. Additionally, as clinical care needs and infection control practices were prioritized, clinical research space was often appropriated for these needs, making it difficult to find the space to conduct trials. Lastly, supply chain issues also posed unique challenges, sometimes making it difficult for participating VAMCs to obtain needed materials, such as IV solution bags of specific sizes and contents, safety injection needles, and IV line filters.

The VA was able to use central purchasing/contracting at coordinating centers or the VA Central Office to support investigators and assist with finding supplies and clinical research space. VAMCs with research operating budgets to cover startup costs were better positioned to handle funding delays. During the pandemic, the ORD further contracted to supply administrative support to research offices to address regulatory and other requirements needed for startup activities. The ability to expand such central contracts to procure clinical research staff and outpatient clinical research space may also prove useful in meeting key needs at a site.

Management Roles and Responsibilities

Ambiguous and variable roles and responsibilities among the various partners and stakeholders represented a challenge given the large-scale, national, or international operations involved in the trials. VA attempts to operate uniformly were further limited given that each sponsor or group had preferred methods for operating and/or organizing work under urgent time lines. For example, one trial involved a coordinating center, a contract research organization, and federal partners that each worked with individual sites. Consequently, VA study teams would receive messages that were conflicting or unclear.

The VA learned that studies need a single “source of truth” and/or central command structure in times of urgency. To mitigate conflicting messages, vaccine trials relied on a clearinghouse through the PRP to interpret requirements or work on behalf of all sites before key actions were taken. For studies with the NIAID, the VA relied on experienced staff at the CSP coordinating center at the Perry Point, Maryland, VAMC before beginning. This approach especially helped with the challenges of understaffing and sites’ lack of familiarity with complex platform trial designs and already-established network practices within the ACTIV-2 and ACTIV-3 studies.

Educational Resources and Training

Since VA participation in externally sponsored, multisite clinical trials traditionally relies on an individual VAMC study team and its local resources, transitioning to centralized approaches for COVID-19 multisite studies created barriers. Many VAMCs were unfamiliar with newer capabilities for more rapid regulatory reviews and approvals involving commercial institutional review boards (IRBs) and central VA information security and privacy reviews. While tools and resources were available to facilitate these processes, real-time use had not been fully tested. As a result, everyone had to learn as they went along.

The simultaneous establishment of workflows required the ORD to centralize operations and provide training and guidance to field personnel. Although many principal investigators and clinical research coordinators had trial experience, training required unlearning previous understandings of requirements to meet urgent time lines. ORD enterprise road maps, central tools, and training materials also were made available on a study-by-study basis. Open communication was vital to train on central study materials while opportunities to discuss, question, and share experiences and ideas were promoted. The ORD also sent regular emails to prepare for upcoming work and/or raise awareness of identified challenges.

 

 

Local Review Requirements/Procedures

The clinical trials were impacted by varying VAMC review requirements and approval processes. Although VA policy defines standard requirements, the timing and procedures are left to the individual facility to determine any local factors to accommodate and/or resource availability. While such an approach is well understood within the VA, external sponsors were not as familiar and assumed a more uniform approach across all sites. In response, some VAMCs established ad hoc research and development committee review procedures, allowing study teams to obtain the necessary reviews in a timely fashion. However, not all VAMCs had the infrastructure (especially when clinical personnel had been redeployed to other priorities) to respond with such agility. One critical role of the VA Central Office coordinating entities was to communicate and manage external sponsor and group expectations surrounding individual site review time lines. However, establishing policies and procedures that focus on streamlining local review processes helped to broadly mitigate the COVID-19 trial challenges.

Study Design Demands

The design of COVID-19 studies combined with the uncertainty of the pandemic required rapid protocol changes and adaptations that were often difficult to deliver. The multinetwork trials that the VA collaborated on were platform or master protocol designs. These designs emphasized overall goals (eg, treating patients requiring intensive care unit care). However, because this trial strategy also introduces complexities that may impact review and execution among those unfamiliar with it, there is a need for increased discussion and understanding of this methodology.8 For example, there can be shared control groups, reliance on specific criteria for halting because of safety or futility concerns, or continuation and expansion applied through an external review board. Delays may arise when changes to study protocols occur rapidly or frequently and necessitate new regulatory reviews, negotiation of new agreements, modifications to contracts, changes to entry criteria, etc.

While the VA has adopted a quality by design framework, VA investigators noted many missed opportunities related to looking at outcomes with new diagnostics, studies of serology, outcomes related to vaccinations, and understanding the natural history of disease in these trials.9 The limited opportunities for investigator input suggested that the advantages offered by platform designs were not maximized during pandemic-focused urgencies. It was unclear whether this barrier was created by a general lack of awareness by sponsors or a lack of opportunities. At the very least, quality by design approaches may help avoid redundancies in documentation or study processes at the central and site levels.

Contracting and Budgeting

Given external sponsorship of COVID-19 trials, efficient contracting and budgeting were critical for a rapid start up. The variability of processes associated with these trials created several challenges that were compounded by issues, such as site sub-agreements and budget documents that did not always go to the correct groups and individuals. Furthermore, the VA’s ability to use contracted resources (eg, tents, trailers, personnel) that external sponsors had built into their contracts was more difficult for VA as a federal agency governed by other statues and policies. This also put VAMCs at a disadvantage from a timing perspective, as the VA often required additional time to find equivalent solutions that met federal regulations.

Although the VA was able to establish contract solutions to some issues, time was still lost while working to secure initial funding. Additionally, for needs such as home phlebotomy—commended for convenience to veterans and research staff—and engaging a specialized research team in the Office of General Counsel, early awareness of protocol needs and sponsor solutions could allow VA to pursue alternatives sooner.

Central-Level Systems and Processes

Not all challenges were at the VAMC level. As the ORD explored solutions, it learned that various tools and study platforms were available but not considered. Applications, such as eConsent, and file-sharing platforms that met existing information security and privacy requirements were needed but had to comply with the Privacy Act of 1974, Federal Information Security Modernization Act, and other requirements. Using sponsor-provided devices, such as drug temperature monitoring equipment, required additional review to ensure that they met system requirements for a national health care system. In addition, the VA uses a clinical trials management review system; however, its implementation was new at the time these trials began. Furthermore, the system engaged with some commercial IRBs but not all. This resulted in additional delays as VAMCs and central resources worked to familiarize themselves with the system and procedures.

The ability to work collaboratively across the VA includes having a framework in which key startup processes are standardized. This allows for efficiency and minimizes variability. Also, all stakeholders should understand the importance of holding discussions to identify appropriate solutions, guidance, and instruction. Finally, the VA must strive to be more nimble when adapting technological, regulatory, and financial processes.

 

 

Internal and External Communication

The value of communication—both internal and external—cannot be understated. Minimizing confusion, managing expectations, and ensuring consistent messaging were essential for rapid trial execution. Despite being the second largest federal agency, the VA did not have a seat at the study leadership table for several protocols. When it joined later, several study aspects were set and/or difficult to revise. Challenges affecting time and securing resources have been noted. The ability to plan and then share expectations and responsibilities across and within the respective participating organizations early in the process was perhaps the single factor that was most addressable. The VA enterprise organization and integration with other units could accentuate key communications that would be essential in time-sensitive activities.

VA as a Partner for Future Research

Before the pandemic, the VA had already undertaken a path to enhance its ability to partner as part of the national biomedical research enterprise. The need for COVID-19 therapeutic and vaccine trials accelerated opportunities to plan and develop processes and capabilities to advance this path. As a key strength for VA scientific activities, clinical trials represent a primary medium by which to develop its partnerships. Learning and development have become part of a culture that expedites opportunities for veterans who actively seek ways to contribute to medical knowledge and treatments for their peers and the nation.

CONCLUSIONS

Challenges associated with rapid startup and completion of clinical trials have been discussed for some time. During the pandemic, needs and barriers were magnified because of the heightened urgency for evidence-based therapeutics and vaccines. While the VA faced similar problems as well as those specific to it as a health care system, it had the opportunity to learn and more systematically implement solutions to help in its partnered efforts.10 As an enterprise, the VA hopes to apply lessons learned, strategies, and best practices to further its goals to enhance veteran access to clinical trials and respond to any future need to quickly establish evidence bases in pandemics and other health emergencies that warrant the rapid implementation of research.

Acknowledgments

The activities reported here were supported by the US Department of Veterans Affairs, Office of Research and Development.

References

1. Hays MT; US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development. A historical look at the establishment of the Department of Veterans Affairs Research & Development Program. Accessed August 28, 2023. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

2. Garcia AP, Huang GD, Arnheim L, Ramoni R, Clancy C. The VA research enterprise: a platform for national partnerships toward evidence building and scientific innovation. Fed Pract. 2023;40(suppl 5):S12-S17. doi:10.12788/fp.0425

3. Johnston SC, Lewis-Hall F, Bajpai A, et al. It’s time to harmonize clinical trial site standards. NAM Perspectives. October 9, 2017. Accessed August 28, 2023. https://nam.edu/wp-content/uploads/2017/10/Its-Time-to-Harmonize-Clinical-Trial-Site-1.pdf

4. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ Network of Dedicated Enrollment Sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

5. US Food and Drug Administration. Master protocols: efficient clinical trial design strategies to expedite development of oncology drugs and biologics guidance for industry. March 2022. Accessed August 23, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/master-protocols-efficient-clinical-trial-design-strategies-expedite-development-oncology-drugs-and

6. IOM Roundtable on Value & Science-Driven Care; Institute of Medicine. Continuous learning and improvement in health care. In: Integrating Research and Practice: Health System Leaders Working Toward High-Value Care: Workshop Summary. National Academies Press (US); 2015:chap 2. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK284654 7. Institute of Medicine (US). Building an infrastructure to support clinical trials. In: Envisioning a Transformed Clinical Trials Enterprise in the United States. National Academies Press (US); 2012:chap 5. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK114656

8. Park JJH, Harari O, Dron L, Lester RT, Thorlund K, Mills EJ. An overview of platform trials with a checklist for clinical readers. J Clin Epidemiol. 2020;125:1-8. doi:10.1016/j.jclinepi.2020.04.025

9. Meeker-O’Connell A, Glessner C, Behm M, et al. Enhancing clinical evidence by proactively building quality into clinical trials. Clin Trials. 2016;13(4):439-444. doi:10.1177/1740774516643491

10. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

References

1. Hays MT; US Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development. A historical look at the establishment of the Department of Veterans Affairs Research & Development Program. Accessed August 28, 2023. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

2. Garcia AP, Huang GD, Arnheim L, Ramoni R, Clancy C. The VA research enterprise: a platform for national partnerships toward evidence building and scientific innovation. Fed Pract. 2023;40(suppl 5):S12-S17. doi:10.12788/fp.0425

3. Johnston SC, Lewis-Hall F, Bajpai A, et al. It’s time to harmonize clinical trial site standards. NAM Perspectives. October 9, 2017. Accessed August 28, 2023. https://nam.edu/wp-content/uploads/2017/10/Its-Time-to-Harmonize-Clinical-Trial-Site-1.pdf

4. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ Network of Dedicated Enrollment Sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

5. US Food and Drug Administration. Master protocols: efficient clinical trial design strategies to expedite development of oncology drugs and biologics guidance for industry. March 2022. Accessed August 23, 2023. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/master-protocols-efficient-clinical-trial-design-strategies-expedite-development-oncology-drugs-and

6. IOM Roundtable on Value & Science-Driven Care; Institute of Medicine. Continuous learning and improvement in health care. In: Integrating Research and Practice: Health System Leaders Working Toward High-Value Care: Workshop Summary. National Academies Press (US); 2015:chap 2. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK284654 7. Institute of Medicine (US). Building an infrastructure to support clinical trials. In: Envisioning a Transformed Clinical Trials Enterprise in the United States. National Academies Press (US); 2012:chap 5. Accessed August 28, 2023. https://www.ncbi.nlm.nih.gov/books/NBK114656

8. Park JJH, Harari O, Dron L, Lester RT, Thorlund K, Mills EJ. An overview of platform trials with a checklist for clinical readers. J Clin Epidemiol. 2020;125:1-8. doi:10.1016/j.jclinepi.2020.04.025

9. Meeker-O’Connell A, Glessner C, Behm M, et al. Enhancing clinical evidence by proactively building quality into clinical trials. Clin Trials. 2016;13(4):439-444. doi:10.1177/1740774516643491

10. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

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The VA Research Enterprise: A Platform for National Partnerships Toward Evidence Building and Scientific Innovation

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The US Department of Veterans Affairs (VA) plays a substantial role in the nation’s public health through the Veterans Health Administration (VHA). Its statutory missions of teaching, clinical care, and research enable it to serve a foundational role in the US biomedical enterprise.1 Throughout its extensive network of VA medical centers (VAMCs) and partnering academic affiliates, thousands of clinicians and researchers have been trained to improve the lives of veterans and benefit the lives of all Americans. In supporting the largest US integrated health care system, the VA also has numerous capabilities and resources that distinctively position it to produce scientific and clinical results specifically within the context of providing care. The VA has formed partnerships with other federal agencies, industry, and nonprofit entities. Its ability to be a nexus of health care and practice, scientific discovery, and innovative ways to integrate shared interests in these areas have led to many transformative endeavors that save lives and improve the quality of care for veterans and the public.

The COVID-19 pandemic triggered another mission: service in times of national emergency. Known as the Fourth Mission, the VA rapidly shifted to highlight how its health care and research enterprises could apply strengths in a unique, coordinated manner. While the Fourth Mission is typically considered in the context of clinical care, the VA’s movement toward greater integration facilitated the role of research as a key component in efforts under a learning health care model.2

VA Office of Research and Development

Within the VHA, the Office of Research and Development (ORD) develops research policy and oversees interdisciplinary efforts focused on generating evidence to improve veteran health.3 These activities span at least 100 of 171 VAMCs and include thousands of investigators and staff across all major health research disciplines. Many of these investigators are also clinicians who provide patient care and are experts in the prevention, diagnosis, and treatment of diseases and disorders affecting veterans.

The ORD has invested in a range of scientific, operational, regulatory, and technological assets and infrastructure as part of its enterprise. These strengths come from a nearly 100-year history originating as part of a set of hospital-based medical studies. This established the model for a culture of cooperative research within the VA and with external groups who benefit from the VA’s foundational role in multisite clinical trials.2,4,5 Today, the VA prioritizes bench-to-bedside research covering a broad spectrum of investigations, which are integrated with clinical operations and systems that deliver care.3 The VA supports an extensive range of work that covers core areas in preclinical and clinical studies to health services research, rehabilitation and implementation science, establishing expertise in genomic and data sciences, and more recent activities in artificial intelligence.

In 2017, the ORD began a focused strategy to transform into a national enterprise that capitalized on its place within the VA and its particular ability to translate and implement scientific findings into real impact for veteran health and care through 5 initiatives: (1) enhancing veteran access to high-quality clinical trials; (2) increasing the substantial real-world impact of VA Research; (3) putting VA data to work for veteran health; (4) promoting diversity, equity, and inclusion within our sphere of influence; and (5) building community through research. These activities are interrelated and, where possible, the ORD works with other VA clinical and operational offices to accomplish multiple goals and coordinate within the health care system. As such, the VA continually seeks to increase efficiencies and improve abilities that provide veterans with best-in-class health care. While still in its early stages, this strategy and its initiatives established a path for the ORD response to the pandemic.

Within 2 weeks of the World Health Organization and the US declaring a COVID-19 pandemic, the ORD began to address the developing needs and challenges of the yet unknown emerging public health threat. This included outreach to and contact from federal, academic, and industry partners. At the same time, the ORD maintained its focus and energy to support its ongoing veteran-centric research portfolio and VHA health care system needs across its broad scope of activities.

This article discusses how the pandemic accelerated the VA’s research enterprise strategy and enacted a response, highlighting the advantages and strengths of this direction. We demonstrate how this evolving strategy enabled the VA to quickly leverage partnerships during a health emergency. While the ORD and VA Research have been used interchangeably, we will attempt to distinguish between the office that serves as headquarters for the national enterprise—the ORD—and the components of that enterprise composed of scientific personnel, equipment, operational units, and partners—VA Research. Finally, we present lessons from this experience toward a broader, post–COVID-19, enterprise-wide approach that the VA has for providing evidence-based care. These experiences may enrich our understanding of postpandemic future research opportunities with the VA as a leader and partner who leverages its commitment to veterans to improve the nation’s health.

 

 

ORGANIZING THE VA COVID-19 RESEARCH RESPONSE

VA Research seeks to internally standardize and integrate collaborations with clinical and operational partners throughout the agency. When possible, it seeks to streamline partnership efforts involving external groups less familiar with how the VA operates or its policies, as well as its capabilities. This need was more obvious during the pandemic, and the ORD assembled its COVID-19 response quickly.6

figure

In early January 2020, VA offices, including the ORD, were carefully observing COVID-19. On March 4, 2020, a week before the World Health Organization declared COVID-19 a pandemic, the ORD and its National Research Advisory Council arranged a briefing from VA public health leaders to deal with reported cases of COVID-19 and VA plans. Immediately afterward, the ORD Chief Research and Development Officer gathered a team of experts in clinical research, infectious disease, and public health to strategize a broader research enterprise approach to the pandemic. This group quickly framed 3 key targets: (1) identify critical research questions to prioritize; (2) provide operational guidance to the research community; and (3) uphold VA research staff safety. This discussion led to the creation of a larger ORD COVID-19 Research Response Team that managed activities within this scope. This team included other ORD leaders and staff with operational, scientific, and regulatory expertise charged with enterprise-level planning and execution for all research activities addressing or affected by the pandemic (Figure).

Effective and timely communication was chief among key ORD responsibilities. On March 19, 2020, the Response Team informed the VA Research community about ORD plans for organizing the VA COVID-19 research response.7 It also mobilized VA research programs and investigators to support an enterprise approach that would be coordinated centrally. We achieved communication goals by developing a dedicated website, which provided a means to distribute up-to-date notices and guidance, answer frequently asked questions, and alert investigators about research opportunities. The site enabled the field to report on its efforts, which enhanced leadership and community awareness. A working group of ORD and field personnel managed communications. Given the volume of existing non–COVID-19 research, we established a research continuity of operations plan to provide guidelines for study participant and research staff safety. The ORD issued an unprecedented full-stop administrative hold on in-person research activities after the global announcement of the pandemic. This policy provided formal protections for research programs to safeguard staff and research participants and to determine appropriate alternatives to conduct research activities within necessary social distancing, safety, and other clinical care parameters. It also aligned with guidance and requirements that local VAMCs issued for their operations and care priorities.

The Response Team also established a scientific steering committee of VA infectious disease, critical care, informatics, and epidemiology experts to prioritize research questions, identify research opportunities, and evaluate proposals using a modified expeditious scientific review process. This group also minimized duplicate scientific efforts that might be expected from a large pool of investigators simultaneously pursuing similar research questions. Committee recommendations set up a portfolio that included basic science efforts in diagnostics, clinical trials, population studies, and research infrastructure.

 

 

Leveraging Existing Infrastructure

Besides quickly organizing a central touchpoint for the VA COVID-19 research response, the ORD capitalized on its extensive nationwide infrastructure. One key component was the Cooperative Studies Program (CSP); the longstanding VA clinical research enterprise that supports the planning and conduct of large multicenter clinical trials and epidemiological studies. The CSP includes experts at 5 data and statistical coordinating centers, a clinical research pharmacy coordinating center, and 4 epidemiological resource centers.8 CSP studies provide definitive evidence for clinical practice and care of veterans and the nation. CSP’s CONFIRM trial (CSP 577) is the largest VA interventional study with > 50,000 veterans.9 CONFIRM followed the Trial of Varicella Zoster Vaccine for the Prevention of Herpes Zoster and Its Complications (CSP 403), which involved > 38,000 participants to evaluate a vaccine to reduce the burden of illness-associated herpes zoster (shingles). In the study, the vaccine markedly reduced the shingles burden of illness among older adults.10 These studies highlight the CSP cohort development ability as evidenced by the Million Veteran Program.11

VA Research, particularly through the CSP, contributed to multiple federal actions for COVID-19. The CSP had already established partnerships with federal and industry groups in multisite clinical trials and observational studies. During COVID-19, the ORD established a COVID-19 clinical trial master protocol framework: the VA CoronavirUs Research & Efficacy Studies network.9 The CSP also supported studies by the Coronavirus Prevention Network, the National Institute of Allergy and Infectious Disease (NIAID), and the US Food and Drug Administration (FDA). As such, the VA could translate requirements in working with an industry sponsor on the rapid execution of studies within a federal health care system. Much of the success arose when there was either earlier engagement in planning and/or existing familiarity among parties with operational and regulatory requirements.

table 1

Before the pandemic, the ORD had also been working on various external partnerships to increase opportunities for veterans in clinical trial participation, particularly for cancer, which Caroff and colleagues discuss further.12 A newly emerging Partnered Research Program (PRP) offered a strategy for participation in the major COVID-19 vaccine efficacy clinical trials. VA Research, through PRP and CSP, rapidly engaged others and managed critical communication (Table 1). In quickly pivoting to COVID-19 clinical studies, the VA also used the Networks of Dedicated Enrollment Sites (NODES), its site-based, CSP-supported infrastructure of existing investigators and coordinators with clinical, operational, and regulatory proficiency for large trials.13,14 Together, the CSP and PRP solidified the VA’s scientific, operational, and regulatory support basis for working with industry partners and federal agencies to conduct therapeutic and vaccine trials.

Speed, Knowledge, and Safety

The scope of VA Research partnerships covers several goals but can be broadly categorized in the following ways: research aimed at evaluating the efficacy of new treatments; development of infrastructure to facilitate more rapid and innovative approaches to research; and building connections within the health care system to take an enterprise approach to research.

Activities are not limited to COVID-19. The VA partners with federal entities on research primarily through interagency agreements whose authorities are derived from the Economy Act (31 USC § 1535). For industry and nonfederal groups, the VA enters into Cooperative Research and Development Agreements that are rooted in the Federal Technology Transfer Act (15 USC § 3710). Although the VA has experience in each of these processes, COVID-19 prompted many groups, existing partners and new ones, to engage with the VA. Consequently, the ORD needed to quickly understand the complexities of how to handle such engagements on a larger scale. The VA Research enterprise strategy also focused on facilitating these processes.

As part of VA integration goals, ORD leaders engaged VA clinical leaders, especially in Public Health, Preventive Medicine, Pharmacy Benefits Management, and Pathology and Laboratory services. The ORD also worked closely with operational leaders, including those responsible for the Veterans Integrated Service Networks and VAMC chiefs of staff and network chief medical officers. The ORD’s familiarity with coordinating complex activities for research further helped to organize nonresearch responses for clinical needs and resources to support the VA COVID-19 response. The Office of the Under Secretary for Health recognized VA Research’s critical role as part of the VA health care system. In turn, it served as a major champion to drive success among the active research efforts, especially the partnered efforts, responding to COVID-19. Continuously communicating support and offering resources for the agency’s overall COVID-19 response reinforced the positive impact of VA Research that extended beyond its traditional roles. That is, the research component of VHA was highlighted as an integral part of the COVID-19 response along with its clinical operations. This integrated approach was perhaps best demonstrated in a VHA-wide push to start and conduct the national vaccine efficacy trials.

Other COVID-19 research supported by the ORD included participation in the Mayo Clinic–led convalescent plasma expanded access treatment protocol, which had emerged as a potential therapeutic option.15 The ORD provided centralized regulatory support to nearly 100 VAMCs, helping to reduce inconsistencies in protocol approval processes for what was hoped to be a promising treatment for COVID-19.16 This rapid approach to address a real-time treatment option demonstrated the VA Research capability for swift mobilization in an emergency.

The ORD also coordinated with other federal agencies. For example, it collaborated with the US Department of Defense to begin a parallel observational study on COVID-19 infections and potential severe outcomes. The study enrolled > 3000 veterans who are being followed for up to 2 years to better understand the natural history and course of COVID-19.17 Other interagency efforts focused on vaccine and therapeutic trials, including Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) with the National Institutes of Health. In these activities, VA Research helped increase recruitment, particularly of a more diverse patient population, in helping to assess promising treatments.10

table 2

Motivated by its expanding portfolio of COVID-19 intervention studies, the VA also created a COVID-19 research registry for all VA investigators. This registry included almost 59,000 veterans who indicated a willingness to volunteer for clinical studies. This registry exemplified a long-standing tradition of veterans willing to serve their nation again in a time of need. Iaquinto and colleagues showcased how VHA programs (eg, Office of Healthcare Innovation and Learning) collaborated by expediting a study on 3D-printed swabs to address supply chain shortages. The study, which involved the FDA, showed that the printed swabs were as effective as commercially available ones.18 It provided evidence supporting the production and dissemination of a greater number of testing swabs to the public while also reducing the cost and time requirements (Table 2).

Altogether, these collaborative efforts advanced a transformative approach within the VA that was already happening but was accelerated by the pandemic. Such activities enabled greater understanding throughout the VA for how research is not merely complementary but an integrated part of how veterans receive health care. By giving opportunities to veterans to participate in studies, especially clinical studies, the VA created a path in which such expectations, understanding, and operations were more fluid.

 

 

Future Directions

The VA continues to work for veterans by emphasizing its strategic goals and strengths in clinical, data science, and other pioneering activities at an enterprise level to provide the highest quality evidence for care. These capabilities perpetuate a scientific and learning environment that also builds toward the future by giving junior investigators and others opportunities to work within a national health care setting. In turn, this provides a more focused perspective on endeavors that align with the VA mission through ORD-supported career development, merit review (independent investigator submissions), and CSP.19 Preclinical, health services, genomic, and implementation research were given insights into more effective operational and methodological partnerships to help inform the health care system. The pandemic also served to strengthen our ability to mobilize and prepare even faster for emergencies and other potential disease outbreaks, including newer pandemic concerns (eg, mpox, Ebola) from research and public health perspectives.

Conclusions

Throughout its 100-year history, VA Research has been a critical, enduring institution within the national medical landscape. The ability to collaborate with partners has helped us to design and create even better processes, optimize and maximize our infrastructure, and learn more about common research interests that can be even more responsive to national health care needs. As an enterprise, VA Research also aims to continually learn and expand on these valuable lessons gained from internal, interagency, and industry collaborations to effectively meet and exceed our mission to serve our veterans.

Acknowledgments

The authors acknowledge Daphne Swancutt for her contribution as copywriter for this manuscript.

References

1. US Department of Veterans Affairs. Functional organization manual: description of organization, structure, missions, functions, tasks, and authorities. Version 6. 2020. Accessed September 11, 2023. https://www.va.gov/VA-Functional-Organization-Manual-2020-4.pdf

2. Kilbourne AM, Schmidt J, Edmunds M, Vega R, Bowersox N, Atkins D. How the VA is training the next-generation workforce for learning health systems. Learn Health Syst. 2022;6(4):e10333. Published 2022 Aug 16. doi:10.1002/lrh2.10333

3. O’Leary TJ, Dominitz JA, Chang KM. Veterans Affairs office of research and development: research programs and emerging opportunities in digestive diseases research. Gastroenterology. 2015;149(7):1652-1661. doi:10.1053/j.gastro.2015.10.021

4. Tucker WB. The evolution of the cooperative studies in the chemotherapy of tuberculosis of the Veterans Administration and armed forces of the U.S.A. An account of the evolving education of the physician in clinical pharmacology. Bibl Tuberc. 1960;15:1-68.

5. Hays MT; Veterans Health Administration. A historical look at the establishment of the Department of Veterans Affairs research & development program. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

6. US Department of Veterans Affairs, Veterans Health Administration. Coronavirus Disease 2019 (COVID-19) response report – annex a. May 10, 2021. Accessed September 11, 2023. https://www.va.gov/health/docs/VHA-COVID-19-Response-2021.pdf

7. US Department of Veterans Affairs, Veterans Health Administration. ORD Research Response to COVID-19. US Department of Veterans Affairs. Updated March 24, 2020. Accessed September 11, 2023. www.research.va.gov/programs/orppe/education/webinars/orppe-031920.cfm

8. Burnaska DR, Huang GD, O’Leary TJ. Clinical trials proposed for the VA cooperative studies program: success rates and factors impacting approval. Contemp Clin Trials Commun. 2021;23:100811. Published 2021 Jul 9. doi:10.1016/j.conctc.2021.100811

9. US Department of Veterans Affairs. VA CoronavirUs Research & Efficacy Studies (VA CURES). Updated January 6, 2022. Accessed September 11, 2023. https://www.research.va.gov/services/csrd/va_cures/default.cfm

10. Oxman MN, Levin MJ, Johnson GR, et al. A vaccine to prevent herpes zoster and postherpetic neuralgia in older adults. N Engl J Med. 2005;352(22):2271-2284. doi:10.1056/NEJMoa051016

11. Whitbourne SB, Moser J, Cho K, et al. Leveraging the Million Veteran Program infrastructure and data for a rapid research response to COVID-19. Fed Pract. 2023;40(suppl 5):S23-S28. doi:10.12788/fp.0416

12. Caroff K, Davey V, Smyth M, et al. VA lessons from partnering in COVID-19 clinical trials. Fed Pract. 2023;40(suppl 5): S18-S22. doi:10.12788/fp.0415

13. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ network of dedicated enrollment sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

14. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

15. Joyner M. Expanded access to convalescent plasma for the treatment of patients with COVID-19. ClinicalTrials.gov identifier: NCT04338360. April 8, 2020. Updated September 2, 2020. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT04338360

16. Joyner MJ, Wright RS, Fairweather D, et al. Early safety indicators of COVID-19 convalescent plasma in 5000 patients. J Clin Invest. 2020;130(9):4791-4797. doi:10.1172/JCI140200

17. Lee JS, Smith NL. Epidemiology, immunology and clinical characteristics of COVID-19 (EPIC3). ClinicalTrials.gov identifier: NCT05764083. March 10, 2023. Updated August 1, 2023. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT05764083

18. Iaquinto J, Ripley B, Dorn PA. How VA innovative partnerships and health care system can respond to national needs: NOSE trial example. Fed Pract. 2023;40(suppl 5):S52-S56. doi:10.12788/fp.0418

19. US Department of Veterans Affairs. Health Services Research & Development research career development program. Updated March 4, 2021. Accessed September 11, 2023. https://hsrd.research.va.gov/cdp/

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The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

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Author disclosures

The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.

Ethics and consent

Not applicable.

Author and Disclosure Information

Amanda P. Garcia, MPHa; Grant D. Huang, MPH, PhDa; Louise Arnheim, MPAb; Rachel B. Ramoni, DMD, ScDa; Carolyn M. Clancy, MDb

Correspondence:  Amanda Garcia  (amanda.garcia@va.gov)

Author affiliations

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bOffice of Discovery, Education and Affiliate Networks, Department of Veterans Affairs, Washington, DC

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The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article.

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The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the US Government, or any of its agencies.

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The US Department of Veterans Affairs (VA) plays a substantial role in the nation’s public health through the Veterans Health Administration (VHA). Its statutory missions of teaching, clinical care, and research enable it to serve a foundational role in the US biomedical enterprise.1 Throughout its extensive network of VA medical centers (VAMCs) and partnering academic affiliates, thousands of clinicians and researchers have been trained to improve the lives of veterans and benefit the lives of all Americans. In supporting the largest US integrated health care system, the VA also has numerous capabilities and resources that distinctively position it to produce scientific and clinical results specifically within the context of providing care. The VA has formed partnerships with other federal agencies, industry, and nonprofit entities. Its ability to be a nexus of health care and practice, scientific discovery, and innovative ways to integrate shared interests in these areas have led to many transformative endeavors that save lives and improve the quality of care for veterans and the public.

The COVID-19 pandemic triggered another mission: service in times of national emergency. Known as the Fourth Mission, the VA rapidly shifted to highlight how its health care and research enterprises could apply strengths in a unique, coordinated manner. While the Fourth Mission is typically considered in the context of clinical care, the VA’s movement toward greater integration facilitated the role of research as a key component in efforts under a learning health care model.2

VA Office of Research and Development

Within the VHA, the Office of Research and Development (ORD) develops research policy and oversees interdisciplinary efforts focused on generating evidence to improve veteran health.3 These activities span at least 100 of 171 VAMCs and include thousands of investigators and staff across all major health research disciplines. Many of these investigators are also clinicians who provide patient care and are experts in the prevention, diagnosis, and treatment of diseases and disorders affecting veterans.

The ORD has invested in a range of scientific, operational, regulatory, and technological assets and infrastructure as part of its enterprise. These strengths come from a nearly 100-year history originating as part of a set of hospital-based medical studies. This established the model for a culture of cooperative research within the VA and with external groups who benefit from the VA’s foundational role in multisite clinical trials.2,4,5 Today, the VA prioritizes bench-to-bedside research covering a broad spectrum of investigations, which are integrated with clinical operations and systems that deliver care.3 The VA supports an extensive range of work that covers core areas in preclinical and clinical studies to health services research, rehabilitation and implementation science, establishing expertise in genomic and data sciences, and more recent activities in artificial intelligence.

In 2017, the ORD began a focused strategy to transform into a national enterprise that capitalized on its place within the VA and its particular ability to translate and implement scientific findings into real impact for veteran health and care through 5 initiatives: (1) enhancing veteran access to high-quality clinical trials; (2) increasing the substantial real-world impact of VA Research; (3) putting VA data to work for veteran health; (4) promoting diversity, equity, and inclusion within our sphere of influence; and (5) building community through research. These activities are interrelated and, where possible, the ORD works with other VA clinical and operational offices to accomplish multiple goals and coordinate within the health care system. As such, the VA continually seeks to increase efficiencies and improve abilities that provide veterans with best-in-class health care. While still in its early stages, this strategy and its initiatives established a path for the ORD response to the pandemic.

Within 2 weeks of the World Health Organization and the US declaring a COVID-19 pandemic, the ORD began to address the developing needs and challenges of the yet unknown emerging public health threat. This included outreach to and contact from federal, academic, and industry partners. At the same time, the ORD maintained its focus and energy to support its ongoing veteran-centric research portfolio and VHA health care system needs across its broad scope of activities.

This article discusses how the pandemic accelerated the VA’s research enterprise strategy and enacted a response, highlighting the advantages and strengths of this direction. We demonstrate how this evolving strategy enabled the VA to quickly leverage partnerships during a health emergency. While the ORD and VA Research have been used interchangeably, we will attempt to distinguish between the office that serves as headquarters for the national enterprise—the ORD—and the components of that enterprise composed of scientific personnel, equipment, operational units, and partners—VA Research. Finally, we present lessons from this experience toward a broader, post–COVID-19, enterprise-wide approach that the VA has for providing evidence-based care. These experiences may enrich our understanding of postpandemic future research opportunities with the VA as a leader and partner who leverages its commitment to veterans to improve the nation’s health.

 

 

ORGANIZING THE VA COVID-19 RESEARCH RESPONSE

VA Research seeks to internally standardize and integrate collaborations with clinical and operational partners throughout the agency. When possible, it seeks to streamline partnership efforts involving external groups less familiar with how the VA operates or its policies, as well as its capabilities. This need was more obvious during the pandemic, and the ORD assembled its COVID-19 response quickly.6

figure

In early January 2020, VA offices, including the ORD, were carefully observing COVID-19. On March 4, 2020, a week before the World Health Organization declared COVID-19 a pandemic, the ORD and its National Research Advisory Council arranged a briefing from VA public health leaders to deal with reported cases of COVID-19 and VA plans. Immediately afterward, the ORD Chief Research and Development Officer gathered a team of experts in clinical research, infectious disease, and public health to strategize a broader research enterprise approach to the pandemic. This group quickly framed 3 key targets: (1) identify critical research questions to prioritize; (2) provide operational guidance to the research community; and (3) uphold VA research staff safety. This discussion led to the creation of a larger ORD COVID-19 Research Response Team that managed activities within this scope. This team included other ORD leaders and staff with operational, scientific, and regulatory expertise charged with enterprise-level planning and execution for all research activities addressing or affected by the pandemic (Figure).

Effective and timely communication was chief among key ORD responsibilities. On March 19, 2020, the Response Team informed the VA Research community about ORD plans for organizing the VA COVID-19 research response.7 It also mobilized VA research programs and investigators to support an enterprise approach that would be coordinated centrally. We achieved communication goals by developing a dedicated website, which provided a means to distribute up-to-date notices and guidance, answer frequently asked questions, and alert investigators about research opportunities. The site enabled the field to report on its efforts, which enhanced leadership and community awareness. A working group of ORD and field personnel managed communications. Given the volume of existing non–COVID-19 research, we established a research continuity of operations plan to provide guidelines for study participant and research staff safety. The ORD issued an unprecedented full-stop administrative hold on in-person research activities after the global announcement of the pandemic. This policy provided formal protections for research programs to safeguard staff and research participants and to determine appropriate alternatives to conduct research activities within necessary social distancing, safety, and other clinical care parameters. It also aligned with guidance and requirements that local VAMCs issued for their operations and care priorities.

The Response Team also established a scientific steering committee of VA infectious disease, critical care, informatics, and epidemiology experts to prioritize research questions, identify research opportunities, and evaluate proposals using a modified expeditious scientific review process. This group also minimized duplicate scientific efforts that might be expected from a large pool of investigators simultaneously pursuing similar research questions. Committee recommendations set up a portfolio that included basic science efforts in diagnostics, clinical trials, population studies, and research infrastructure.

 

 

Leveraging Existing Infrastructure

Besides quickly organizing a central touchpoint for the VA COVID-19 research response, the ORD capitalized on its extensive nationwide infrastructure. One key component was the Cooperative Studies Program (CSP); the longstanding VA clinical research enterprise that supports the planning and conduct of large multicenter clinical trials and epidemiological studies. The CSP includes experts at 5 data and statistical coordinating centers, a clinical research pharmacy coordinating center, and 4 epidemiological resource centers.8 CSP studies provide definitive evidence for clinical practice and care of veterans and the nation. CSP’s CONFIRM trial (CSP 577) is the largest VA interventional study with > 50,000 veterans.9 CONFIRM followed the Trial of Varicella Zoster Vaccine for the Prevention of Herpes Zoster and Its Complications (CSP 403), which involved > 38,000 participants to evaluate a vaccine to reduce the burden of illness-associated herpes zoster (shingles). In the study, the vaccine markedly reduced the shingles burden of illness among older adults.10 These studies highlight the CSP cohort development ability as evidenced by the Million Veteran Program.11

VA Research, particularly through the CSP, contributed to multiple federal actions for COVID-19. The CSP had already established partnerships with federal and industry groups in multisite clinical trials and observational studies. During COVID-19, the ORD established a COVID-19 clinical trial master protocol framework: the VA CoronavirUs Research & Efficacy Studies network.9 The CSP also supported studies by the Coronavirus Prevention Network, the National Institute of Allergy and Infectious Disease (NIAID), and the US Food and Drug Administration (FDA). As such, the VA could translate requirements in working with an industry sponsor on the rapid execution of studies within a federal health care system. Much of the success arose when there was either earlier engagement in planning and/or existing familiarity among parties with operational and regulatory requirements.

table 1

Before the pandemic, the ORD had also been working on various external partnerships to increase opportunities for veterans in clinical trial participation, particularly for cancer, which Caroff and colleagues discuss further.12 A newly emerging Partnered Research Program (PRP) offered a strategy for participation in the major COVID-19 vaccine efficacy clinical trials. VA Research, through PRP and CSP, rapidly engaged others and managed critical communication (Table 1). In quickly pivoting to COVID-19 clinical studies, the VA also used the Networks of Dedicated Enrollment Sites (NODES), its site-based, CSP-supported infrastructure of existing investigators and coordinators with clinical, operational, and regulatory proficiency for large trials.13,14 Together, the CSP and PRP solidified the VA’s scientific, operational, and regulatory support basis for working with industry partners and federal agencies to conduct therapeutic and vaccine trials.

Speed, Knowledge, and Safety

The scope of VA Research partnerships covers several goals but can be broadly categorized in the following ways: research aimed at evaluating the efficacy of new treatments; development of infrastructure to facilitate more rapid and innovative approaches to research; and building connections within the health care system to take an enterprise approach to research.

Activities are not limited to COVID-19. The VA partners with federal entities on research primarily through interagency agreements whose authorities are derived from the Economy Act (31 USC § 1535). For industry and nonfederal groups, the VA enters into Cooperative Research and Development Agreements that are rooted in the Federal Technology Transfer Act (15 USC § 3710). Although the VA has experience in each of these processes, COVID-19 prompted many groups, existing partners and new ones, to engage with the VA. Consequently, the ORD needed to quickly understand the complexities of how to handle such engagements on a larger scale. The VA Research enterprise strategy also focused on facilitating these processes.

As part of VA integration goals, ORD leaders engaged VA clinical leaders, especially in Public Health, Preventive Medicine, Pharmacy Benefits Management, and Pathology and Laboratory services. The ORD also worked closely with operational leaders, including those responsible for the Veterans Integrated Service Networks and VAMC chiefs of staff and network chief medical officers. The ORD’s familiarity with coordinating complex activities for research further helped to organize nonresearch responses for clinical needs and resources to support the VA COVID-19 response. The Office of the Under Secretary for Health recognized VA Research’s critical role as part of the VA health care system. In turn, it served as a major champion to drive success among the active research efforts, especially the partnered efforts, responding to COVID-19. Continuously communicating support and offering resources for the agency’s overall COVID-19 response reinforced the positive impact of VA Research that extended beyond its traditional roles. That is, the research component of VHA was highlighted as an integral part of the COVID-19 response along with its clinical operations. This integrated approach was perhaps best demonstrated in a VHA-wide push to start and conduct the national vaccine efficacy trials.

Other COVID-19 research supported by the ORD included participation in the Mayo Clinic–led convalescent plasma expanded access treatment protocol, which had emerged as a potential therapeutic option.15 The ORD provided centralized regulatory support to nearly 100 VAMCs, helping to reduce inconsistencies in protocol approval processes for what was hoped to be a promising treatment for COVID-19.16 This rapid approach to address a real-time treatment option demonstrated the VA Research capability for swift mobilization in an emergency.

The ORD also coordinated with other federal agencies. For example, it collaborated with the US Department of Defense to begin a parallel observational study on COVID-19 infections and potential severe outcomes. The study enrolled > 3000 veterans who are being followed for up to 2 years to better understand the natural history and course of COVID-19.17 Other interagency efforts focused on vaccine and therapeutic trials, including Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) with the National Institutes of Health. In these activities, VA Research helped increase recruitment, particularly of a more diverse patient population, in helping to assess promising treatments.10

table 2

Motivated by its expanding portfolio of COVID-19 intervention studies, the VA also created a COVID-19 research registry for all VA investigators. This registry included almost 59,000 veterans who indicated a willingness to volunteer for clinical studies. This registry exemplified a long-standing tradition of veterans willing to serve their nation again in a time of need. Iaquinto and colleagues showcased how VHA programs (eg, Office of Healthcare Innovation and Learning) collaborated by expediting a study on 3D-printed swabs to address supply chain shortages. The study, which involved the FDA, showed that the printed swabs were as effective as commercially available ones.18 It provided evidence supporting the production and dissemination of a greater number of testing swabs to the public while also reducing the cost and time requirements (Table 2).

Altogether, these collaborative efforts advanced a transformative approach within the VA that was already happening but was accelerated by the pandemic. Such activities enabled greater understanding throughout the VA for how research is not merely complementary but an integrated part of how veterans receive health care. By giving opportunities to veterans to participate in studies, especially clinical studies, the VA created a path in which such expectations, understanding, and operations were more fluid.

 

 

Future Directions

The VA continues to work for veterans by emphasizing its strategic goals and strengths in clinical, data science, and other pioneering activities at an enterprise level to provide the highest quality evidence for care. These capabilities perpetuate a scientific and learning environment that also builds toward the future by giving junior investigators and others opportunities to work within a national health care setting. In turn, this provides a more focused perspective on endeavors that align with the VA mission through ORD-supported career development, merit review (independent investigator submissions), and CSP.19 Preclinical, health services, genomic, and implementation research were given insights into more effective operational and methodological partnerships to help inform the health care system. The pandemic also served to strengthen our ability to mobilize and prepare even faster for emergencies and other potential disease outbreaks, including newer pandemic concerns (eg, mpox, Ebola) from research and public health perspectives.

Conclusions

Throughout its 100-year history, VA Research has been a critical, enduring institution within the national medical landscape. The ability to collaborate with partners has helped us to design and create even better processes, optimize and maximize our infrastructure, and learn more about common research interests that can be even more responsive to national health care needs. As an enterprise, VA Research also aims to continually learn and expand on these valuable lessons gained from internal, interagency, and industry collaborations to effectively meet and exceed our mission to serve our veterans.

Acknowledgments

The authors acknowledge Daphne Swancutt for her contribution as copywriter for this manuscript.

The US Department of Veterans Affairs (VA) plays a substantial role in the nation’s public health through the Veterans Health Administration (VHA). Its statutory missions of teaching, clinical care, and research enable it to serve a foundational role in the US biomedical enterprise.1 Throughout its extensive network of VA medical centers (VAMCs) and partnering academic affiliates, thousands of clinicians and researchers have been trained to improve the lives of veterans and benefit the lives of all Americans. In supporting the largest US integrated health care system, the VA also has numerous capabilities and resources that distinctively position it to produce scientific and clinical results specifically within the context of providing care. The VA has formed partnerships with other federal agencies, industry, and nonprofit entities. Its ability to be a nexus of health care and practice, scientific discovery, and innovative ways to integrate shared interests in these areas have led to many transformative endeavors that save lives and improve the quality of care for veterans and the public.

The COVID-19 pandemic triggered another mission: service in times of national emergency. Known as the Fourth Mission, the VA rapidly shifted to highlight how its health care and research enterprises could apply strengths in a unique, coordinated manner. While the Fourth Mission is typically considered in the context of clinical care, the VA’s movement toward greater integration facilitated the role of research as a key component in efforts under a learning health care model.2

VA Office of Research and Development

Within the VHA, the Office of Research and Development (ORD) develops research policy and oversees interdisciplinary efforts focused on generating evidence to improve veteran health.3 These activities span at least 100 of 171 VAMCs and include thousands of investigators and staff across all major health research disciplines. Many of these investigators are also clinicians who provide patient care and are experts in the prevention, diagnosis, and treatment of diseases and disorders affecting veterans.

The ORD has invested in a range of scientific, operational, regulatory, and technological assets and infrastructure as part of its enterprise. These strengths come from a nearly 100-year history originating as part of a set of hospital-based medical studies. This established the model for a culture of cooperative research within the VA and with external groups who benefit from the VA’s foundational role in multisite clinical trials.2,4,5 Today, the VA prioritizes bench-to-bedside research covering a broad spectrum of investigations, which are integrated with clinical operations and systems that deliver care.3 The VA supports an extensive range of work that covers core areas in preclinical and clinical studies to health services research, rehabilitation and implementation science, establishing expertise in genomic and data sciences, and more recent activities in artificial intelligence.

In 2017, the ORD began a focused strategy to transform into a national enterprise that capitalized on its place within the VA and its particular ability to translate and implement scientific findings into real impact for veteran health and care through 5 initiatives: (1) enhancing veteran access to high-quality clinical trials; (2) increasing the substantial real-world impact of VA Research; (3) putting VA data to work for veteran health; (4) promoting diversity, equity, and inclusion within our sphere of influence; and (5) building community through research. These activities are interrelated and, where possible, the ORD works with other VA clinical and operational offices to accomplish multiple goals and coordinate within the health care system. As such, the VA continually seeks to increase efficiencies and improve abilities that provide veterans with best-in-class health care. While still in its early stages, this strategy and its initiatives established a path for the ORD response to the pandemic.

Within 2 weeks of the World Health Organization and the US declaring a COVID-19 pandemic, the ORD began to address the developing needs and challenges of the yet unknown emerging public health threat. This included outreach to and contact from federal, academic, and industry partners. At the same time, the ORD maintained its focus and energy to support its ongoing veteran-centric research portfolio and VHA health care system needs across its broad scope of activities.

This article discusses how the pandemic accelerated the VA’s research enterprise strategy and enacted a response, highlighting the advantages and strengths of this direction. We demonstrate how this evolving strategy enabled the VA to quickly leverage partnerships during a health emergency. While the ORD and VA Research have been used interchangeably, we will attempt to distinguish between the office that serves as headquarters for the national enterprise—the ORD—and the components of that enterprise composed of scientific personnel, equipment, operational units, and partners—VA Research. Finally, we present lessons from this experience toward a broader, post–COVID-19, enterprise-wide approach that the VA has for providing evidence-based care. These experiences may enrich our understanding of postpandemic future research opportunities with the VA as a leader and partner who leverages its commitment to veterans to improve the nation’s health.

 

 

ORGANIZING THE VA COVID-19 RESEARCH RESPONSE

VA Research seeks to internally standardize and integrate collaborations with clinical and operational partners throughout the agency. When possible, it seeks to streamline partnership efforts involving external groups less familiar with how the VA operates or its policies, as well as its capabilities. This need was more obvious during the pandemic, and the ORD assembled its COVID-19 response quickly.6

figure

In early January 2020, VA offices, including the ORD, were carefully observing COVID-19. On March 4, 2020, a week before the World Health Organization declared COVID-19 a pandemic, the ORD and its National Research Advisory Council arranged a briefing from VA public health leaders to deal with reported cases of COVID-19 and VA plans. Immediately afterward, the ORD Chief Research and Development Officer gathered a team of experts in clinical research, infectious disease, and public health to strategize a broader research enterprise approach to the pandemic. This group quickly framed 3 key targets: (1) identify critical research questions to prioritize; (2) provide operational guidance to the research community; and (3) uphold VA research staff safety. This discussion led to the creation of a larger ORD COVID-19 Research Response Team that managed activities within this scope. This team included other ORD leaders and staff with operational, scientific, and regulatory expertise charged with enterprise-level planning and execution for all research activities addressing or affected by the pandemic (Figure).

Effective and timely communication was chief among key ORD responsibilities. On March 19, 2020, the Response Team informed the VA Research community about ORD plans for organizing the VA COVID-19 research response.7 It also mobilized VA research programs and investigators to support an enterprise approach that would be coordinated centrally. We achieved communication goals by developing a dedicated website, which provided a means to distribute up-to-date notices and guidance, answer frequently asked questions, and alert investigators about research opportunities. The site enabled the field to report on its efforts, which enhanced leadership and community awareness. A working group of ORD and field personnel managed communications. Given the volume of existing non–COVID-19 research, we established a research continuity of operations plan to provide guidelines for study participant and research staff safety. The ORD issued an unprecedented full-stop administrative hold on in-person research activities after the global announcement of the pandemic. This policy provided formal protections for research programs to safeguard staff and research participants and to determine appropriate alternatives to conduct research activities within necessary social distancing, safety, and other clinical care parameters. It also aligned with guidance and requirements that local VAMCs issued for their operations and care priorities.

The Response Team also established a scientific steering committee of VA infectious disease, critical care, informatics, and epidemiology experts to prioritize research questions, identify research opportunities, and evaluate proposals using a modified expeditious scientific review process. This group also minimized duplicate scientific efforts that might be expected from a large pool of investigators simultaneously pursuing similar research questions. Committee recommendations set up a portfolio that included basic science efforts in diagnostics, clinical trials, population studies, and research infrastructure.

 

 

Leveraging Existing Infrastructure

Besides quickly organizing a central touchpoint for the VA COVID-19 research response, the ORD capitalized on its extensive nationwide infrastructure. One key component was the Cooperative Studies Program (CSP); the longstanding VA clinical research enterprise that supports the planning and conduct of large multicenter clinical trials and epidemiological studies. The CSP includes experts at 5 data and statistical coordinating centers, a clinical research pharmacy coordinating center, and 4 epidemiological resource centers.8 CSP studies provide definitive evidence for clinical practice and care of veterans and the nation. CSP’s CONFIRM trial (CSP 577) is the largest VA interventional study with > 50,000 veterans.9 CONFIRM followed the Trial of Varicella Zoster Vaccine for the Prevention of Herpes Zoster and Its Complications (CSP 403), which involved > 38,000 participants to evaluate a vaccine to reduce the burden of illness-associated herpes zoster (shingles). In the study, the vaccine markedly reduced the shingles burden of illness among older adults.10 These studies highlight the CSP cohort development ability as evidenced by the Million Veteran Program.11

VA Research, particularly through the CSP, contributed to multiple federal actions for COVID-19. The CSP had already established partnerships with federal and industry groups in multisite clinical trials and observational studies. During COVID-19, the ORD established a COVID-19 clinical trial master protocol framework: the VA CoronavirUs Research & Efficacy Studies network.9 The CSP also supported studies by the Coronavirus Prevention Network, the National Institute of Allergy and Infectious Disease (NIAID), and the US Food and Drug Administration (FDA). As such, the VA could translate requirements in working with an industry sponsor on the rapid execution of studies within a federal health care system. Much of the success arose when there was either earlier engagement in planning and/or existing familiarity among parties with operational and regulatory requirements.

table 1

Before the pandemic, the ORD had also been working on various external partnerships to increase opportunities for veterans in clinical trial participation, particularly for cancer, which Caroff and colleagues discuss further.12 A newly emerging Partnered Research Program (PRP) offered a strategy for participation in the major COVID-19 vaccine efficacy clinical trials. VA Research, through PRP and CSP, rapidly engaged others and managed critical communication (Table 1). In quickly pivoting to COVID-19 clinical studies, the VA also used the Networks of Dedicated Enrollment Sites (NODES), its site-based, CSP-supported infrastructure of existing investigators and coordinators with clinical, operational, and regulatory proficiency for large trials.13,14 Together, the CSP and PRP solidified the VA’s scientific, operational, and regulatory support basis for working with industry partners and federal agencies to conduct therapeutic and vaccine trials.

Speed, Knowledge, and Safety

The scope of VA Research partnerships covers several goals but can be broadly categorized in the following ways: research aimed at evaluating the efficacy of new treatments; development of infrastructure to facilitate more rapid and innovative approaches to research; and building connections within the health care system to take an enterprise approach to research.

Activities are not limited to COVID-19. The VA partners with federal entities on research primarily through interagency agreements whose authorities are derived from the Economy Act (31 USC § 1535). For industry and nonfederal groups, the VA enters into Cooperative Research and Development Agreements that are rooted in the Federal Technology Transfer Act (15 USC § 3710). Although the VA has experience in each of these processes, COVID-19 prompted many groups, existing partners and new ones, to engage with the VA. Consequently, the ORD needed to quickly understand the complexities of how to handle such engagements on a larger scale. The VA Research enterprise strategy also focused on facilitating these processes.

As part of VA integration goals, ORD leaders engaged VA clinical leaders, especially in Public Health, Preventive Medicine, Pharmacy Benefits Management, and Pathology and Laboratory services. The ORD also worked closely with operational leaders, including those responsible for the Veterans Integrated Service Networks and VAMC chiefs of staff and network chief medical officers. The ORD’s familiarity with coordinating complex activities for research further helped to organize nonresearch responses for clinical needs and resources to support the VA COVID-19 response. The Office of the Under Secretary for Health recognized VA Research’s critical role as part of the VA health care system. In turn, it served as a major champion to drive success among the active research efforts, especially the partnered efforts, responding to COVID-19. Continuously communicating support and offering resources for the agency’s overall COVID-19 response reinforced the positive impact of VA Research that extended beyond its traditional roles. That is, the research component of VHA was highlighted as an integral part of the COVID-19 response along with its clinical operations. This integrated approach was perhaps best demonstrated in a VHA-wide push to start and conduct the national vaccine efficacy trials.

Other COVID-19 research supported by the ORD included participation in the Mayo Clinic–led convalescent plasma expanded access treatment protocol, which had emerged as a potential therapeutic option.15 The ORD provided centralized regulatory support to nearly 100 VAMCs, helping to reduce inconsistencies in protocol approval processes for what was hoped to be a promising treatment for COVID-19.16 This rapid approach to address a real-time treatment option demonstrated the VA Research capability for swift mobilization in an emergency.

The ORD also coordinated with other federal agencies. For example, it collaborated with the US Department of Defense to begin a parallel observational study on COVID-19 infections and potential severe outcomes. The study enrolled > 3000 veterans who are being followed for up to 2 years to better understand the natural history and course of COVID-19.17 Other interagency efforts focused on vaccine and therapeutic trials, including Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) with the National Institutes of Health. In these activities, VA Research helped increase recruitment, particularly of a more diverse patient population, in helping to assess promising treatments.10

table 2

Motivated by its expanding portfolio of COVID-19 intervention studies, the VA also created a COVID-19 research registry for all VA investigators. This registry included almost 59,000 veterans who indicated a willingness to volunteer for clinical studies. This registry exemplified a long-standing tradition of veterans willing to serve their nation again in a time of need. Iaquinto and colleagues showcased how VHA programs (eg, Office of Healthcare Innovation and Learning) collaborated by expediting a study on 3D-printed swabs to address supply chain shortages. The study, which involved the FDA, showed that the printed swabs were as effective as commercially available ones.18 It provided evidence supporting the production and dissemination of a greater number of testing swabs to the public while also reducing the cost and time requirements (Table 2).

Altogether, these collaborative efforts advanced a transformative approach within the VA that was already happening but was accelerated by the pandemic. Such activities enabled greater understanding throughout the VA for how research is not merely complementary but an integrated part of how veterans receive health care. By giving opportunities to veterans to participate in studies, especially clinical studies, the VA created a path in which such expectations, understanding, and operations were more fluid.

 

 

Future Directions

The VA continues to work for veterans by emphasizing its strategic goals and strengths in clinical, data science, and other pioneering activities at an enterprise level to provide the highest quality evidence for care. These capabilities perpetuate a scientific and learning environment that also builds toward the future by giving junior investigators and others opportunities to work within a national health care setting. In turn, this provides a more focused perspective on endeavors that align with the VA mission through ORD-supported career development, merit review (independent investigator submissions), and CSP.19 Preclinical, health services, genomic, and implementation research were given insights into more effective operational and methodological partnerships to help inform the health care system. The pandemic also served to strengthen our ability to mobilize and prepare even faster for emergencies and other potential disease outbreaks, including newer pandemic concerns (eg, mpox, Ebola) from research and public health perspectives.

Conclusions

Throughout its 100-year history, VA Research has been a critical, enduring institution within the national medical landscape. The ability to collaborate with partners has helped us to design and create even better processes, optimize and maximize our infrastructure, and learn more about common research interests that can be even more responsive to national health care needs. As an enterprise, VA Research also aims to continually learn and expand on these valuable lessons gained from internal, interagency, and industry collaborations to effectively meet and exceed our mission to serve our veterans.

Acknowledgments

The authors acknowledge Daphne Swancutt for her contribution as copywriter for this manuscript.

References

1. US Department of Veterans Affairs. Functional organization manual: description of organization, structure, missions, functions, tasks, and authorities. Version 6. 2020. Accessed September 11, 2023. https://www.va.gov/VA-Functional-Organization-Manual-2020-4.pdf

2. Kilbourne AM, Schmidt J, Edmunds M, Vega R, Bowersox N, Atkins D. How the VA is training the next-generation workforce for learning health systems. Learn Health Syst. 2022;6(4):e10333. Published 2022 Aug 16. doi:10.1002/lrh2.10333

3. O’Leary TJ, Dominitz JA, Chang KM. Veterans Affairs office of research and development: research programs and emerging opportunities in digestive diseases research. Gastroenterology. 2015;149(7):1652-1661. doi:10.1053/j.gastro.2015.10.021

4. Tucker WB. The evolution of the cooperative studies in the chemotherapy of tuberculosis of the Veterans Administration and armed forces of the U.S.A. An account of the evolving education of the physician in clinical pharmacology. Bibl Tuberc. 1960;15:1-68.

5. Hays MT; Veterans Health Administration. A historical look at the establishment of the Department of Veterans Affairs research & development program. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

6. US Department of Veterans Affairs, Veterans Health Administration. Coronavirus Disease 2019 (COVID-19) response report – annex a. May 10, 2021. Accessed September 11, 2023. https://www.va.gov/health/docs/VHA-COVID-19-Response-2021.pdf

7. US Department of Veterans Affairs, Veterans Health Administration. ORD Research Response to COVID-19. US Department of Veterans Affairs. Updated March 24, 2020. Accessed September 11, 2023. www.research.va.gov/programs/orppe/education/webinars/orppe-031920.cfm

8. Burnaska DR, Huang GD, O’Leary TJ. Clinical trials proposed for the VA cooperative studies program: success rates and factors impacting approval. Contemp Clin Trials Commun. 2021;23:100811. Published 2021 Jul 9. doi:10.1016/j.conctc.2021.100811

9. US Department of Veterans Affairs. VA CoronavirUs Research & Efficacy Studies (VA CURES). Updated January 6, 2022. Accessed September 11, 2023. https://www.research.va.gov/services/csrd/va_cures/default.cfm

10. Oxman MN, Levin MJ, Johnson GR, et al. A vaccine to prevent herpes zoster and postherpetic neuralgia in older adults. N Engl J Med. 2005;352(22):2271-2284. doi:10.1056/NEJMoa051016

11. Whitbourne SB, Moser J, Cho K, et al. Leveraging the Million Veteran Program infrastructure and data for a rapid research response to COVID-19. Fed Pract. 2023;40(suppl 5):S23-S28. doi:10.12788/fp.0416

12. Caroff K, Davey V, Smyth M, et al. VA lessons from partnering in COVID-19 clinical trials. Fed Pract. 2023;40(suppl 5): S18-S22. doi:10.12788/fp.0415

13. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ network of dedicated enrollment sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

14. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

15. Joyner M. Expanded access to convalescent plasma for the treatment of patients with COVID-19. ClinicalTrials.gov identifier: NCT04338360. April 8, 2020. Updated September 2, 2020. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT04338360

16. Joyner MJ, Wright RS, Fairweather D, et al. Early safety indicators of COVID-19 convalescent plasma in 5000 patients. J Clin Invest. 2020;130(9):4791-4797. doi:10.1172/JCI140200

17. Lee JS, Smith NL. Epidemiology, immunology and clinical characteristics of COVID-19 (EPIC3). ClinicalTrials.gov identifier: NCT05764083. March 10, 2023. Updated August 1, 2023. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT05764083

18. Iaquinto J, Ripley B, Dorn PA. How VA innovative partnerships and health care system can respond to national needs: NOSE trial example. Fed Pract. 2023;40(suppl 5):S52-S56. doi:10.12788/fp.0418

19. US Department of Veterans Affairs. Health Services Research & Development research career development program. Updated March 4, 2021. Accessed September 11, 2023. https://hsrd.research.va.gov/cdp/

References

1. US Department of Veterans Affairs. Functional organization manual: description of organization, structure, missions, functions, tasks, and authorities. Version 6. 2020. Accessed September 11, 2023. https://www.va.gov/VA-Functional-Organization-Manual-2020-4.pdf

2. Kilbourne AM, Schmidt J, Edmunds M, Vega R, Bowersox N, Atkins D. How the VA is training the next-generation workforce for learning health systems. Learn Health Syst. 2022;6(4):e10333. Published 2022 Aug 16. doi:10.1002/lrh2.10333

3. O’Leary TJ, Dominitz JA, Chang KM. Veterans Affairs office of research and development: research programs and emerging opportunities in digestive diseases research. Gastroenterology. 2015;149(7):1652-1661. doi:10.1053/j.gastro.2015.10.021

4. Tucker WB. The evolution of the cooperative studies in the chemotherapy of tuberculosis of the Veterans Administration and armed forces of the U.S.A. An account of the evolving education of the physician in clinical pharmacology. Bibl Tuberc. 1960;15:1-68.

5. Hays MT; Veterans Health Administration. A historical look at the establishment of the Department of Veterans Affairs research & development program. https://www.research.va.gov/pubs/docs/ORD-85yrHistory.pdf

6. US Department of Veterans Affairs, Veterans Health Administration. Coronavirus Disease 2019 (COVID-19) response report – annex a. May 10, 2021. Accessed September 11, 2023. https://www.va.gov/health/docs/VHA-COVID-19-Response-2021.pdf

7. US Department of Veterans Affairs, Veterans Health Administration. ORD Research Response to COVID-19. US Department of Veterans Affairs. Updated March 24, 2020. Accessed September 11, 2023. www.research.va.gov/programs/orppe/education/webinars/orppe-031920.cfm

8. Burnaska DR, Huang GD, O’Leary TJ. Clinical trials proposed for the VA cooperative studies program: success rates and factors impacting approval. Contemp Clin Trials Commun. 2021;23:100811. Published 2021 Jul 9. doi:10.1016/j.conctc.2021.100811

9. US Department of Veterans Affairs. VA CoronavirUs Research & Efficacy Studies (VA CURES). Updated January 6, 2022. Accessed September 11, 2023. https://www.research.va.gov/services/csrd/va_cures/default.cfm

10. Oxman MN, Levin MJ, Johnson GR, et al. A vaccine to prevent herpes zoster and postherpetic neuralgia in older adults. N Engl J Med. 2005;352(22):2271-2284. doi:10.1056/NEJMoa051016

11. Whitbourne SB, Moser J, Cho K, et al. Leveraging the Million Veteran Program infrastructure and data for a rapid research response to COVID-19. Fed Pract. 2023;40(suppl 5):S23-S28. doi:10.12788/fp.0416

12. Caroff K, Davey V, Smyth M, et al. VA lessons from partnering in COVID-19 clinical trials. Fed Pract. 2023;40(suppl 5): S18-S22. doi:10.12788/fp.0415

13. Condon DL, Beck D, Kenworthy-Heinige T, et al. A cross-cutting approach to enhancing clinical trial site success: the Department of Veterans Affairs’ network of dedicated enrollment sites (NODES) model. Contemp Clin Trials Commun. 2017;6:78-84. Published 2017 Mar 29. doi:10.1016/j.conctc.2017.03.006

14. McClure J, Asghar A, Krajec A, et al. Clinical trial facilitators: a novel approach to support the execution of clinical research at the study site level. Contemp Clin Trials Commun. 2023;33:101106. doi:10.1016/j.conctc.2023.101106

15. Joyner M. Expanded access to convalescent plasma for the treatment of patients with COVID-19. ClinicalTrials.gov identifier: NCT04338360. April 8, 2020. Updated September 2, 2020. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT04338360

16. Joyner MJ, Wright RS, Fairweather D, et al. Early safety indicators of COVID-19 convalescent plasma in 5000 patients. J Clin Invest. 2020;130(9):4791-4797. doi:10.1172/JCI140200

17. Lee JS, Smith NL. Epidemiology, immunology and clinical characteristics of COVID-19 (EPIC3). ClinicalTrials.gov identifier: NCT05764083. March 10, 2023. Updated August 1, 2023. Accessed September 11, 2023. https://clinicaltrials.gov/ct2/show/NCT05764083

18. Iaquinto J, Ripley B, Dorn PA. How VA innovative partnerships and health care system can respond to national needs: NOSE trial example. Fed Pract. 2023;40(suppl 5):S52-S56. doi:10.12788/fp.0418

19. US Department of Veterans Affairs. Health Services Research & Development research career development program. Updated March 4, 2021. Accessed September 11, 2023. https://hsrd.research.va.gov/cdp/

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Introduction

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Bad times have a scientific value. These are occasions a good learner would not miss.

Ralph Waldo Emerson

Like the flip of a light switch, the world in March 2020 went into lockdown. Suddenly the novel coronavirus disease (COVID-19) was ever-present and everywhere. At a time when very little was certain, scientific inquiry—along with its related skills and disciplines—offered a much-needed pathway for navigating the virus’s myriad unknowns.

From the pandemic’s onset, the Veterans Health Administration (VHA) of the US Department of Veterans Affairs (VA) made singular contributions to the advancement and acceleration of national and international research activity. This special issue of Federal Practitioner demonstrates how the VHA, through its Office of Research and Development (ORD), took advantage of its newly deployed enterprise strategy to meet the unprecedented demands of this public health emergency.

Launched in 2017, the ORD enterprise strategy enabled the VHA not only to capitalize on existing collaborations—both internal and external—but also move swiftly in forging new ones. Additionally, the strategy was key to leveraging unique VHA assets as the nation’s largest integrated health care system, including: (1) nationwide clinical trials infrastructure, including its longstanding Cooperative Studies Program; (2) a tightly integrated system of clinical care and research that serves as a ready platform for big data science, the world’s largest genomic database, and emergent capabilities; and (3) an established innovation ecosystem that worked with VA research to address rapidly changing circumstances.

In The VA Research Enterprise (p. S12), Garcia and colleagues demonstrate how the VHA pandemic response “arose from an enterprise strategy that was already in motion and aimed at identifying needs for supporting the clinical care mission, more rapidly leveraging resources, and coordinating research across the national VA health care system.” Thus, the VHA took a “model for a culture of cooperative research within the VA and with external groups” and translated it beyond the scope of clinical trials, which had been its foundation.

Led by Chief Research and Development Officer Rachel Ramoni, DMD, ScD, this strategy forged 121 VA medical centers conducting research into an integrated enterprise that could respond to needs for scientific evidence in a coordinated fashion, thereby translating research into practice for real impact on veterans. This approach built on relationships with not only scientific communities but also clinical and operational partners working within the VA to address the immediate pandemic-related needs.

In tandem with its physical infrastructure, the VA’s longstanding network of collaborators, physical infrastructure, and ability to develop new partnerships became drivers of success. Because of previous, ongoing, multisite clinical trials and observational studies, the VA had already partnered with numerous federal government agencies and industry groups and was able to quickly set up a VA COVID-19 clinical trial master protocol framework called the CURES (VA Coronavirus Research and Efficacy studies) network. The ORD enterprise strategy is noted by several other authors, including Caroff and colleagues, who show how the VA efforts to broaden partnerships prepandemic were critical to its participation in 7 large-scale COVID-19 therapeutic and vaccine trials (p. S18).

Similarly, in discussing the VA Million Veteran Program (MVP), Whitbourne and colleagues (p. S23) demonstrate how the VA research strategy and infrastructure were key to leveraging “unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations.”

Launched in 2011, the MVP is one of the world’s largest genomic cohorts, with more than 985,000 veterans enrolled. MVP developers had the prescience to foresee how a robust genomic database could inform public health emergencies. Whitbourne and colleagues show the many ways the MVP facilitated the VHA COVID-19 response. By extending the MVP centralized recruitment and enrollment infrastructure, an ORD COVID-19 volunteer registry successfully registered 50,000 veterans interested in volunteering for clinical trials.

This tight integration between research and clinical care is one of the VHA’s greatest assets as a health care system. More than 60% of VA researchers are also clinicians who provide direct patient care. This enables VA physician-researchers to learn directly from veteran patients and quickly translate new findings into improved care. It also supported numerous capabilities that played a key role during the pandemic.

For example, in the article VA Big Data Science (p. S39), Young-Xu and colleagues note that the VA use of health care data proved medical research could be performed “quickly and judiciously.” Foundational to this research was a data sharing framework, electronic health record, and VA Corporate Data Warehouse that were accessible to all VA researchers. Researchers had access to clinical data and patient health records that allowed them to perform targeted, time-sensitive research. By building a cohort of 1,363,180 veterans who received ≥ 1 vaccine dose by March 7, 2021, VA researchers added significantly to our understanding of the real-world COVID-19 vaccine clinical performance.

In addition to leveraging existing capabilities, VHA clinicians and researchers created new ones in response. Krishnan and colleagues discuss the launch of 2 clinical and research consortiums focused on COVID-19 genomic surveillance (p. S44). SeqFORCE positioned the VHA to rapidly detect emergent variants and better inform the care of patients with COVID-19. SeqCURE focused on the broader study and trends of variants through sequencing.

The tightly integrated nature of VA care also supported the creation of a large-scale biorepository of specimens with accompanying clinical data to advance research and improve diagnostic and therapeutic research. Epstein and colleagues share the developmental history of the VA SHIELD biorepository, its structure, and its current and future contributions to research science (p. S48).

Finally, the same forward-learning culture which gave rise to the ORD enterprise strategy also resulted in an innovation ecosystem that was well established prior to March 2020. Now a firmly established portfolio within the VHA Office of Healthcare Innovation and Learning (OHIL), the VHA Innovation Ecosystem engages frontline clinicians in reimagining veteran health care. Iaquinto and colleagues discuss how the ecosystem’s preexisting partnerships were critical to addressing shortages in personal protective equipment and other vital resources (p. S52). The OHIL provided the quality system and manufacturing oversight and delivery of swabs for testing, while the ORD furnished research infrastructure and human subjects oversight. Together, these offices not only addressed the shortage by producing swabs but also validated the swabs’ safety and efficacy in the clinical setting.

The articles in this special issue chronicle how the VA quickly mobilized its considerable enterprise-wide resources—especially during the pandemic’s acute phases—to contribute to timely veteran, national, and global evidence about what interventions were effective, what factors were associated with better care and outcomes, and how to flip the switch back to a nonemergency response. As Emerson might have observed, the scientific value of these recent “bad times” did not go unnoticed by VHA learners. In addition to catalyzing opportunities that accelerated the VHA enterprise strategy, the pandemic strengthened existing partnerships, led to new ones, and yielded lessons learned. With variants of the virus continuing to circulate, the VHA continues to harness the lessons learned from the emergency response perspective of the pandemic in order to effectively meet and exceed our mission to serve veterans.

The 35 authors whose work is featured in this issue—and their 3665 colleagues across the VHA research enterprise—offer testament not only to the power of scientific inquiry but of dedication to the mission by the individuals whose lives and families were also impacted by the pandemic.

VA Research continues working to unravel the ongoing impact of COVID-19. As the nation observes an increase in cases again, the VA is ready and well positioned to help lead and address needs for this and other public health crises.

Acknowledgments

This special issue is dedicated to Mitchell (Mitch) Mirkin and his enduring legacy at VA Research, helping to make the contributions of VA Research known as broadly as possible. A superb writer and “editor’s editor,” Mitch had an outstanding ability to translate complex scientific findings into layman’s terms. From the start of the pandemic to his unexpected passing in 2022, Mitch was Acting Director of VA Research Communications. He was a key member of the VA Office of Research and Development COVID-19 research response team. His contributions included his work leading to the generation of this Issue.

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Bad times have a scientific value. These are occasions a good learner would not miss.

Ralph Waldo Emerson

Like the flip of a light switch, the world in March 2020 went into lockdown. Suddenly the novel coronavirus disease (COVID-19) was ever-present and everywhere. At a time when very little was certain, scientific inquiry—along with its related skills and disciplines—offered a much-needed pathway for navigating the virus’s myriad unknowns.

From the pandemic’s onset, the Veterans Health Administration (VHA) of the US Department of Veterans Affairs (VA) made singular contributions to the advancement and acceleration of national and international research activity. This special issue of Federal Practitioner demonstrates how the VHA, through its Office of Research and Development (ORD), took advantage of its newly deployed enterprise strategy to meet the unprecedented demands of this public health emergency.

Launched in 2017, the ORD enterprise strategy enabled the VHA not only to capitalize on existing collaborations—both internal and external—but also move swiftly in forging new ones. Additionally, the strategy was key to leveraging unique VHA assets as the nation’s largest integrated health care system, including: (1) nationwide clinical trials infrastructure, including its longstanding Cooperative Studies Program; (2) a tightly integrated system of clinical care and research that serves as a ready platform for big data science, the world’s largest genomic database, and emergent capabilities; and (3) an established innovation ecosystem that worked with VA research to address rapidly changing circumstances.

In The VA Research Enterprise (p. S12), Garcia and colleagues demonstrate how the VHA pandemic response “arose from an enterprise strategy that was already in motion and aimed at identifying needs for supporting the clinical care mission, more rapidly leveraging resources, and coordinating research across the national VA health care system.” Thus, the VHA took a “model for a culture of cooperative research within the VA and with external groups” and translated it beyond the scope of clinical trials, which had been its foundation.

Led by Chief Research and Development Officer Rachel Ramoni, DMD, ScD, this strategy forged 121 VA medical centers conducting research into an integrated enterprise that could respond to needs for scientific evidence in a coordinated fashion, thereby translating research into practice for real impact on veterans. This approach built on relationships with not only scientific communities but also clinical and operational partners working within the VA to address the immediate pandemic-related needs.

In tandem with its physical infrastructure, the VA’s longstanding network of collaborators, physical infrastructure, and ability to develop new partnerships became drivers of success. Because of previous, ongoing, multisite clinical trials and observational studies, the VA had already partnered with numerous federal government agencies and industry groups and was able to quickly set up a VA COVID-19 clinical trial master protocol framework called the CURES (VA Coronavirus Research and Efficacy studies) network. The ORD enterprise strategy is noted by several other authors, including Caroff and colleagues, who show how the VA efforts to broaden partnerships prepandemic were critical to its participation in 7 large-scale COVID-19 therapeutic and vaccine trials (p. S18).

Similarly, in discussing the VA Million Veteran Program (MVP), Whitbourne and colleagues (p. S23) demonstrate how the VA research strategy and infrastructure were key to leveraging “unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations.”

Launched in 2011, the MVP is one of the world’s largest genomic cohorts, with more than 985,000 veterans enrolled. MVP developers had the prescience to foresee how a robust genomic database could inform public health emergencies. Whitbourne and colleagues show the many ways the MVP facilitated the VHA COVID-19 response. By extending the MVP centralized recruitment and enrollment infrastructure, an ORD COVID-19 volunteer registry successfully registered 50,000 veterans interested in volunteering for clinical trials.

This tight integration between research and clinical care is one of the VHA’s greatest assets as a health care system. More than 60% of VA researchers are also clinicians who provide direct patient care. This enables VA physician-researchers to learn directly from veteran patients and quickly translate new findings into improved care. It also supported numerous capabilities that played a key role during the pandemic.

For example, in the article VA Big Data Science (p. S39), Young-Xu and colleagues note that the VA use of health care data proved medical research could be performed “quickly and judiciously.” Foundational to this research was a data sharing framework, electronic health record, and VA Corporate Data Warehouse that were accessible to all VA researchers. Researchers had access to clinical data and patient health records that allowed them to perform targeted, time-sensitive research. By building a cohort of 1,363,180 veterans who received ≥ 1 vaccine dose by March 7, 2021, VA researchers added significantly to our understanding of the real-world COVID-19 vaccine clinical performance.

In addition to leveraging existing capabilities, VHA clinicians and researchers created new ones in response. Krishnan and colleagues discuss the launch of 2 clinical and research consortiums focused on COVID-19 genomic surveillance (p. S44). SeqFORCE positioned the VHA to rapidly detect emergent variants and better inform the care of patients with COVID-19. SeqCURE focused on the broader study and trends of variants through sequencing.

The tightly integrated nature of VA care also supported the creation of a large-scale biorepository of specimens with accompanying clinical data to advance research and improve diagnostic and therapeutic research. Epstein and colleagues share the developmental history of the VA SHIELD biorepository, its structure, and its current and future contributions to research science (p. S48).

Finally, the same forward-learning culture which gave rise to the ORD enterprise strategy also resulted in an innovation ecosystem that was well established prior to March 2020. Now a firmly established portfolio within the VHA Office of Healthcare Innovation and Learning (OHIL), the VHA Innovation Ecosystem engages frontline clinicians in reimagining veteran health care. Iaquinto and colleagues discuss how the ecosystem’s preexisting partnerships were critical to addressing shortages in personal protective equipment and other vital resources (p. S52). The OHIL provided the quality system and manufacturing oversight and delivery of swabs for testing, while the ORD furnished research infrastructure and human subjects oversight. Together, these offices not only addressed the shortage by producing swabs but also validated the swabs’ safety and efficacy in the clinical setting.

The articles in this special issue chronicle how the VA quickly mobilized its considerable enterprise-wide resources—especially during the pandemic’s acute phases—to contribute to timely veteran, national, and global evidence about what interventions were effective, what factors were associated with better care and outcomes, and how to flip the switch back to a nonemergency response. As Emerson might have observed, the scientific value of these recent “bad times” did not go unnoticed by VHA learners. In addition to catalyzing opportunities that accelerated the VHA enterprise strategy, the pandemic strengthened existing partnerships, led to new ones, and yielded lessons learned. With variants of the virus continuing to circulate, the VHA continues to harness the lessons learned from the emergency response perspective of the pandemic in order to effectively meet and exceed our mission to serve veterans.

The 35 authors whose work is featured in this issue—and their 3665 colleagues across the VHA research enterprise—offer testament not only to the power of scientific inquiry but of dedication to the mission by the individuals whose lives and families were also impacted by the pandemic.

VA Research continues working to unravel the ongoing impact of COVID-19. As the nation observes an increase in cases again, the VA is ready and well positioned to help lead and address needs for this and other public health crises.

Acknowledgments

This special issue is dedicated to Mitchell (Mitch) Mirkin and his enduring legacy at VA Research, helping to make the contributions of VA Research known as broadly as possible. A superb writer and “editor’s editor,” Mitch had an outstanding ability to translate complex scientific findings into layman’s terms. From the start of the pandemic to his unexpected passing in 2022, Mitch was Acting Director of VA Research Communications. He was a key member of the VA Office of Research and Development COVID-19 research response team. His contributions included his work leading to the generation of this Issue.

Bad times have a scientific value. These are occasions a good learner would not miss.

Ralph Waldo Emerson

Like the flip of a light switch, the world in March 2020 went into lockdown. Suddenly the novel coronavirus disease (COVID-19) was ever-present and everywhere. At a time when very little was certain, scientific inquiry—along with its related skills and disciplines—offered a much-needed pathway for navigating the virus’s myriad unknowns.

From the pandemic’s onset, the Veterans Health Administration (VHA) of the US Department of Veterans Affairs (VA) made singular contributions to the advancement and acceleration of national and international research activity. This special issue of Federal Practitioner demonstrates how the VHA, through its Office of Research and Development (ORD), took advantage of its newly deployed enterprise strategy to meet the unprecedented demands of this public health emergency.

Launched in 2017, the ORD enterprise strategy enabled the VHA not only to capitalize on existing collaborations—both internal and external—but also move swiftly in forging new ones. Additionally, the strategy was key to leveraging unique VHA assets as the nation’s largest integrated health care system, including: (1) nationwide clinical trials infrastructure, including its longstanding Cooperative Studies Program; (2) a tightly integrated system of clinical care and research that serves as a ready platform for big data science, the world’s largest genomic database, and emergent capabilities; and (3) an established innovation ecosystem that worked with VA research to address rapidly changing circumstances.

In The VA Research Enterprise (p. S12), Garcia and colleagues demonstrate how the VHA pandemic response “arose from an enterprise strategy that was already in motion and aimed at identifying needs for supporting the clinical care mission, more rapidly leveraging resources, and coordinating research across the national VA health care system.” Thus, the VHA took a “model for a culture of cooperative research within the VA and with external groups” and translated it beyond the scope of clinical trials, which had been its foundation.

Led by Chief Research and Development Officer Rachel Ramoni, DMD, ScD, this strategy forged 121 VA medical centers conducting research into an integrated enterprise that could respond to needs for scientific evidence in a coordinated fashion, thereby translating research into practice for real impact on veterans. This approach built on relationships with not only scientific communities but also clinical and operational partners working within the VA to address the immediate pandemic-related needs.

In tandem with its physical infrastructure, the VA’s longstanding network of collaborators, physical infrastructure, and ability to develop new partnerships became drivers of success. Because of previous, ongoing, multisite clinical trials and observational studies, the VA had already partnered with numerous federal government agencies and industry groups and was able to quickly set up a VA COVID-19 clinical trial master protocol framework called the CURES (VA Coronavirus Research and Efficacy studies) network. The ORD enterprise strategy is noted by several other authors, including Caroff and colleagues, who show how the VA efforts to broaden partnerships prepandemic were critical to its participation in 7 large-scale COVID-19 therapeutic and vaccine trials (p. S18).

Similarly, in discussing the VA Million Veteran Program (MVP), Whitbourne and colleagues (p. S23) demonstrate how the VA research strategy and infrastructure were key to leveraging “unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations.”

Launched in 2011, the MVP is one of the world’s largest genomic cohorts, with more than 985,000 veterans enrolled. MVP developers had the prescience to foresee how a robust genomic database could inform public health emergencies. Whitbourne and colleagues show the many ways the MVP facilitated the VHA COVID-19 response. By extending the MVP centralized recruitment and enrollment infrastructure, an ORD COVID-19 volunteer registry successfully registered 50,000 veterans interested in volunteering for clinical trials.

This tight integration between research and clinical care is one of the VHA’s greatest assets as a health care system. More than 60% of VA researchers are also clinicians who provide direct patient care. This enables VA physician-researchers to learn directly from veteran patients and quickly translate new findings into improved care. It also supported numerous capabilities that played a key role during the pandemic.

For example, in the article VA Big Data Science (p. S39), Young-Xu and colleagues note that the VA use of health care data proved medical research could be performed “quickly and judiciously.” Foundational to this research was a data sharing framework, electronic health record, and VA Corporate Data Warehouse that were accessible to all VA researchers. Researchers had access to clinical data and patient health records that allowed them to perform targeted, time-sensitive research. By building a cohort of 1,363,180 veterans who received ≥ 1 vaccine dose by March 7, 2021, VA researchers added significantly to our understanding of the real-world COVID-19 vaccine clinical performance.

In addition to leveraging existing capabilities, VHA clinicians and researchers created new ones in response. Krishnan and colleagues discuss the launch of 2 clinical and research consortiums focused on COVID-19 genomic surveillance (p. S44). SeqFORCE positioned the VHA to rapidly detect emergent variants and better inform the care of patients with COVID-19. SeqCURE focused on the broader study and trends of variants through sequencing.

The tightly integrated nature of VA care also supported the creation of a large-scale biorepository of specimens with accompanying clinical data to advance research and improve diagnostic and therapeutic research. Epstein and colleagues share the developmental history of the VA SHIELD biorepository, its structure, and its current and future contributions to research science (p. S48).

Finally, the same forward-learning culture which gave rise to the ORD enterprise strategy also resulted in an innovation ecosystem that was well established prior to March 2020. Now a firmly established portfolio within the VHA Office of Healthcare Innovation and Learning (OHIL), the VHA Innovation Ecosystem engages frontline clinicians in reimagining veteran health care. Iaquinto and colleagues discuss how the ecosystem’s preexisting partnerships were critical to addressing shortages in personal protective equipment and other vital resources (p. S52). The OHIL provided the quality system and manufacturing oversight and delivery of swabs for testing, while the ORD furnished research infrastructure and human subjects oversight. Together, these offices not only addressed the shortage by producing swabs but also validated the swabs’ safety and efficacy in the clinical setting.

The articles in this special issue chronicle how the VA quickly mobilized its considerable enterprise-wide resources—especially during the pandemic’s acute phases—to contribute to timely veteran, national, and global evidence about what interventions were effective, what factors were associated with better care and outcomes, and how to flip the switch back to a nonemergency response. As Emerson might have observed, the scientific value of these recent “bad times” did not go unnoticed by VHA learners. In addition to catalyzing opportunities that accelerated the VHA enterprise strategy, the pandemic strengthened existing partnerships, led to new ones, and yielded lessons learned. With variants of the virus continuing to circulate, the VHA continues to harness the lessons learned from the emergency response perspective of the pandemic in order to effectively meet and exceed our mission to serve veterans.

The 35 authors whose work is featured in this issue—and their 3665 colleagues across the VHA research enterprise—offer testament not only to the power of scientific inquiry but of dedication to the mission by the individuals whose lives and families were also impacted by the pandemic.

VA Research continues working to unravel the ongoing impact of COVID-19. As the nation observes an increase in cases again, the VA is ready and well positioned to help lead and address needs for this and other public health crises.

Acknowledgments

This special issue is dedicated to Mitchell (Mitch) Mirkin and his enduring legacy at VA Research, helping to make the contributions of VA Research known as broadly as possible. A superb writer and “editor’s editor,” Mitch had an outstanding ability to translate complex scientific findings into layman’s terms. From the start of the pandemic to his unexpected passing in 2022, Mitch was Acting Director of VA Research Communications. He was a key member of the VA Office of Research and Development COVID-19 research response team. His contributions included his work leading to the generation of this Issue.

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Foreword: VA Research and COVID-19

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Sylvester Norman, a 67-year-old Coast Guard veteran and retired day-care worker from Nashville, Tennessee, volunteered to participate in the US Department of Veterans Affairs (VA) Million Veteran Program (MVP). He and all 4 of his brothers had experienced kidney illness. During the pandemic, Adriana Hung, MD, MPH, an MVP researcher and associate professor of nephrology at Vanderbilt University, noticed that a disproportionate number of Black patients hospitalized with COVID-19 were dying of acute kidney failure. Dr. Hung used data from Norman and other Black veterans provided through the MVP to identify genetic variations in the APOL1 gene linked to kidney disease found in 1 of every 8 people of African descent. Her research proved that a COVID-19 viral infection can trigger these genes and drive a patient’s kidneys to go into failure. Thanks to her research and volunteers like Norman, a new drug targeting APOL1 may soon receive approval from the US Food and Drug Administration (FDA).

This is only one example of the life-saving work conducted by the Veterans Health Administration (VHA) during the pandemic. On January 21, 2020, 1 day after the first confirmed COVID-19 case in the US, the VHA quickly activated its Emergency Management Coordination Cell (EMCC) under a unified command structure with round-the-clock operations to track the evolving risk and plan a response to this once-in-a-century pandemic. A few months later, and before the US declared COVID-19 a pandemic, the VHA research program sprang into action, preparing its community of investigators to address the emerging needs and challenges of the COVID-19 public health crisis. Three years later, although the federal COVID-19 public emergency is declared over, the VHA remains diligent in observing trends and conducting necessary research on the disease as case numbers rise and fall across time.

This special issue of Federal Practitioner showcases the many ways that the VHA successfully leveraged and rapidly mobilized its research enterprise capabilities as part of the national response to COVID-19 and continues to work in this area. As the virus rapidly spread across the country, the VHA research program, overseen by the Office of Research and Development (ORD) and in partnership with other VHA offices, demonstrated the strength and agility that come from being part of a nationwide integrated health care system.

Historically, the VHA has been one of the nation’s leaders in translating medical breakthroughs to the treatment and care of veterans and the nation. Today, the VHA ensures that veterans have increased access to innovative health care solutions by promoting new medical research initiatives, training health care professionals, and developing community partnerships.

As this special issue of Federal Practitioner demonstrates, the VHA’s extraordinary research response to the COVID-19 pandemic was shaped by its ongoing transformation to a full-scale research enterprise; diversity of partnerships with academia, other federal agencies, and industry; extensive infrastructure for funding and quickly ramping up multisite clinical trials; and longstanding partnership with veterans, who volunteer to serve their country twice—first in uniform, and later by volunteering to participate in VA research.

By leveraging these and other assets, VHA investigators have conducted > 900 COVID-19 research projects across 83 VA medical centers, with nearly 3000 VA-affiliated papers published by mid-2023. We have also become a leader in long COVID, generating notable findings using our electronic health record data and filling in the picture with studies that include interviews with thousands of patients, examinations of blood markers, and exploration of the role of genetics. Along the way, the VA collaborated with federal partners, such as the US Department of Defense, by funding a longitudinal research cohort in which 2800 veterans are enrolled. Through this joint effort, researchers will learn more about the natural history and outcomes among veterans affected by COVID-19. This work continues as part of the VA commitment to the health and care of these veterans and nation as a whole.

Additionally, by partnering with veterans, the VA established a research volunteer registry. More than 58,000 veterans volunteered to be contacted to participate in studies if they were eligible. This effort was critical to the VA’s ability to contribute to the vaccine and other therapeutic trials that were seeking approval from the FDA for broader public use. This volunteerism by these veterans showed the nation that the VA is a valuable partner in times of need.

The VA research program remains tightly focused on understanding the long-term impacts of COVID-19. At the same time, the VA is committed to using lessons learned during the crisis in addressing high priorities in veterans’ health care. Among those priorities is fulfilling our mission under the Sergeant First Class Heath Robinson Honoring Our Promise to Address Comprehensive Toxics (PACT) Act of 2022 to improve care for veterans with military environmental exposures. Over the next few years, VA researchers will analyze health care and epidemiologic data to improve the identification and treatment of medical conditions potentially associated with toxic exposures. This work will include analyses of health trends of post-9/11 veterans, cancer rates among veterans, toxic exposure and mental health outcomes, and the health effects of jet fuels.

Our research program also will support the VA priority of hiring faster and more competitively. With many of the 3700 VA-funded principal investigators also serving as faculty at top universities, VA research programs position us to recruit the best and brightest professionals on the cutting edge of health care. These efforts work hand in hand with the clinical training the VA provides to 113,000 health professions trainees, creating a pipeline of clinicians and physician-researchers for the future. Further, these partnerships strengthen the VA’s ability to expand access by connecting veterans to the best, immediate care.

Finally, VA research will continue to be critical to our top clinical priority of preventing veteran suicide. This area of VA research covers a wide and critically important set of topics, such as the use of predictive modeling to determine veterans most at risk as well as studies on substance use disorders and suicidal ideation, among others.

The impressive collection of articles in this special issue provides a snapshot of the large-scale, all-hands approach the VHA adopted during the COVID-19 public health crisis. I am extremely proud of the work you are about to read.

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Sylvester Norman, a 67-year-old Coast Guard veteran and retired day-care worker from Nashville, Tennessee, volunteered to participate in the US Department of Veterans Affairs (VA) Million Veteran Program (MVP). He and all 4 of his brothers had experienced kidney illness. During the pandemic, Adriana Hung, MD, MPH, an MVP researcher and associate professor of nephrology at Vanderbilt University, noticed that a disproportionate number of Black patients hospitalized with COVID-19 were dying of acute kidney failure. Dr. Hung used data from Norman and other Black veterans provided through the MVP to identify genetic variations in the APOL1 gene linked to kidney disease found in 1 of every 8 people of African descent. Her research proved that a COVID-19 viral infection can trigger these genes and drive a patient’s kidneys to go into failure. Thanks to her research and volunteers like Norman, a new drug targeting APOL1 may soon receive approval from the US Food and Drug Administration (FDA).

This is only one example of the life-saving work conducted by the Veterans Health Administration (VHA) during the pandemic. On January 21, 2020, 1 day after the first confirmed COVID-19 case in the US, the VHA quickly activated its Emergency Management Coordination Cell (EMCC) under a unified command structure with round-the-clock operations to track the evolving risk and plan a response to this once-in-a-century pandemic. A few months later, and before the US declared COVID-19 a pandemic, the VHA research program sprang into action, preparing its community of investigators to address the emerging needs and challenges of the COVID-19 public health crisis. Three years later, although the federal COVID-19 public emergency is declared over, the VHA remains diligent in observing trends and conducting necessary research on the disease as case numbers rise and fall across time.

This special issue of Federal Practitioner showcases the many ways that the VHA successfully leveraged and rapidly mobilized its research enterprise capabilities as part of the national response to COVID-19 and continues to work in this area. As the virus rapidly spread across the country, the VHA research program, overseen by the Office of Research and Development (ORD) and in partnership with other VHA offices, demonstrated the strength and agility that come from being part of a nationwide integrated health care system.

Historically, the VHA has been one of the nation’s leaders in translating medical breakthroughs to the treatment and care of veterans and the nation. Today, the VHA ensures that veterans have increased access to innovative health care solutions by promoting new medical research initiatives, training health care professionals, and developing community partnerships.

As this special issue of Federal Practitioner demonstrates, the VHA’s extraordinary research response to the COVID-19 pandemic was shaped by its ongoing transformation to a full-scale research enterprise; diversity of partnerships with academia, other federal agencies, and industry; extensive infrastructure for funding and quickly ramping up multisite clinical trials; and longstanding partnership with veterans, who volunteer to serve their country twice—first in uniform, and later by volunteering to participate in VA research.

By leveraging these and other assets, VHA investigators have conducted > 900 COVID-19 research projects across 83 VA medical centers, with nearly 3000 VA-affiliated papers published by mid-2023. We have also become a leader in long COVID, generating notable findings using our electronic health record data and filling in the picture with studies that include interviews with thousands of patients, examinations of blood markers, and exploration of the role of genetics. Along the way, the VA collaborated with federal partners, such as the US Department of Defense, by funding a longitudinal research cohort in which 2800 veterans are enrolled. Through this joint effort, researchers will learn more about the natural history and outcomes among veterans affected by COVID-19. This work continues as part of the VA commitment to the health and care of these veterans and nation as a whole.

Additionally, by partnering with veterans, the VA established a research volunteer registry. More than 58,000 veterans volunteered to be contacted to participate in studies if they were eligible. This effort was critical to the VA’s ability to contribute to the vaccine and other therapeutic trials that were seeking approval from the FDA for broader public use. This volunteerism by these veterans showed the nation that the VA is a valuable partner in times of need.

The VA research program remains tightly focused on understanding the long-term impacts of COVID-19. At the same time, the VA is committed to using lessons learned during the crisis in addressing high priorities in veterans’ health care. Among those priorities is fulfilling our mission under the Sergeant First Class Heath Robinson Honoring Our Promise to Address Comprehensive Toxics (PACT) Act of 2022 to improve care for veterans with military environmental exposures. Over the next few years, VA researchers will analyze health care and epidemiologic data to improve the identification and treatment of medical conditions potentially associated with toxic exposures. This work will include analyses of health trends of post-9/11 veterans, cancer rates among veterans, toxic exposure and mental health outcomes, and the health effects of jet fuels.

Our research program also will support the VA priority of hiring faster and more competitively. With many of the 3700 VA-funded principal investigators also serving as faculty at top universities, VA research programs position us to recruit the best and brightest professionals on the cutting edge of health care. These efforts work hand in hand with the clinical training the VA provides to 113,000 health professions trainees, creating a pipeline of clinicians and physician-researchers for the future. Further, these partnerships strengthen the VA’s ability to expand access by connecting veterans to the best, immediate care.

Finally, VA research will continue to be critical to our top clinical priority of preventing veteran suicide. This area of VA research covers a wide and critically important set of topics, such as the use of predictive modeling to determine veterans most at risk as well as studies on substance use disorders and suicidal ideation, among others.

The impressive collection of articles in this special issue provides a snapshot of the large-scale, all-hands approach the VHA adopted during the COVID-19 public health crisis. I am extremely proud of the work you are about to read.

Sylvester Norman, a 67-year-old Coast Guard veteran and retired day-care worker from Nashville, Tennessee, volunteered to participate in the US Department of Veterans Affairs (VA) Million Veteran Program (MVP). He and all 4 of his brothers had experienced kidney illness. During the pandemic, Adriana Hung, MD, MPH, an MVP researcher and associate professor of nephrology at Vanderbilt University, noticed that a disproportionate number of Black patients hospitalized with COVID-19 were dying of acute kidney failure. Dr. Hung used data from Norman and other Black veterans provided through the MVP to identify genetic variations in the APOL1 gene linked to kidney disease found in 1 of every 8 people of African descent. Her research proved that a COVID-19 viral infection can trigger these genes and drive a patient’s kidneys to go into failure. Thanks to her research and volunteers like Norman, a new drug targeting APOL1 may soon receive approval from the US Food and Drug Administration (FDA).

This is only one example of the life-saving work conducted by the Veterans Health Administration (VHA) during the pandemic. On January 21, 2020, 1 day after the first confirmed COVID-19 case in the US, the VHA quickly activated its Emergency Management Coordination Cell (EMCC) under a unified command structure with round-the-clock operations to track the evolving risk and plan a response to this once-in-a-century pandemic. A few months later, and before the US declared COVID-19 a pandemic, the VHA research program sprang into action, preparing its community of investigators to address the emerging needs and challenges of the COVID-19 public health crisis. Three years later, although the federal COVID-19 public emergency is declared over, the VHA remains diligent in observing trends and conducting necessary research on the disease as case numbers rise and fall across time.

This special issue of Federal Practitioner showcases the many ways that the VHA successfully leveraged and rapidly mobilized its research enterprise capabilities as part of the national response to COVID-19 and continues to work in this area. As the virus rapidly spread across the country, the VHA research program, overseen by the Office of Research and Development (ORD) and in partnership with other VHA offices, demonstrated the strength and agility that come from being part of a nationwide integrated health care system.

Historically, the VHA has been one of the nation’s leaders in translating medical breakthroughs to the treatment and care of veterans and the nation. Today, the VHA ensures that veterans have increased access to innovative health care solutions by promoting new medical research initiatives, training health care professionals, and developing community partnerships.

As this special issue of Federal Practitioner demonstrates, the VHA’s extraordinary research response to the COVID-19 pandemic was shaped by its ongoing transformation to a full-scale research enterprise; diversity of partnerships with academia, other federal agencies, and industry; extensive infrastructure for funding and quickly ramping up multisite clinical trials; and longstanding partnership with veterans, who volunteer to serve their country twice—first in uniform, and later by volunteering to participate in VA research.

By leveraging these and other assets, VHA investigators have conducted > 900 COVID-19 research projects across 83 VA medical centers, with nearly 3000 VA-affiliated papers published by mid-2023. We have also become a leader in long COVID, generating notable findings using our electronic health record data and filling in the picture with studies that include interviews with thousands of patients, examinations of blood markers, and exploration of the role of genetics. Along the way, the VA collaborated with federal partners, such as the US Department of Defense, by funding a longitudinal research cohort in which 2800 veterans are enrolled. Through this joint effort, researchers will learn more about the natural history and outcomes among veterans affected by COVID-19. This work continues as part of the VA commitment to the health and care of these veterans and nation as a whole.

Additionally, by partnering with veterans, the VA established a research volunteer registry. More than 58,000 veterans volunteered to be contacted to participate in studies if they were eligible. This effort was critical to the VA’s ability to contribute to the vaccine and other therapeutic trials that were seeking approval from the FDA for broader public use. This volunteerism by these veterans showed the nation that the VA is a valuable partner in times of need.

The VA research program remains tightly focused on understanding the long-term impacts of COVID-19. At the same time, the VA is committed to using lessons learned during the crisis in addressing high priorities in veterans’ health care. Among those priorities is fulfilling our mission under the Sergeant First Class Heath Robinson Honoring Our Promise to Address Comprehensive Toxics (PACT) Act of 2022 to improve care for veterans with military environmental exposures. Over the next few years, VA researchers will analyze health care and epidemiologic data to improve the identification and treatment of medical conditions potentially associated with toxic exposures. This work will include analyses of health trends of post-9/11 veterans, cancer rates among veterans, toxic exposure and mental health outcomes, and the health effects of jet fuels.

Our research program also will support the VA priority of hiring faster and more competitively. With many of the 3700 VA-funded principal investigators also serving as faculty at top universities, VA research programs position us to recruit the best and brightest professionals on the cutting edge of health care. These efforts work hand in hand with the clinical training the VA provides to 113,000 health professions trainees, creating a pipeline of clinicians and physician-researchers for the future. Further, these partnerships strengthen the VA’s ability to expand access by connecting veterans to the best, immediate care.

Finally, VA research will continue to be critical to our top clinical priority of preventing veteran suicide. This area of VA research covers a wide and critically important set of topics, such as the use of predictive modeling to determine veterans most at risk as well as studies on substance use disorders and suicidal ideation, among others.

The impressive collection of articles in this special issue provides a snapshot of the large-scale, all-hands approach the VHA adopted during the COVID-19 public health crisis. I am extremely proud of the work you are about to read.

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VA and Non-VA Partners Improving Care by Sharing Data

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The US Department of Veterans Affairs (VA) and 13 health care systems have signed a pledge for interoperability—to securely share data on veteran health care, regardless of whether it is provided inside the VA or not.

“This pledge will improve veteran health care by giving us seamless, immediate access to a patient’s medical history, which will help us make timely and accurate treatment decisions,” said VA Under Secretary for Health Shereef Elnahal, MD, MBA. “It will also empower VA to send helpful information to our partner health systems that they can then offer to veterans in their care—including information about new benefits we are offering under the PACT Act, no-cost emergency suicide care, and more.”

The pledge will allow the health systems to access local, state, and federal health resources and will provide the VA access to health system clinical and administrative data for quality assessment and care coordination. The pledge signers are committed to developing and providing capabilities that: (1) Accurately identify veterans when they seek care from clinicians in [the signers’] communities; (2) Connect veterans with VA and community resources that promote health and health care—especially VA services that lower veterans’ out-of-pocket expenses; and (3) Responsively and reliably coordinate care for shared patients—including exchanging care information requested and provided.

In addition to helping reduce the financial burden for veterans, the VA says, the information sharing could help clinicians outside the VA system to provide more targeted care: “[I]t will also allow us to send helpful information to our partner health systems that they can then offer to veterans in their care,” Elnahal said, “to include information about new benefits we are offering under the PACT Act and other resources that assist with suicide prevention and identifying social risk factors."

The first pledge partners are Emory Healthcare, Inova, Jefferson Health, Sanford Health, University of California Davis Health, Intermountain Health, Mass General Brigham, Rush Health, Tufts Medicine, Marshfield Clinic, Kaiser Permanente Health Plan and Hospitals, University of Pittsburg Medical Center, and Atrium Health. Any health system or clinician that supports the pledge’s objectives is encouraged to participate, the VA says. Signers have begun work, and aim to provide proof-of-concept in early 2024.

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The US Department of Veterans Affairs (VA) and 13 health care systems have signed a pledge for interoperability—to securely share data on veteran health care, regardless of whether it is provided inside the VA or not.

“This pledge will improve veteran health care by giving us seamless, immediate access to a patient’s medical history, which will help us make timely and accurate treatment decisions,” said VA Under Secretary for Health Shereef Elnahal, MD, MBA. “It will also empower VA to send helpful information to our partner health systems that they can then offer to veterans in their care—including information about new benefits we are offering under the PACT Act, no-cost emergency suicide care, and more.”

The pledge will allow the health systems to access local, state, and federal health resources and will provide the VA access to health system clinical and administrative data for quality assessment and care coordination. The pledge signers are committed to developing and providing capabilities that: (1) Accurately identify veterans when they seek care from clinicians in [the signers’] communities; (2) Connect veterans with VA and community resources that promote health and health care—especially VA services that lower veterans’ out-of-pocket expenses; and (3) Responsively and reliably coordinate care for shared patients—including exchanging care information requested and provided.

In addition to helping reduce the financial burden for veterans, the VA says, the information sharing could help clinicians outside the VA system to provide more targeted care: “[I]t will also allow us to send helpful information to our partner health systems that they can then offer to veterans in their care,” Elnahal said, “to include information about new benefits we are offering under the PACT Act and other resources that assist with suicide prevention and identifying social risk factors."

The first pledge partners are Emory Healthcare, Inova, Jefferson Health, Sanford Health, University of California Davis Health, Intermountain Health, Mass General Brigham, Rush Health, Tufts Medicine, Marshfield Clinic, Kaiser Permanente Health Plan and Hospitals, University of Pittsburg Medical Center, and Atrium Health. Any health system or clinician that supports the pledge’s objectives is encouraged to participate, the VA says. Signers have begun work, and aim to provide proof-of-concept in early 2024.

The US Department of Veterans Affairs (VA) and 13 health care systems have signed a pledge for interoperability—to securely share data on veteran health care, regardless of whether it is provided inside the VA or not.

“This pledge will improve veteran health care by giving us seamless, immediate access to a patient’s medical history, which will help us make timely and accurate treatment decisions,” said VA Under Secretary for Health Shereef Elnahal, MD, MBA. “It will also empower VA to send helpful information to our partner health systems that they can then offer to veterans in their care—including information about new benefits we are offering under the PACT Act, no-cost emergency suicide care, and more.”

The pledge will allow the health systems to access local, state, and federal health resources and will provide the VA access to health system clinical and administrative data for quality assessment and care coordination. The pledge signers are committed to developing and providing capabilities that: (1) Accurately identify veterans when they seek care from clinicians in [the signers’] communities; (2) Connect veterans with VA and community resources that promote health and health care—especially VA services that lower veterans’ out-of-pocket expenses; and (3) Responsively and reliably coordinate care for shared patients—including exchanging care information requested and provided.

In addition to helping reduce the financial burden for veterans, the VA says, the information sharing could help clinicians outside the VA system to provide more targeted care: “[I]t will also allow us to send helpful information to our partner health systems that they can then offer to veterans in their care,” Elnahal said, “to include information about new benefits we are offering under the PACT Act and other resources that assist with suicide prevention and identifying social risk factors."

The first pledge partners are Emory Healthcare, Inova, Jefferson Health, Sanford Health, University of California Davis Health, Intermountain Health, Mass General Brigham, Rush Health, Tufts Medicine, Marshfield Clinic, Kaiser Permanente Health Plan and Hospitals, University of Pittsburg Medical Center, and Atrium Health. Any health system or clinician that supports the pledge’s objectives is encouraged to participate, the VA says. Signers have begun work, and aim to provide proof-of-concept in early 2024.

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Meet the JCOM Author with Dr. Barkoudah: The Hospitalist Triage Role for Reducing Admission Delays

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Meet the JCOM Author with Dr. Barkoudah: The Hospitalist Triage Role for Reducing Admission Delays
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The Hospitalist Triage Role for Reducing Admission Delays: Impacts on Throughput, Quality, Interprofessional Practice, and Clinician Experience of Care

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The Hospitalist Triage Role for Reducing Admission Delays: Impacts on Throughput, Quality, Interprofessional Practice, and Clinician Experience of Care

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)1-3 and total inpatient cost,1 as well as increased inpatient mortality, higher left-without-being-seen rates,4 delays in clinically meaningful care,5,6 and poor patient and clinician satisfaction.6,7 While various solutions have been proposed to alleviate ED crowding,8 excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”9 Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.9 Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.10 No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

 

 

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.11 Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

 

 

Results

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

Time to admission (TTA) throughout pilot and staged implementation

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

ED-2 (median time elapsed from admit decision time to time of departure from the ED for patients admitted to inpatient status) from pre-intervention (July 2019) period through postintervention (December 2020).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Percentage of internal medicine admissions with emergency department admission request (EDAR)

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

Results of Pre- and Postintervention Survey of Interprofessional Practice Perspectives

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Results of Pre- and Postintervention Survey of Clinician Experience of Care

 

 

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.9 A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al12 reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

References

1. Huang Q, Thind A, Dreyer JF, et al. The impact of delays to admission from the emergency department on inpatient outcomes. BMC Emerg Med. 2010;10:16. doi:10.1186/1471-227X-10-16

2. Liew D, Liew D, Kennedy MP. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust. 2003;179:524-526. doi:10.5694/j.1326-5377.2003.tb05676.x

3. Richardson DB. The access-block effect: relationship between delay to reaching an inpatient bed and inpatient length of stay. Med J Aust. 2002;177:492-495. doi:10.5694/j.1326-5377.2002.tb04917.x

4. Polevoi SK, Quinn JV, Kramer KR. Factors associated with patients who leave without being seen. Acad Emerg Med. 2005;12:232-236. doi:10.1197/j.aem.2004.10.029

5. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16:1-10. doi:10.1111/j.1553-2712.2008.00295.x

6. Vieth TL, Rhodes KV. The effect of crowding on access and quality in an academic ED. Am J Emerg Med. 2006;24:787-794. doi:10.1016/j.ajem.2006.03.026

7. Rondeau KV, Francescutti LH. Emergency department overcrowding: the impact of resource scarcity on physician job satisfaction. J Healthc Manag. 2005;50:327-340; discussion 341-342.

8. Emergency Department Crowding: High Impact Solutions. American College of Emergency Physicians. Emergency Medicine Practice Committee. 2016. Accessed March 31, 2023. https://www.acep.org/globalassets/sites/acep/media/crowding/empc_crowding-ip_092016.pdf

9. Velásquez ST, Wang ES, White AW, et al. Hospitalists as triagists: description of the triagist role across academic medical centers. J Hosp Med. 2020;15:87-90. doi:10.12788/jhm.3327

10. Amick A, Bann M. Characterizing the role of the “triagist”: reasons for triage discordance and impact on disposition. J Gen Intern Med. 2021;36:2177-2179. doi:10.1007/s11606-020-05887-y

11. Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning for variation in healthcare processes. BMJ Qual Saf. 2011;20:46-51. doi:10.1136/bmjqs.2009.037895

12. Howell EE, Bessman ES, Rubin HR. Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19:266-268. doi:10.1111/j.1525-1497.2004.30431.x

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From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)1-3 and total inpatient cost,1 as well as increased inpatient mortality, higher left-without-being-seen rates,4 delays in clinically meaningful care,5,6 and poor patient and clinician satisfaction.6,7 While various solutions have been proposed to alleviate ED crowding,8 excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”9 Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.9 Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.10 No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

 

 

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.11 Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

 

 

Results

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

Time to admission (TTA) throughout pilot and staged implementation

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

ED-2 (median time elapsed from admit decision time to time of departure from the ED for patients admitted to inpatient status) from pre-intervention (July 2019) period through postintervention (December 2020).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Percentage of internal medicine admissions with emergency department admission request (EDAR)

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

Results of Pre- and Postintervention Survey of Interprofessional Practice Perspectives

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Results of Pre- and Postintervention Survey of Clinician Experience of Care

 

 

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.9 A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al12 reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

From the Division of Hospital Medicine, University of New Mexico Hospital, Albuquerque (Drs. Bartlett, Pizanis, Angeli, Lacy, and Rogers), Department of Emergency Medicine, University of New Mexico Hospital, Albuquerque (Dr. Scott), and University of New Mexico School of Medicine, Albuquerque (Ms. Baca).

ABSTRACT

Background: Emergency department (ED) crowding is associated with deleterious consequences for patient care and throughput. Admission delays worsen ED crowding. Time to admission (TTA)—the time between an ED admission request and internal medicine (IM) admission orders—can be shortened through implementation of a triage hospitalist role. Limited research is available highlighting the impact of triage hospitalists on throughput, care quality, interprofessional practice, and clinician experience of care.

Methods: A triage hospitalist role was piloted and implemented. Run charts were interpreted using accepted rules for deriving statistically significant conclusions. Statistical analysis was applied to interprofessional practice and clinician experience-of-care survey results.

Results: Following implementation, TTA decreased from 5 hours 19 minutes to 2 hours 8 minutes. Emergency department crowding increased from baseline. The reduction in TTA was associated with decreased time from ED arrival to IM admission request, no change in critical care transfers during the initial 24 hours, and increased admissions to inpatient status. Additionally, decreased TTA was associated with no change in referring hospital transfer rates and no change in hospital medicine length of stay. Interprofessional practice attitudes improved among ED clinicians but not IM clinicians. Clinician experience-of-care results were mixed.

Conclusion: A triage hospitalist role is an effective approach for mitigating admission delays, with no evident adverse clinical consequences. A triage hospitalist alone was incapable of resolving ED crowding issues without a complementary focus on downstream bottlenecks.

Keywords: triage hospitalist, admission delay, quality improvement.

Excess time to admission (TTA), defined as the time between an emergency department (ED) admission request and internal medicine (IM) admission orders, contributes to ED crowding, which is associated with deleterious impacts on patient care and throughput. Prior research has correlated ED crowding with an increase in length of stay (LOS)1-3 and total inpatient cost,1 as well as increased inpatient mortality, higher left-without-being-seen rates,4 delays in clinically meaningful care,5,6 and poor patient and clinician satisfaction.6,7 While various solutions have been proposed to alleviate ED crowding,8 excess TTA is one aspect that IM can directly address.

Like many institutions, ours is challenged by ED crowding. Time to admission is a known bottleneck. Underlying factors that contribute to excess TTA include varied admission request volumes in relation to fixed admitting capacity; learner-focused admitting processes; and unreliable strategies for determining whether patients are eligible for ED observation, transfer to an alternative facility, or admission to an alternative primary service.

To address excess TTA, we piloted then implemented a triage hospitalist role, envisioned as responsible for evaluating ED admission requests to IM, making timely determinations of admission appropriateness, and distributing patients to admitting teams. This intervention was selected because of its strengths, including the ability to standardize admission processes, improve the proximity of clinical decision-makers to patient care to reduce delays, and decrease hierarchical imbalances experienced by trainees, and also because the institution expressed a willingness to mitigate its primary weakness (ie, ongoing financial support for sustainability) should it prove successful.

Previously, a triage hospitalist has been defined as “a physician who assesses patients for admission, actively supporting the transition of the patient from the outpatient to the inpatient setting.”9 Velásquez et al surveyed 10 academic medical centers and identified significant heterogeneity in the roles and responsibilities of a triage hospitalist.9 Limited research addresses the impact of this role on throughput. One report described the volume and source of requests evaluated by a triage hospitalist and the frequency with which the triage hospitalists’ assessment of admission appropriateness aligned with that of the referring clinicians.10 No prior research is available demonstrating the impact of this role on care quality, interprofessional practice, or clinician experience of care. This article is intended to address these gaps in the literature.

 

 

Methods

Setting

The University of New Mexico Hospital has 537 beds and is the only level-1 trauma and academic medical center in the state. On average, approximately 8000 patients register to be seen in the ED per month. Roughly 600 are admitted to IM per month. This study coincided with the COVID-19 pandemic, with low patient volumes in April 2020, overcapacity census starting in May 2020, and markedly high patient volumes in May/June 2020 and November/December 2020. All authors participated in project development, implementation, and analysis.

Preintervention IM Admission Process

When requesting IM admission, ED clinicians (resident, advanced practice provider [APP], or attending) contacted the IM triage person (typically an IM resident physician) by phone or in person. The IM triage person would then assess whether the patient needed critical care consultation (a unique and separate admission pathway), was eligible for ED observation or transfer to an outside hospital, or was clinically appropriate for IM subacute and floor admission. Pending admissions were evaluated in order of severity of illness or based on wait time if severity of illness was equal. Transfers from the intensive care unit (ICU) and referring hospitals were prioritized. Between 7:00 AM and 7:00 PM, patients were typically evaluated by junior team members, with subsequent presentation to an attending, at which time a final admission decision was made. At night, between 7:00 PM and 7:00 AM, 2 IM residents managed triage, admissions, and transfers with an on-call attending physician.

Triage Hospitalist Pilot

Key changes made during the pilot included scheduling an IM attending to serve as triage hospitalist for all IM admission requests from the ED between 7:00 AM and 7:00 PM; requiring that all IM admission requests be initiated by the ED attending and directed to the triage hospitalist; requiring ED attendings to enter into the electronic medical record (EMR) an admission request order (subsequently referred to as ED admission request [EDAR] order); and encouraging bedside handoffs. Eight pilot shifts were completed in November and December 2019.

Measures for Triage Hospitalist Pilot

Data collected included request type (new vs overflow from night) and patient details (name, medical record number). Two time points were recorded: when the EDAR order was entered and when admission orders were entered. Process indicators, including whether the EDAR order was entered and the final triage decision (eg, discharge, IM), were recorded. General feedback was requested at the end of each shift.

Phased Implementation of Triage Hospitalist Role

Triage hospitalist role implementation was approved following the pilot, with additional salary support funded by the institution. A new performance measure (time from admission request to admission order, self-identified goal < 3 hours) was approved by all parties.

In January 2020, the role was scheduled from 7:00 AM to 7:00 PM daily. All hospitalists participated. Based on pilot feedback, IM admission requests could be initiated by an ED attending or an ED APP. In addition to admissions from the ED, the triage hospitalist was tasked with managing ICU, subspecialty, and referring facility transfer requests, as well as staffing some admissions with residents.

In March 2020, to create a single communication pathway while simultaneously hardwiring our measurement strategy, the EDAR order was modified such that it would automatically prompt a 1-way communication to the triage hospitalist using the institution’s secure messaging software. The message included patient name, medical record number, location, ED attending, reason for admission, and consultation priority, as well as 2 questions prompting ED clinicians to reflect on the most common reasons for the triage hospitalist to recommend against IM admission (eligible for admission to other primary service, transfer to alternative hospital).

In July 2020, the triage hospitalist role was scheduled 24 hours a day, 7 days a week, to meet an institutional request. The schedule was divided into a daytime 7:00 AM to 3:00 PM shift, a 3:00 PM to 7:00 PM shift covered by a resident ward team IM attending with additional cross-cover responsibility, and a 7:00 PM to 7:00 AM shift covered by a nocturnist.

Measures for Triage Hospitalist Role

The primary outcome measure was TTA, defined as the time between EDAR (operationalized using EDAR order timestamp) and IM admission decision (operationalized using inpatient bed request order timestamp). Additional outcome measures included the Centers for Medicare & Medicaid Services Electronic Clinical Quality Measure ED-2 (eCQM ED-2), defined as the median time from admit decision to departure from the ED for patients admitted to inpatient status.

Process measures included time between patient arrival to the ED (operationalized using ED registration timestamp) and EDAR and percentage of IM admissions with an EDAR order. Balancing measures included time between bed request order (referred to as the IM admission order) and subsequent admission orders. While the IM admission order prompts an inpatient clinical encounter and inpatient bed assignment, subsequent admission orders are necessary for clinical care. Additional balancing measures included ICU transfer rate within the first 24 hours, referring facility transfer frequency to IM (an indicator of access for patients at outside hospitals), average hospital medicine LOS (operationalized using ED registration timestamp to discharge timestamp), and admission status (inpatient vs observation).

An anonymous preintervention (December 2019) and postintervention (August 2020) survey focusing on interprofessional practice and clinician experience of care was used to obtain feedback from ED and IM attendings, APPs, and trainees. Emergency department clinicians were asked questions pertaining to their IM colleagues and vice versa. A Likert 5-point scale was used to respond.

Data Analysis

The preintervention period was June 1, 2019, to October 31, 2019; the pilot period was November 1, 2019, to December 31, 2019; the staged implementation period was January 1, 2020, to June 30, 2020; and the postintervention period was July 1, 2020, to December 31, 2020. Run charts for outcome, process, and balancing measures were interpreted using rules for deriving statistically significant conclusions.11 Statistical analysis using a t test assuming unequal variances with P < . 05 to indicate statistical significance was applied to experience-of-care results. The study was approved by the Institutional Review Board.

 

 

Results

Triage Hospitalist Pilot Time Period

Seventy-four entries were recorded, 56 (75.7%) reflecting new admission requests. Average time between EDAR order and IM admission order was 40 minutes. The EDAR order was entered into the EMR without prompting in 22 (29.7%) cases. In 56 (75.7%) cases, the final triage decision was IM admission. Other dispositions included 3 discharges, 4 transfers, 3 alternative primary service admissions, 1 ED observation, and 7 triage deferrals pending additional workup or stabilization.

Feedback substantiated several benefits, including improved coordination among IM, ED, and consultant clinicians, as well as early admission of seriously ill patients. Feedback also confirmed several expected challenges, including evidence of communication lapses, difficulty with transfer coordinator integration, difficulty hardwiring elements of the verbal and bedside handoff, and perceived high cognitive load for the triage hospitalist. Several unexpected issues included whether ED APPs can request admission independently and how reconsultation is expected to occur if admission is initially deferred.

Triage Hospitalist Implementation Time Period

Time to admission decreased from a baseline pre-pilot average of 5 hours 19 minutes (median, 4 hours 45 minutes) to a postintervention average of 2 hours 8 minutes, with a statistically significant downward shift post intervention (Figure 1).

Time to admission (TTA) throughout pilot and staged implementation

ED-2 increased from a baseline average of 3 hours 40 minutes (median, 2 hours 39 minutes), with a statistically significant upward shift starting in May 2020 (Figure 2). Time between patient arrival to the ED and EDAR order decreased from a baseline average of 8 hours 47 minutes (median, 8 hours 37 minutes) to a postintervention average of 5 hours 57 minutes, with a statistically significant downward shift post intervention. Percentage of IM admissions with an EDAR order increased from a baseline average of 47% (median, 47%) to 97%, with a statistically significant upward shift starting in January 2020 (Figure 3).

ED-2 (median time elapsed from admit decision time to time of departure from the ED for patients admitted to inpatient status) from pre-intervention (July 2019) period through postintervention (December 2020).

There was no change in observed average time between IM admission order and subsequent admission orders pre and post intervention (16 minutes vs 18 minutes). However, there was a statistically significant shift up to an average of 40 minutes from January through June 2020, which then resolved. The percentage of patients transferred to the ICU within 24 hours of admission to IM did not change (1.1% pre vs 1.4% post intervention). Frequency of patients transferred in from a referring facility also did not change (26/month vs 22/month). Average hospital medicine LOS did not change to a statistically significant degree (6.48 days vs 6.62 days). The percentage of inpatient admissions relative to short stays increased from a baseline of 74.0% (median, 73.6%) to a postintervention average of 82.4%, with a statistically significant shift upward starting March 2020.

Percentage of internal medicine admissions with emergency department admission request (EDAR)

Regarding interprofessional practice and clinician experience of care, 122 of 309 preintervention surveys (39.5% response rate) and 98 of 309 postintervention surveys (31.7% response rate) were completed. Pre- and postintervention responses were not linked.

Regarding interprofessional practice, EM residents and EM attendings experienced statistically significant improvements in all interprofessional practice domains (Table 1). Emergency medicine APPs experienced statistically significant improvements post intervention with “I am satisfied with the level of communication with IM hospitalist clinicians” and “Interactions with IM hospitalist clinicians are collaborative” and nonstatistically significant improvement in “Interactions with IM hospitalist clinicians are professional” and “IM hospitalist clinicians treat me with respect.” All EM groups experienced a small but not statistically significant worsening for “Efficiency is more valued than good patient care.” Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains. Internal medicine attendings experienced nonstatistically significant improvements for “My interactions with ED clinicians are professional,” “EM clinicians treat me with respect,” and “Interactions with EM clinicians are collaborative,” but a nonstatistically significant worsening in “I am satisfied with level of communication with EM clinicians.” Internal medicine residents experienced a nonstatistically significant worsening in “Efficiency is more valued than good patient care,” while IM attendings experienced a nonstatistically significant improvement.

Results of Pre- and Postintervention Survey of Interprofessional Practice Perspectives

For clinician experience of care, EM residents (P < .001) and attendings (P < .001) experienced statistically significant improvements in “Patients are well informed and involved in the decision to admit,” whereas IM residents and attendings, as well as EM APPs, experienced nonstatistically significant improvements (Table 2). All groups except IM attendings experienced a statistically significant improvement (IM resident P = .011, EM resident P < .001, EM APP P = .001, EM attending P < .001) in “I believe that my patients are evaluated and treated within an appropriate time frame.” Internal medicine attendings felt that this indicator worsened to a nonstatistically significant degree. Post intervention, EM groups experienced a statistically significant worsening in “The process of admitting patients to a UNM IM hospitalist service is difficult,” while IM groups experienced a nonstatistically significant worsening.

Results of Pre- and Postintervention Survey of Clinician Experience of Care

 

 

Discussion

Implementation of the triage hospitalist role led to a significant reduction in average TTA, from 5 hours 19 minutes to 2 hours 8 minutes. Performance has been sustained at 1 hour 42 minutes on average over the past 6 months. The triage hospitalist was successful at reducing TTA because of their focus on evaluating new admission and transfer requests, deferring other admission responsibilities to on-call admitting teams. Early admission led to no increase in ICU transfers or hospitalist LOS. To ensure that earlier admission reflected improved timeliness of care and that new sources of delay were not being created, we measured the time between IM admission and subsequent admission orders. A statistically significant increase to 40 minutes from January through June 2020 was attributable to the hospitalist acclimating to their new role and the need to standardize workflow. This delay subsequently resolved. An additional benefit of the triage hospitalist was an increase in the proportion of inpatient admissions compared with short stays.

ED-2, an indicator of ED crowding, increased from 3 hours 40 minutes, with a statistically significant upward shift starting May 2020. Increasing ED-2 associated with the triage hospitalist role makes intuitive sense. Patients are admitted 2 hours 40 minutes earlier in their hospital course while downstream bottlenecks preventing patient movement to an inpatient bed remained unchanged. Unfortunately, the COVID-19 pandemic complicates interpretation of ED-2 because the measure reflects institutional capacity to match demand for inpatient beds. Fewer ED registrations and lower hospital medicine census (and resulting inpatient bed availability) in April 2020 during the first COVID-19 surge coincided with an ED-2 nadir of 1 hour 46 minutes. The statistically significant upward shift from May onward reflects ongoing and unprecedented patient volumes. It remains difficult to tease apart the presumed lesser contribution of the triage hospitalist role and presumed larger contribution of high patient volumes on ED-2 increases.

An important complementary change was linkage between the EDAR order and our secure messaging software, creating a single source of admission and transfer requests, prompting early ED clinician consideration of factors that could result in alternative disposition, and ensuring a sustainable data source for TTA. The order did not replace direct communication and included guidance for how triage hospitalists should connect with their ED colleagues. Percentage of IM admissions with the EDAR order increased to 97%. Fallouts are attributed to admissions from non-ED sources (eg, referring facility, endoscopy suite transfers). This communication strategy has been expanded as the primary mechanism of initiating consultation requests between IM and all consulting services.

This intervention was successful from the perspective of ED clinicians. Improvements can be attributed to the simplified admission process, timely patient assessment, a perception that patients are better informed of the decision to admit, and the ability to communicate with the triage hospitalist. Emergency medicine APPs may not have experienced similar improvements due to ongoing perceptions of a hierarchical imbalance. Unfortunately, the small but not statistically significant worsening perspective among ED clinicians that “efficiency is more valued than good patient care” and the statistically significant worsening perspective that “admitting patients to a UNM IM hospitalist service is difficult” may be due to the triage hospitalist responsibility for identifying the roughly 25% of patients who are safe for an alternative disposition.

Internal medicine clinicians experienced no significant changes in attitudes. Underlying causes are likely multifactorial and a focus of ongoing work. Internal medicine residents experienced statistically significant improvements for “I am satisfied with the level of communication with EM clinicians” and nonstatistically significant improvements for the other 3 domains, likely because the intervention enabled them to focus on clinical care rather than the administrative tasks and decision-making complexities inherent to the IM admission process. Internal medicine attendings reported a nonstatistically significant worsening in “I am satisfied with the level of communication with EM clinicians,” which is possibly attributable to challenges connecting with ED attendings after being notified that a new admission is pending. Unfortunately, bedside handoff was not hardwired and is done sporadically. Independent of the data, we believe that the triage hospitalist role has facilitated closer ED-IM relationships by aligning clinical priorities, standardizing processes, improving communication, and reducing sources of hierarchical imbalance and conflict. We expected IM attendings and residents to experience some degree of resolution of the perception that “efficiency is more valued than good patient care” because of the addition of a dedicated triage role. Our data also suggest that IM attendings are less likely to agree that “patients are evaluated and treated within an appropriate time frame.” Both concerns may be linked to the triage hospitalist facing multiple admission and transfer sources with variable arrival rates and variable patient complexity, resulting in high cognitive load and the perception that individual tasks are not completed to the best of their abilities.

To our knowledge, this is the first study assessing the impact of the triage hospitalist role on throughput, clinical care quality, interprofessional practice, and clinician experience of care. In the cross-sectional survey of 10 academic medical centers, 8 had defined triage roles filled by IM attendings, while the remainder had IM attendings supervising trainees.9 A complete picture of the prevalence and varying approaches of triage hospitalists models is unknown. Howell et al12 reported on an approach that reduced admission delays without a resulting increase in mortality or LOS. Our approach differed in several ways, with greater involvement of the triage hospitalist in determining a final admission decision, incorporation of EMR communication, and presence of existing throughput challenges preventing patients from moving seamlessly to an inpatient unit.

Conclusion

We believe this effort was successful for several reasons, including adherence to quality improvement best practices, such as engagement of stakeholders early on, the use of data to inform decision-making, the application of technology to hardwire process, and alignment with institutional priorities. Spread of this intervention will be limited by the financial investment required to start and maintain a triage hospitalist role. A primary limitation of this study is the confounding effect of the COVID-19 pandemic on our analysis. Next steps include identification of clinicians wishing to specialize in triage and expanding triage to include non-IM primary services. Additional research to optimize the triage hospitalist experience of care, as well as to measure improvements in patient-centered outcomes, is necessary.

Corresponding author: Christopher Bartlett, MD, MPH; MSC10 5550, 1 University of New Mexico, Albuquerque, NM 87131; CSBartlett@salud.unm.edu

Disclosures: None reported.

References

1. Huang Q, Thind A, Dreyer JF, et al. The impact of delays to admission from the emergency department on inpatient outcomes. BMC Emerg Med. 2010;10:16. doi:10.1186/1471-227X-10-16

2. Liew D, Liew D, Kennedy MP. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust. 2003;179:524-526. doi:10.5694/j.1326-5377.2003.tb05676.x

3. Richardson DB. The access-block effect: relationship between delay to reaching an inpatient bed and inpatient length of stay. Med J Aust. 2002;177:492-495. doi:10.5694/j.1326-5377.2002.tb04917.x

4. Polevoi SK, Quinn JV, Kramer KR. Factors associated with patients who leave without being seen. Acad Emerg Med. 2005;12:232-236. doi:10.1197/j.aem.2004.10.029

5. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16:1-10. doi:10.1111/j.1553-2712.2008.00295.x

6. Vieth TL, Rhodes KV. The effect of crowding on access and quality in an academic ED. Am J Emerg Med. 2006;24:787-794. doi:10.1016/j.ajem.2006.03.026

7. Rondeau KV, Francescutti LH. Emergency department overcrowding: the impact of resource scarcity on physician job satisfaction. J Healthc Manag. 2005;50:327-340; discussion 341-342.

8. Emergency Department Crowding: High Impact Solutions. American College of Emergency Physicians. Emergency Medicine Practice Committee. 2016. Accessed March 31, 2023. https://www.acep.org/globalassets/sites/acep/media/crowding/empc_crowding-ip_092016.pdf

9. Velásquez ST, Wang ES, White AW, et al. Hospitalists as triagists: description of the triagist role across academic medical centers. J Hosp Med. 2020;15:87-90. doi:10.12788/jhm.3327

10. Amick A, Bann M. Characterizing the role of the “triagist”: reasons for triage discordance and impact on disposition. J Gen Intern Med. 2021;36:2177-2179. doi:10.1007/s11606-020-05887-y

11. Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning for variation in healthcare processes. BMJ Qual Saf. 2011;20:46-51. doi:10.1136/bmjqs.2009.037895

12. Howell EE, Bessman ES, Rubin HR. Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19:266-268. doi:10.1111/j.1525-1497.2004.30431.x

References

1. Huang Q, Thind A, Dreyer JF, et al. The impact of delays to admission from the emergency department on inpatient outcomes. BMC Emerg Med. 2010;10:16. doi:10.1186/1471-227X-10-16

2. Liew D, Liew D, Kennedy MP. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust. 2003;179:524-526. doi:10.5694/j.1326-5377.2003.tb05676.x

3. Richardson DB. The access-block effect: relationship between delay to reaching an inpatient bed and inpatient length of stay. Med J Aust. 2002;177:492-495. doi:10.5694/j.1326-5377.2002.tb04917.x

4. Polevoi SK, Quinn JV, Kramer KR. Factors associated with patients who leave without being seen. Acad Emerg Med. 2005;12:232-236. doi:10.1197/j.aem.2004.10.029

5. Bernstein SL, Aronsky D, Duseja R, et al. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med. 2009;16:1-10. doi:10.1111/j.1553-2712.2008.00295.x

6. Vieth TL, Rhodes KV. The effect of crowding on access and quality in an academic ED. Am J Emerg Med. 2006;24:787-794. doi:10.1016/j.ajem.2006.03.026

7. Rondeau KV, Francescutti LH. Emergency department overcrowding: the impact of resource scarcity on physician job satisfaction. J Healthc Manag. 2005;50:327-340; discussion 341-342.

8. Emergency Department Crowding: High Impact Solutions. American College of Emergency Physicians. Emergency Medicine Practice Committee. 2016. Accessed March 31, 2023. https://www.acep.org/globalassets/sites/acep/media/crowding/empc_crowding-ip_092016.pdf

9. Velásquez ST, Wang ES, White AW, et al. Hospitalists as triagists: description of the triagist role across academic medical centers. J Hosp Med. 2020;15:87-90. doi:10.12788/jhm.3327

10. Amick A, Bann M. Characterizing the role of the “triagist”: reasons for triage discordance and impact on disposition. J Gen Intern Med. 2021;36:2177-2179. doi:10.1007/s11606-020-05887-y

11. Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning for variation in healthcare processes. BMJ Qual Saf. 2011;20:46-51. doi:10.1136/bmjqs.2009.037895

12. Howell EE, Bessman ES, Rubin HR. Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19:266-268. doi:10.1111/j.1525-1497.2004.30431.x

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Redesign of Health Care Systems to Reduce Diagnostic Errors: Leveraging Human Experience and Artificial Intelligence

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Redesign of Health Care Systems to Reduce Diagnostic Errors: Leveraging Human Experience and Artificial Intelligence

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.1 According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.1 Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.1 Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.2 While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.1

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.5 Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.6 The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.1 Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.7 However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.8 To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.9 Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.11 Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.12

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.2 Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.3 Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.15 Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.15 By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Leveraging human experience and artificial intelligence to redesign the health care system for safer diagnosis.

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.17 Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.18 Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.18

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.19 This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.16 This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.17 One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.20 Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.21 A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.22 This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.23

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.24 Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.25

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

References

1. National Academy of Medicine. Improving Diagnosis in Health Care. Balogh EP, Miller BT, Ball JR, eds. National Academies Press; 2015. doi:10.17226/21794

2. Singh H, Schiff GD, Graber ML, et al. The global burden of diagnostic errors in primary care. BMJ Qual Saf. 2017;26(6):484-494. doi:10.1136/bmjqs-2016-005401

3. Croskerry P, Campbell SG, Petrie DA. The challenge of cognitive science for medical diagnosis. Cogn Res Princ Implic. 2023;8(1):13. doi:10.1186/s41235-022-00460-z

4. Dahm MR, Williams M, Crock C. ‘More than words’ - interpersonal communication, cogntive bias and diagnostic errors. Patient Educ Couns. 2022;105(1):252-256. doi:10.1016/j.pec.2021.05.012

5. Wolf JA, Niederhauser V, Marshburn D, LaVela SL. Reexamining “defining patient experience”: The human experience in Healthcare. Patient Experience J. 2021;8(1):16-29. doi:10.35680/2372-0247.1594

6. Sacco AY, Self QR, Worswick EL, et al. Patients’ perspectives of diagnostic error: A qualitative study. J Patient Saf. 2021;17(8):e1759-e1764. doi:10.1097/PTS.0000000000000642

7. Singh H, Graber ML. Improving diagnosis in health care—the next imperative for patient safety. N Engl J Med. 2015;373(26):2493-2495. doi:10.1056/NEJMp1512241

8. Austin E, LeRouge C, Hartzler AL, Segal C, Lavallee DC. Capturing the patient voice: implementing patient-reported outcomes across the health system. Qual Life Res. 2020;29(2):347-355. doi:10.1007/s11136-019-02320-8

9. Waddell A, Lennox A, Spassova G, Bragge P. Barriers and facilitators to shared decision-making in hospitals from policy to practice: a systematic review. Implement Sci. 2021;16(1):74. doi: 10.1186/s13012-021-01142-y

10. US Preventive Services Task Force. Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US Preventive Services Task Force Recommendations. JAMA. 2022;327(12):1171-1176. doi:10.1001/jama.2022.3267

11. Reporting patient safety events. Patient Safety Network. Published September 7, 2019. Accessed April 29, 2023. https://psnet.ahrq.gov/primer/reporting-patient-safety-events

12. McLaney E, Morassaei S, Hughes L, et al. A framework for interprofessional team collaboration in a hospital setting: Advancing team competencies and behaviours. Healthc Manage Forum. 2022;35(2):112-117. doi:10.1177/08404704211063584

13. Abid MH, Abid MM, Shahid R, et al. Patient and family engagement during challenging times: what works and what does not? Cureus. 2021;13(5):e14814. doi:10.7759/cureus.14814

14. Abimanyi-Ochom J, Bohingamu Mudiyanselage S, Catchpool M, et al. Strategies to reduce diagnostic errors: a systematic review. BMC Med Inform Decis Mak. 2019;19(1):174. doi:10.1186/s12911-019-0901-1

15. Watari T, Tokuda Y, Amano Y, et al. Cognitive bias and diagnostic errors among physicians in Japan: A self-reflection survey. Int J Environ Res Public Health. 2022;19(8):4645. doi:10.3390/ijerph19084645

16. Rajkomar A, Oren E, Chen K et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1

17. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94

18. Dymek C, Kim B, Melton GB, et al. Building the evidence-base to reduce electronic health record-related clinician burden. J Am Med Inform Assoc. 2021;28(5):1057-1061. doi:10.1093/jamia/ocaa238

19. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318. doi:10.1001/jama.2017.18391

20. Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828-1837. doi:10.1001/jamainternmed.2015.5231

21. Liao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885. doi:10.1136/bmj.h1885

22. Irving J, Patel R, Oliver D, et al. Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk. Schizophr Bull. 2021;47(2):405-414. doi:10.1093/schbul/sbaa126. Erratum in: Schizophr Bull. 2021;47(2):575.

23. Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234. doi:10.1136/bmjresp-2017-000234

24. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342

25. Ibrahim SA, Pronovost PJ. Diagnostic errors, health disparities, and artificial intelligence: a combination for health or harm? JAMA Health Forum. 2021;2(9):e212430. doi:10.1001/jamahealthforum.2021.2430

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From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.1 According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.1 Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.1 Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.2 While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.1

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.5 Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.6 The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.1 Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.7 However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.8 To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.9 Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.11 Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.12

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.2 Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.3 Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.15 Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.15 By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Leveraging human experience and artificial intelligence to redesign the health care system for safer diagnosis.

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.17 Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.18 Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.18

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.19 This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.16 This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.17 One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.20 Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.21 A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.22 This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.23

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.24 Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.25

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.1 According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.1 Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.1 Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.2 While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.1

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.5 Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.6 The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.1 Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.7 However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.8 To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.9 Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.11 Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.12

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.2 Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.3 Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.15 Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.15 By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

Leveraging human experience and artificial intelligence to redesign the health care system for safer diagnosis.

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.17 Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.18 Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.18

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.19 This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.16 This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.17 One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.20 Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.21 A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.22 This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.23

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.24 Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.25

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

References

1. National Academy of Medicine. Improving Diagnosis in Health Care. Balogh EP, Miller BT, Ball JR, eds. National Academies Press; 2015. doi:10.17226/21794

2. Singh H, Schiff GD, Graber ML, et al. The global burden of diagnostic errors in primary care. BMJ Qual Saf. 2017;26(6):484-494. doi:10.1136/bmjqs-2016-005401

3. Croskerry P, Campbell SG, Petrie DA. The challenge of cognitive science for medical diagnosis. Cogn Res Princ Implic. 2023;8(1):13. doi:10.1186/s41235-022-00460-z

4. Dahm MR, Williams M, Crock C. ‘More than words’ - interpersonal communication, cogntive bias and diagnostic errors. Patient Educ Couns. 2022;105(1):252-256. doi:10.1016/j.pec.2021.05.012

5. Wolf JA, Niederhauser V, Marshburn D, LaVela SL. Reexamining “defining patient experience”: The human experience in Healthcare. Patient Experience J. 2021;8(1):16-29. doi:10.35680/2372-0247.1594

6. Sacco AY, Self QR, Worswick EL, et al. Patients’ perspectives of diagnostic error: A qualitative study. J Patient Saf. 2021;17(8):e1759-e1764. doi:10.1097/PTS.0000000000000642

7. Singh H, Graber ML. Improving diagnosis in health care—the next imperative for patient safety. N Engl J Med. 2015;373(26):2493-2495. doi:10.1056/NEJMp1512241

8. Austin E, LeRouge C, Hartzler AL, Segal C, Lavallee DC. Capturing the patient voice: implementing patient-reported outcomes across the health system. Qual Life Res. 2020;29(2):347-355. doi:10.1007/s11136-019-02320-8

9. Waddell A, Lennox A, Spassova G, Bragge P. Barriers and facilitators to shared decision-making in hospitals from policy to practice: a systematic review. Implement Sci. 2021;16(1):74. doi: 10.1186/s13012-021-01142-y

10. US Preventive Services Task Force. Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US Preventive Services Task Force Recommendations. JAMA. 2022;327(12):1171-1176. doi:10.1001/jama.2022.3267

11. Reporting patient safety events. Patient Safety Network. Published September 7, 2019. Accessed April 29, 2023. https://psnet.ahrq.gov/primer/reporting-patient-safety-events

12. McLaney E, Morassaei S, Hughes L, et al. A framework for interprofessional team collaboration in a hospital setting: Advancing team competencies and behaviours. Healthc Manage Forum. 2022;35(2):112-117. doi:10.1177/08404704211063584

13. Abid MH, Abid MM, Shahid R, et al. Patient and family engagement during challenging times: what works and what does not? Cureus. 2021;13(5):e14814. doi:10.7759/cureus.14814

14. Abimanyi-Ochom J, Bohingamu Mudiyanselage S, Catchpool M, et al. Strategies to reduce diagnostic errors: a systematic review. BMC Med Inform Decis Mak. 2019;19(1):174. doi:10.1186/s12911-019-0901-1

15. Watari T, Tokuda Y, Amano Y, et al. Cognitive bias and diagnostic errors among physicians in Japan: A self-reflection survey. Int J Environ Res Public Health. 2022;19(8):4645. doi:10.3390/ijerph19084645

16. Rajkomar A, Oren E, Chen K et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1

17. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94

18. Dymek C, Kim B, Melton GB, et al. Building the evidence-base to reduce electronic health record-related clinician burden. J Am Med Inform Assoc. 2021;28(5):1057-1061. doi:10.1093/jamia/ocaa238

19. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318. doi:10.1001/jama.2017.18391

20. Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828-1837. doi:10.1001/jamainternmed.2015.5231

21. Liao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885. doi:10.1136/bmj.h1885

22. Irving J, Patel R, Oliver D, et al. Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk. Schizophr Bull. 2021;47(2):405-414. doi:10.1093/schbul/sbaa126. Erratum in: Schizophr Bull. 2021;47(2):575.

23. Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234. doi:10.1136/bmjresp-2017-000234

24. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342

25. Ibrahim SA, Pronovost PJ. Diagnostic errors, health disparities, and artificial intelligence: a combination for health or harm? JAMA Health Forum. 2021;2(9):e212430. doi:10.1001/jamahealthforum.2021.2430

References

1. National Academy of Medicine. Improving Diagnosis in Health Care. Balogh EP, Miller BT, Ball JR, eds. National Academies Press; 2015. doi:10.17226/21794

2. Singh H, Schiff GD, Graber ML, et al. The global burden of diagnostic errors in primary care. BMJ Qual Saf. 2017;26(6):484-494. doi:10.1136/bmjqs-2016-005401

3. Croskerry P, Campbell SG, Petrie DA. The challenge of cognitive science for medical diagnosis. Cogn Res Princ Implic. 2023;8(1):13. doi:10.1186/s41235-022-00460-z

4. Dahm MR, Williams M, Crock C. ‘More than words’ - interpersonal communication, cogntive bias and diagnostic errors. Patient Educ Couns. 2022;105(1):252-256. doi:10.1016/j.pec.2021.05.012

5. Wolf JA, Niederhauser V, Marshburn D, LaVela SL. Reexamining “defining patient experience”: The human experience in Healthcare. Patient Experience J. 2021;8(1):16-29. doi:10.35680/2372-0247.1594

6. Sacco AY, Self QR, Worswick EL, et al. Patients’ perspectives of diagnostic error: A qualitative study. J Patient Saf. 2021;17(8):e1759-e1764. doi:10.1097/PTS.0000000000000642

7. Singh H, Graber ML. Improving diagnosis in health care—the next imperative for patient safety. N Engl J Med. 2015;373(26):2493-2495. doi:10.1056/NEJMp1512241

8. Austin E, LeRouge C, Hartzler AL, Segal C, Lavallee DC. Capturing the patient voice: implementing patient-reported outcomes across the health system. Qual Life Res. 2020;29(2):347-355. doi:10.1007/s11136-019-02320-8

9. Waddell A, Lennox A, Spassova G, Bragge P. Barriers and facilitators to shared decision-making in hospitals from policy to practice: a systematic review. Implement Sci. 2021;16(1):74. doi: 10.1186/s13012-021-01142-y

10. US Preventive Services Task Force. Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US Preventive Services Task Force Recommendations. JAMA. 2022;327(12):1171-1176. doi:10.1001/jama.2022.3267

11. Reporting patient safety events. Patient Safety Network. Published September 7, 2019. Accessed April 29, 2023. https://psnet.ahrq.gov/primer/reporting-patient-safety-events

12. McLaney E, Morassaei S, Hughes L, et al. A framework for interprofessional team collaboration in a hospital setting: Advancing team competencies and behaviours. Healthc Manage Forum. 2022;35(2):112-117. doi:10.1177/08404704211063584

13. Abid MH, Abid MM, Shahid R, et al. Patient and family engagement during challenging times: what works and what does not? Cureus. 2021;13(5):e14814. doi:10.7759/cureus.14814

14. Abimanyi-Ochom J, Bohingamu Mudiyanselage S, Catchpool M, et al. Strategies to reduce diagnostic errors: a systematic review. BMC Med Inform Decis Mak. 2019;19(1):174. doi:10.1186/s12911-019-0901-1

15. Watari T, Tokuda Y, Amano Y, et al. Cognitive bias and diagnostic errors among physicians in Japan: A self-reflection survey. Int J Environ Res Public Health. 2022;19(8):4645. doi:10.3390/ijerph19084645

16. Rajkomar A, Oren E, Chen K et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1

17. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi:10.7861/futurehosp.6-2-94

18. Dymek C, Kim B, Melton GB, et al. Building the evidence-base to reduce electronic health record-related clinician burden. J Am Med Inform Assoc. 2021;28(5):1057-1061. doi:10.1093/jamia/ocaa238

19. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318. doi:10.1001/jama.2017.18391

20. Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175(11):1828-1837. doi:10.1001/jamainternmed.2015.5231

21. Liao KP, Cai T, Savova GK, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ. 2015;350:h1885. doi:10.1136/bmj.h1885

22. Irving J, Patel R, Oliver D, et al. Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk. Schizophr Bull. 2021;47(2):405-414. doi:10.1093/schbul/sbaa126. Erratum in: Schizophr Bull. 2021;47(2):575.

23. Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4(1):e000234. doi:10.1136/bmjresp-2017-000234

24. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342

25. Ibrahim SA, Pronovost PJ. Diagnostic errors, health disparities, and artificial intelligence: a combination for health or harm? JAMA Health Forum. 2021;2(9):e212430. doi:10.1001/jamahealthforum.2021.2430

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