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Rates, predictors, and variability of interhospital transfers
Clinical question: What is the national frequency of interhospital transfers, and are there any patient or hospital factors that predict these transfers?
Background: Interhospital patient transfers may be due to the need for a specialized service, but the factors and patterns have not been well studied.
Setting: All acute care hospitals in the United States.
Synopsis: Using data from the 2013 Centers for Medicare & Medicaid Services and the 2013 American Hospital Association, this study showed that 1.5% of the 6.6 million eligible beneficiaries underwent interhospital transfer (IHT). Patient and hospital characteristics that increased the odds of IHT included age 74-85 years, nonblack race, higher comorbidity, lower diagnosis-related group weight, fewer recent hospitalizations, and hospitalization in the Northeast region of the United States. Lower case mix index was associated with increased odds of IHT. Rates of IHT remain variable, after adjusting for patient and hospital characteristics. This study was restricted to the Medicare population so did not represent all populations. IHT from the emergency room was not assessed, and those who were transferred more than once (to another hospital and back) were not included.
Bottom line: A large number of Medicare patients undergo IHT nationally, and the rate varies widely based on patient factors, geography, and other factors unrelated to patient or hospital characteristics.
Citation: Mueller SK, Jie Zheng, Orav EJ, Schnipper JL. Rates, predictors, and variability of interhospital transfers: A national evaluation. J Hosp Med. 2017;6:435-42.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Clinical question: What is the national frequency of interhospital transfers, and are there any patient or hospital factors that predict these transfers?
Background: Interhospital patient transfers may be due to the need for a specialized service, but the factors and patterns have not been well studied.
Setting: All acute care hospitals in the United States.
Synopsis: Using data from the 2013 Centers for Medicare & Medicaid Services and the 2013 American Hospital Association, this study showed that 1.5% of the 6.6 million eligible beneficiaries underwent interhospital transfer (IHT). Patient and hospital characteristics that increased the odds of IHT included age 74-85 years, nonblack race, higher comorbidity, lower diagnosis-related group weight, fewer recent hospitalizations, and hospitalization in the Northeast region of the United States. Lower case mix index was associated with increased odds of IHT. Rates of IHT remain variable, after adjusting for patient and hospital characteristics. This study was restricted to the Medicare population so did not represent all populations. IHT from the emergency room was not assessed, and those who were transferred more than once (to another hospital and back) were not included.
Bottom line: A large number of Medicare patients undergo IHT nationally, and the rate varies widely based on patient factors, geography, and other factors unrelated to patient or hospital characteristics.
Citation: Mueller SK, Jie Zheng, Orav EJ, Schnipper JL. Rates, predictors, and variability of interhospital transfers: A national evaluation. J Hosp Med. 2017;6:435-42.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Clinical question: What is the national frequency of interhospital transfers, and are there any patient or hospital factors that predict these transfers?
Background: Interhospital patient transfers may be due to the need for a specialized service, but the factors and patterns have not been well studied.
Setting: All acute care hospitals in the United States.
Synopsis: Using data from the 2013 Centers for Medicare & Medicaid Services and the 2013 American Hospital Association, this study showed that 1.5% of the 6.6 million eligible beneficiaries underwent interhospital transfer (IHT). Patient and hospital characteristics that increased the odds of IHT included age 74-85 years, nonblack race, higher comorbidity, lower diagnosis-related group weight, fewer recent hospitalizations, and hospitalization in the Northeast region of the United States. Lower case mix index was associated with increased odds of IHT. Rates of IHT remain variable, after adjusting for patient and hospital characteristics. This study was restricted to the Medicare population so did not represent all populations. IHT from the emergency room was not assessed, and those who were transferred more than once (to another hospital and back) were not included.
Bottom line: A large number of Medicare patients undergo IHT nationally, and the rate varies widely based on patient factors, geography, and other factors unrelated to patient or hospital characteristics.
Citation: Mueller SK, Jie Zheng, Orav EJ, Schnipper JL. Rates, predictors, and variability of interhospital transfers: A national evaluation. J Hosp Med. 2017;6:435-42.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Mortality risks associated with emergency admission during weekends and public holidays
Clinical question: What factors contribute to increased mortality in weekend hospital admissions?
Background: The “weekend effect” is a commonly known phenomenon, where patients admitted to the hospital on weekends have higher mortality risk than those admitted on weekdays. However, little is known about the factors contributing to the excess mortality associated with weekend admissions.
Setting: Four Oxford University National Health Service hospitals in the United Kingdom (a district general hospital, a large teaching hospital, a specialist orthopedic hospital, and a major cancer center).
Synopsis: Data from the Infections in Oxfordshire Research Database of 503,938 admissions between Jan. 1, 2006, and Dec. 31, 2014 were analyzed. Thirty-day mortality was 4.7%, 5.1%, and 5.8% for patients admitted during weekdays, weekends, and public holidays, respectively (P less than .0001). Fifteen routine hematology and biochemistry test results were determined to be prognostic of high mortality risk. Adjustment for these routine test results reduced excess mortality associated with emergency admissions on weekends and public holidays. Excess mortality was notable for patients admitted on Saturdays and Sundays between 11:00 a.m. and 3:00 p.m. Hospital staffing and workload were not associated with excess mortality. The study is limited by a lack of additional patient factors such as vital signs and blood gas results that may further explain excess mortality on weekends and public holidays.
Bottom line: Patient factors, including laboratory abnormalities, rather than hospital workload and staffing may be the major contributing factors for the excess mortality seen for emergency admissions on weekends and public holidays.
Citation: Walker AS, Mason A, Quan TP, et al. Mortality risks associated with emergency admissions during weekends and public holidays: An analysis of electronic health records. The Lancet. 2017;390(10089):62-72.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Clinical question: What factors contribute to increased mortality in weekend hospital admissions?
Background: The “weekend effect” is a commonly known phenomenon, where patients admitted to the hospital on weekends have higher mortality risk than those admitted on weekdays. However, little is known about the factors contributing to the excess mortality associated with weekend admissions.
Setting: Four Oxford University National Health Service hospitals in the United Kingdom (a district general hospital, a large teaching hospital, a specialist orthopedic hospital, and a major cancer center).
Synopsis: Data from the Infections in Oxfordshire Research Database of 503,938 admissions between Jan. 1, 2006, and Dec. 31, 2014 were analyzed. Thirty-day mortality was 4.7%, 5.1%, and 5.8% for patients admitted during weekdays, weekends, and public holidays, respectively (P less than .0001). Fifteen routine hematology and biochemistry test results were determined to be prognostic of high mortality risk. Adjustment for these routine test results reduced excess mortality associated with emergency admissions on weekends and public holidays. Excess mortality was notable for patients admitted on Saturdays and Sundays between 11:00 a.m. and 3:00 p.m. Hospital staffing and workload were not associated with excess mortality. The study is limited by a lack of additional patient factors such as vital signs and blood gas results that may further explain excess mortality on weekends and public holidays.
Bottom line: Patient factors, including laboratory abnormalities, rather than hospital workload and staffing may be the major contributing factors for the excess mortality seen for emergency admissions on weekends and public holidays.
Citation: Walker AS, Mason A, Quan TP, et al. Mortality risks associated with emergency admissions during weekends and public holidays: An analysis of electronic health records. The Lancet. 2017;390(10089):62-72.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Clinical question: What factors contribute to increased mortality in weekend hospital admissions?
Background: The “weekend effect” is a commonly known phenomenon, where patients admitted to the hospital on weekends have higher mortality risk than those admitted on weekdays. However, little is known about the factors contributing to the excess mortality associated with weekend admissions.
Setting: Four Oxford University National Health Service hospitals in the United Kingdom (a district general hospital, a large teaching hospital, a specialist orthopedic hospital, and a major cancer center).
Synopsis: Data from the Infections in Oxfordshire Research Database of 503,938 admissions between Jan. 1, 2006, and Dec. 31, 2014 were analyzed. Thirty-day mortality was 4.7%, 5.1%, and 5.8% for patients admitted during weekdays, weekends, and public holidays, respectively (P less than .0001). Fifteen routine hematology and biochemistry test results were determined to be prognostic of high mortality risk. Adjustment for these routine test results reduced excess mortality associated with emergency admissions on weekends and public holidays. Excess mortality was notable for patients admitted on Saturdays and Sundays between 11:00 a.m. and 3:00 p.m. Hospital staffing and workload were not associated with excess mortality. The study is limited by a lack of additional patient factors such as vital signs and blood gas results that may further explain excess mortality on weekends and public holidays.
Bottom line: Patient factors, including laboratory abnormalities, rather than hospital workload and staffing may be the major contributing factors for the excess mortality seen for emergency admissions on weekends and public holidays.
Citation: Walker AS, Mason A, Quan TP, et al. Mortality risks associated with emergency admissions during weekends and public holidays: An analysis of electronic health records. The Lancet. 2017;390(10089):62-72.
Dr. Xu is assistant professor and hospitalist, Icahn School of Medicine of the Mount Sinai Health System, New York.
Improving transitions for elderly patients
Transitions are always a time of concern for hospitalists, and the transition from hospital to skilled nursing facilities (SNF) is no exception.
“During the transition and in the 30 days after discharge from the hospital to a SNF, patients are at high risk for death, rehospitalization, and high-cost health care,” said Amber Moore, MD, MPH, a hospitalist at Beth Israel Deaconess Medical Center, and instructor of medicine, Harvard Medical School. “Elderly adults are especially vulnerable because of impairments that may prevent them from participating in the discharge process and an increase in the risk that information is lost or incomplete during the care transition.” 
To address this, she and several other physicians studied a novel video-conference program called Extension for Community Health Outcomes–Care Transitions (ECHO-CT) that connects an interdisciplinary hospital-based team with clinicians at SNFs to help reduce patient mortality, hospital readmission, skilled nursing facility length of stay, and 30-day health care costs.
The results of their study suggest that this intervention significantly decreased SNF length of stay, readmission rate, and costs of care, she says; the model they used is reproducible and has the potential to significantly improve care of these patients. “Our model was hospitalist run and is a mechanism to help hospitalists improve care to their patients during the transition time and beyond,” Dr. Moore said. “Furthermore, in participating in this model, hospitalists have the opportunity to better understand the challenges that face their patients after discharge and learn from postacute care providers.”
Ideally, she would like to see the model spread to other hospitals; she says hospitalists are well positioned to set up this program at their institution. “I also hope that our study highlights the incredible opportunity for improvement in the care of patients during transition from hospital to SNF and encourages hospitalists to look for innovative ways to improve care at this transition,” she said.
Reference
Moore AB, Krupp JE, Dufour AB, et al. Improving transitions to post-acute care for elderly patients using a novel video-conferencing program: ECHO-Care transitions. Am J Med. 2017 Oct;130(10):1199-204. Accessed June 6, 2017.
Transitions are always a time of concern for hospitalists, and the transition from hospital to skilled nursing facilities (SNF) is no exception.
“During the transition and in the 30 days after discharge from the hospital to a SNF, patients are at high risk for death, rehospitalization, and high-cost health care,” said Amber Moore, MD, MPH, a hospitalist at Beth Israel Deaconess Medical Center, and instructor of medicine, Harvard Medical School. “Elderly adults are especially vulnerable because of impairments that may prevent them from participating in the discharge process and an increase in the risk that information is lost or incomplete during the care transition.”
To address this, she and several other physicians studied a novel video-conference program called Extension for Community Health Outcomes–Care Transitions (ECHO-CT) that connects an interdisciplinary hospital-based team with clinicians at SNFs to help reduce patient mortality, hospital readmission, skilled nursing facility length of stay, and 30-day health care costs.
The results of their study suggest that this intervention significantly decreased SNF length of stay, readmission rate, and costs of care, she says; the model they used is reproducible and has the potential to significantly improve care of these patients. “Our model was hospitalist run and is a mechanism to help hospitalists improve care to their patients during the transition time and beyond,” Dr. Moore said. “Furthermore, in participating in this model, hospitalists have the opportunity to better understand the challenges that face their patients after discharge and learn from postacute care providers.”
Ideally, she would like to see the model spread to other hospitals; she says hospitalists are well positioned to set up this program at their institution. “I also hope that our study highlights the incredible opportunity for improvement in the care of patients during transition from hospital to SNF and encourages hospitalists to look for innovative ways to improve care at this transition,” she said.
Reference
Moore AB, Krupp JE, Dufour AB, et al. Improving transitions to post-acute care for elderly patients using a novel video-conferencing program: ECHO-Care transitions. Am J Med. 2017 Oct;130(10):1199-204. Accessed June 6, 2017.
Transitions are always a time of concern for hospitalists, and the transition from hospital to skilled nursing facilities (SNF) is no exception.
“During the transition and in the 30 days after discharge from the hospital to a SNF, patients are at high risk for death, rehospitalization, and high-cost health care,” said Amber Moore, MD, MPH, a hospitalist at Beth Israel Deaconess Medical Center, and instructor of medicine, Harvard Medical School. “Elderly adults are especially vulnerable because of impairments that may prevent them from participating in the discharge process and an increase in the risk that information is lost or incomplete during the care transition.”
To address this, she and several other physicians studied a novel video-conference program called Extension for Community Health Outcomes–Care Transitions (ECHO-CT) that connects an interdisciplinary hospital-based team with clinicians at SNFs to help reduce patient mortality, hospital readmission, skilled nursing facility length of stay, and 30-day health care costs.
The results of their study suggest that this intervention significantly decreased SNF length of stay, readmission rate, and costs of care, she says; the model they used is reproducible and has the potential to significantly improve care of these patients. “Our model was hospitalist run and is a mechanism to help hospitalists improve care to their patients during the transition time and beyond,” Dr. Moore said. “Furthermore, in participating in this model, hospitalists have the opportunity to better understand the challenges that face their patients after discharge and learn from postacute care providers.”
Ideally, she would like to see the model spread to other hospitals; she says hospitalists are well positioned to set up this program at their institution. “I also hope that our study highlights the incredible opportunity for improvement in the care of patients during transition from hospital to SNF and encourages hospitalists to look for innovative ways to improve care at this transition,” she said.
Reference
Moore AB, Krupp JE, Dufour AB, et al. Improving transitions to post-acute care for elderly patients using a novel video-conferencing program: ECHO-Care transitions. Am J Med. 2017 Oct;130(10):1199-204. Accessed June 6, 2017.
Identifying the right database
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Sneak Peek: The Hospital Leader blog – Oct. 2017
You Have Lowered Length of Stay. Congratulations: You’re Fired.
For several decades, providers working within hospitals have had incentives to reduce stay durations and keep patient flow tip-top. Diagnosis Related Group (DRG)–based and capitated payments expedited that shift.
Accompanying the change, physicians became more aware of the potential repercussions of sicker and quicker discharges. They began to monitor their care and, as best as possible, use what measures they could as a proxy for quality (readmissions and hospital-acquired conditions). Providers balanced the harms of a continued stay with the benefits of added days, not to mention the need for cost savings.
I recognize this because of the cognitive dissonance providers now experience because of the mixed messages delivered by hospital leaders.
On the one hand, the DRG-driven system that we have binds the hospital’s bottom line – and that is not going away. On the other, we are paying more attention to excessive costs in post-acute settings, that is, subacute facilities when home health will do or more intense acute rehabilitation rather than the subacute route.
Making determinations as to whether a certain course is proper, whether a patient will be safe, whether families can provide adequate agency and backing, and whether we can avail community services takes time. Sicker and quicker; mindful of short-term outcomes; worked when we had postdischarge blinders on. As we remove such obstacles, and payment incentives change to cover broader intervals of time, we have to adapt. And that means leadership must realize that the practices that held hospitals in sound financial stead in years past are heading toward extinction – or, at best, falling out of favor.
Compare the costs of routine hospital care with the added expense of post-acute care, then multiply that extra expense times an aging, dependent population, and you add billions of dollars to the recovery tab. Some of these expenses are necessary, and some are not; a stay at a skilled nursing facility, for example, doubles the cost of an episode.
Read the full post at hospitalleader.org.
Also on The Hospital Leader …
- Why 7 On/7 Off Doesn’t Meet the Needs of Long-Stay Hospital Patients by Lauren Doctoroff, MD, FHM
- Is It Time for Health Policy M&Ms? by Chris Moriates, MD
- George Carlin Predicts Hospital Planning Strategy by Jordan Messler, MD, SFHM
- Many Paths to a Richer Job by Leslie Flores, MHA, MPH, SFHM
- A New Face for Online Modules by Chris Moriates, MD
You Have Lowered Length of Stay. Congratulations: You’re Fired.
For several decades, providers working within hospitals have had incentives to reduce stay durations and keep patient flow tip-top. Diagnosis Related Group (DRG)–based and capitated payments expedited that shift.
Accompanying the change, physicians became more aware of the potential repercussions of sicker and quicker discharges. They began to monitor their care and, as best as possible, use what measures they could as a proxy for quality (readmissions and hospital-acquired conditions). Providers balanced the harms of a continued stay with the benefits of added days, not to mention the need for cost savings.
I recognize this because of the cognitive dissonance providers now experience because of the mixed messages delivered by hospital leaders.
On the one hand, the DRG-driven system that we have binds the hospital’s bottom line – and that is not going away. On the other, we are paying more attention to excessive costs in post-acute settings, that is, subacute facilities when home health will do or more intense acute rehabilitation rather than the subacute route.
Making determinations as to whether a certain course is proper, whether a patient will be safe, whether families can provide adequate agency and backing, and whether we can avail community services takes time. Sicker and quicker; mindful of short-term outcomes; worked when we had postdischarge blinders on. As we remove such obstacles, and payment incentives change to cover broader intervals of time, we have to adapt. And that means leadership must realize that the practices that held hospitals in sound financial stead in years past are heading toward extinction – or, at best, falling out of favor.
Compare the costs of routine hospital care with the added expense of post-acute care, then multiply that extra expense times an aging, dependent population, and you add billions of dollars to the recovery tab. Some of these expenses are necessary, and some are not; a stay at a skilled nursing facility, for example, doubles the cost of an episode.
Read the full post at hospitalleader.org.
Also on The Hospital Leader …
- Why 7 On/7 Off Doesn’t Meet the Needs of Long-Stay Hospital Patients by Lauren Doctoroff, MD, FHM
- Is It Time for Health Policy M&Ms? by Chris Moriates, MD
- George Carlin Predicts Hospital Planning Strategy by Jordan Messler, MD, SFHM
- Many Paths to a Richer Job by Leslie Flores, MHA, MPH, SFHM
- A New Face for Online Modules by Chris Moriates, MD
You Have Lowered Length of Stay. Congratulations: You’re Fired.
For several decades, providers working within hospitals have had incentives to reduce stay durations and keep patient flow tip-top. Diagnosis Related Group (DRG)–based and capitated payments expedited that shift.
Accompanying the change, physicians became more aware of the potential repercussions of sicker and quicker discharges. They began to monitor their care and, as best as possible, use what measures they could as a proxy for quality (readmissions and hospital-acquired conditions). Providers balanced the harms of a continued stay with the benefits of added days, not to mention the need for cost savings.
I recognize this because of the cognitive dissonance providers now experience because of the mixed messages delivered by hospital leaders.
On the one hand, the DRG-driven system that we have binds the hospital’s bottom line – and that is not going away. On the other, we are paying more attention to excessive costs in post-acute settings, that is, subacute facilities when home health will do or more intense acute rehabilitation rather than the subacute route.
Making determinations as to whether a certain course is proper, whether a patient will be safe, whether families can provide adequate agency and backing, and whether we can avail community services takes time. Sicker and quicker; mindful of short-term outcomes; worked when we had postdischarge blinders on. As we remove such obstacles, and payment incentives change to cover broader intervals of time, we have to adapt. And that means leadership must realize that the practices that held hospitals in sound financial stead in years past are heading toward extinction – or, at best, falling out of favor.
Compare the costs of routine hospital care with the added expense of post-acute care, then multiply that extra expense times an aging, dependent population, and you add billions of dollars to the recovery tab. Some of these expenses are necessary, and some are not; a stay at a skilled nursing facility, for example, doubles the cost of an episode.
Read the full post at hospitalleader.org.
Also on The Hospital Leader …
- Why 7 On/7 Off Doesn’t Meet the Needs of Long-Stay Hospital Patients by Lauren Doctoroff, MD, FHM
- Is It Time for Health Policy M&Ms? by Chris Moriates, MD
- George Carlin Predicts Hospital Planning Strategy by Jordan Messler, MD, SFHM
- Many Paths to a Richer Job by Leslie Flores, MHA, MPH, SFHM
- A New Face for Online Modules by Chris Moriates, MD
A game of telephone?
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
The transfer of information from floor to the MICU team is a very interesting process: outside of the patient record, the person performing the handoff is highly responsible in the appropriate transfer of information.
One of the challenges encountered within the project is the way in which we are categorizing agreement between groups. Previously, we created a set of categories based upon recurring themes present within the free-text provider responses, and created categories, such as “cardiac management” and “diabetes management.” Upon creating these categories, I would then group them based upon concordance. However, responses such as “bipap during the night” and “not giving her bipap” would both be coded under “respiratory management,” but those two responses would not show the providers being in concordance. Upon consulting with my mentors Dr. Vineet Arora and Dr. Juan Rojas, we decided that it would be more accurate to categorize concordance based upon the original answers, keeping the breadth of the original data intact.
As I continue to organize the data based on concordance, I have to modify my frame of thought and focus on appropriately representing the responses. There is no such thing as perfect data, and this project is no exception; in this case, not every provider was able to be reached for a response, which requires more nuance as I categorize the degree of concordance within the data and think of appropriate categories. I am very glad to learn the skill of appropriate data representation, as we want it to demonstrate both the potential lack or presence of clarity in handoffs, as well as the represented responding providers.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
The transfer of information from floor to the MICU team is a very interesting process: outside of the patient record, the person performing the handoff is highly responsible in the appropriate transfer of information.
One of the challenges encountered within the project is the way in which we are categorizing agreement between groups. Previously, we created a set of categories based upon recurring themes present within the free-text provider responses, and created categories, such as “cardiac management” and “diabetes management.” Upon creating these categories, I would then group them based upon concordance. However, responses such as “bipap during the night” and “not giving her bipap” would both be coded under “respiratory management,” but those two responses would not show the providers being in concordance. Upon consulting with my mentors Dr. Vineet Arora and Dr. Juan Rojas, we decided that it would be more accurate to categorize concordance based upon the original answers, keeping the breadth of the original data intact.
As I continue to organize the data based on concordance, I have to modify my frame of thought and focus on appropriately representing the responses. There is no such thing as perfect data, and this project is no exception; in this case, not every provider was able to be reached for a response, which requires more nuance as I categorize the degree of concordance within the data and think of appropriate categories. I am very glad to learn the skill of appropriate data representation, as we want it to demonstrate both the potential lack or presence of clarity in handoffs, as well as the represented responding providers.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
The transfer of information from floor to the MICU team is a very interesting process: outside of the patient record, the person performing the handoff is highly responsible in the appropriate transfer of information.
One of the challenges encountered within the project is the way in which we are categorizing agreement between groups. Previously, we created a set of categories based upon recurring themes present within the free-text provider responses, and created categories, such as “cardiac management” and “diabetes management.” Upon creating these categories, I would then group them based upon concordance. However, responses such as “bipap during the night” and “not giving her bipap” would both be coded under “respiratory management,” but those two responses would not show the providers being in concordance. Upon consulting with my mentors Dr. Vineet Arora and Dr. Juan Rojas, we decided that it would be more accurate to categorize concordance based upon the original answers, keeping the breadth of the original data intact.
As I continue to organize the data based on concordance, I have to modify my frame of thought and focus on appropriately representing the responses. There is no such thing as perfect data, and this project is no exception; in this case, not every provider was able to be reached for a response, which requires more nuance as I categorize the degree of concordance within the data and think of appropriate categories. I am very glad to learn the skill of appropriate data representation, as we want it to demonstrate both the potential lack or presence of clarity in handoffs, as well as the represented responding providers.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
Improving our approach to discharge planning
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Sneak Peek: The Hospital Leader blog – Sept. 2017
Wrongful Life
There have been recent discussions in the lay media about a growing trend of litigation cases focused not on the “right to live,” but rather on the “right to die.” These cases have involved patients who received aggressive treatment, despite having documentation of their wishes not to receive such aggressive treatment. Although unsettling, it is not surprising that this issue has arisen, given the national conversations about the exorbitant cost of care at the end of life in the United States, and the frequency with which patients do not receive end-of-life care that is concordant with their wishes.
These conversations have spurred providers and patients to discuss and document their wishes, via advanced care directives and/or POLST orders (Physicians Orders for Life Sustaining Treatment). There is now even a national day devoted to advanced care decision making (National Healthcare Decisions Day).
But for situations where the paperwork is clear, and the patient actually does receive undesired aggressive care, more plaintiff attorneys are taking on these cases of the “right to die,” since now more people are recognizing and accepting that unwanted life is a type of harm.
This brings to light two important considerations in how we use advanced care planning documentation:
1. These documents should be treated as dynamic decision-making documents, not static documents that are filled out and filed at a single point in time. Patient wishes can and do change due to a variety of factors; any changes should be repeatedly sought to ensure consistency with care plans.
2. These documents should be the start of a conversation, not the end of a conversation. Written documentation can still be wrought with ambiguity; a conversation about the document can help clarify desires and ensure that wishes and care plans match.
In our ongoing desire to “do no harm,” overtreatment is increasingly being recognized by patients and families as a type of harm. To avoid these potentially catastrophic situations, we should all use advanced care documentation as the start of a careful conversation about goals of care and treatment choices. Hospitalists should work with their interprofessional team members (for example, case managers, social workers, nurse navigators, and so on) to make sure every patient has, or is at least working on, advance care directives, and guide the patient and family in decision-making that puts them at ease. With our patients, we can help ensure concordance between their end-of-life wishes and our care plans.
Read the full post at hospitalleader.org.
Also on The Hospital Leader…
Follow You, Follow Me by Tracy Cardin, ACNP-BC, SFHM
SHM Movers & Shakers, Hospital Silos & JHM Research in HM News by Felicia Steele
Wrongful Life
There have been recent discussions in the lay media about a growing trend of litigation cases focused not on the “right to live,” but rather on the “right to die.” These cases have involved patients who received aggressive treatment, despite having documentation of their wishes not to receive such aggressive treatment. Although unsettling, it is not surprising that this issue has arisen, given the national conversations about the exorbitant cost of care at the end of life in the United States, and the frequency with which patients do not receive end-of-life care that is concordant with their wishes.
These conversations have spurred providers and patients to discuss and document their wishes, via advanced care directives and/or POLST orders (Physicians Orders for Life Sustaining Treatment). There is now even a national day devoted to advanced care decision making (National Healthcare Decisions Day).
But for situations where the paperwork is clear, and the patient actually does receive undesired aggressive care, more plaintiff attorneys are taking on these cases of the “right to die,” since now more people are recognizing and accepting that unwanted life is a type of harm.
This brings to light two important considerations in how we use advanced care planning documentation:
1. These documents should be treated as dynamic decision-making documents, not static documents that are filled out and filed at a single point in time. Patient wishes can and do change due to a variety of factors; any changes should be repeatedly sought to ensure consistency with care plans.
2. These documents should be the start of a conversation, not the end of a conversation. Written documentation can still be wrought with ambiguity; a conversation about the document can help clarify desires and ensure that wishes and care plans match.
In our ongoing desire to “do no harm,” overtreatment is increasingly being recognized by patients and families as a type of harm. To avoid these potentially catastrophic situations, we should all use advanced care documentation as the start of a careful conversation about goals of care and treatment choices. Hospitalists should work with their interprofessional team members (for example, case managers, social workers, nurse navigators, and so on) to make sure every patient has, or is at least working on, advance care directives, and guide the patient and family in decision-making that puts them at ease. With our patients, we can help ensure concordance between their end-of-life wishes and our care plans.
Read the full post at hospitalleader.org.
Also on The Hospital Leader…
Follow You, Follow Me by Tracy Cardin, ACNP-BC, SFHM
SHM Movers & Shakers, Hospital Silos & JHM Research in HM News by Felicia Steele
Wrongful Life
There have been recent discussions in the lay media about a growing trend of litigation cases focused not on the “right to live,” but rather on the “right to die.” These cases have involved patients who received aggressive treatment, despite having documentation of their wishes not to receive such aggressive treatment. Although unsettling, it is not surprising that this issue has arisen, given the national conversations about the exorbitant cost of care at the end of life in the United States, and the frequency with which patients do not receive end-of-life care that is concordant with their wishes.
These conversations have spurred providers and patients to discuss and document their wishes, via advanced care directives and/or POLST orders (Physicians Orders for Life Sustaining Treatment). There is now even a national day devoted to advanced care decision making (National Healthcare Decisions Day).
But for situations where the paperwork is clear, and the patient actually does receive undesired aggressive care, more plaintiff attorneys are taking on these cases of the “right to die,” since now more people are recognizing and accepting that unwanted life is a type of harm.
This brings to light two important considerations in how we use advanced care planning documentation:
1. These documents should be treated as dynamic decision-making documents, not static documents that are filled out and filed at a single point in time. Patient wishes can and do change due to a variety of factors; any changes should be repeatedly sought to ensure consistency with care plans.
2. These documents should be the start of a conversation, not the end of a conversation. Written documentation can still be wrought with ambiguity; a conversation about the document can help clarify desires and ensure that wishes and care plans match.
In our ongoing desire to “do no harm,” overtreatment is increasingly being recognized by patients and families as a type of harm. To avoid these potentially catastrophic situations, we should all use advanced care documentation as the start of a careful conversation about goals of care and treatment choices. Hospitalists should work with their interprofessional team members (for example, case managers, social workers, nurse navigators, and so on) to make sure every patient has, or is at least working on, advance care directives, and guide the patient and family in decision-making that puts them at ease. With our patients, we can help ensure concordance between their end-of-life wishes and our care plans.
Read the full post at hospitalleader.org.
Also on The Hospital Leader…
Follow You, Follow Me by Tracy Cardin, ACNP-BC, SFHM
SHM Movers & Shakers, Hospital Silos & JHM Research in HM News by Felicia Steele
Student Hospitalist Scholars: The importance of shared mental models
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
As I walk the University of Chicago Hospital observing various health care practitioners, I am continually impressed with the businesslike approach and productivity of each individual. The hospital staff is composed of highly intelligent, experienced, and talented physicians, but I have come to understand that in this large system it can be difficult to maintain quality patient care with both increased census and increased handoffs.
The research project I am working on focuses on shared mental models between the MICU and the general floor on what the most important factor of care is while they are on the floor, and to identify how prominent it is for shared mental models to be present between the transferring and receiving teams. After reading various papers, I am beginning to understand the various complexities present in translating information when transferring patients from any department onto the floor.
I continue to discuss these topics with my mentors, Dr. Vineet Arora and Dr. Juan Rojas, in order to appropriately categorize all survey responses and identify whether there is concordance between teams. I am glad to be able to rely on their insight concerning methods of coding the data, as well as what type of medical care each responding individual receives, and remaining on track with my estimated timeline of completion.
Past research supports the idea that increased times, distractions, and workloads in regard to handoffs result in potential errors, decreasing the quality of patient care and potentially resulting in worse patient outcomes. MICU patients are at a particular risk, since ineffective communication could lead to readmission, which could result in worsened health outcomes.
I believe that this current research project is highly significant since it highlights whether effective communication is occurring in the first place, and whether teams are appropriately communicating patient plans for this group of higher-acuity patients. As I continue my research at the university, I hope to further identify whether effective communication is taking place for this at-risk group of floor patients.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
As I walk the University of Chicago Hospital observing various health care practitioners, I am continually impressed with the businesslike approach and productivity of each individual. The hospital staff is composed of highly intelligent, experienced, and talented physicians, but I have come to understand that in this large system it can be difficult to maintain quality patient care with both increased census and increased handoffs.
The research project I am working on focuses on shared mental models between the MICU and the general floor on what the most important factor of care is while they are on the floor, and to identify how prominent it is for shared mental models to be present between the transferring and receiving teams. After reading various papers, I am beginning to understand the various complexities present in translating information when transferring patients from any department onto the floor.
I continue to discuss these topics with my mentors, Dr. Vineet Arora and Dr. Juan Rojas, in order to appropriately categorize all survey responses and identify whether there is concordance between teams. I am glad to be able to rely on their insight concerning methods of coding the data, as well as what type of medical care each responding individual receives, and remaining on track with my estimated timeline of completion.
Past research supports the idea that increased times, distractions, and workloads in regard to handoffs result in potential errors, decreasing the quality of patient care and potentially resulting in worse patient outcomes. MICU patients are at a particular risk, since ineffective communication could lead to readmission, which could result in worsened health outcomes.
I believe that this current research project is highly significant since it highlights whether effective communication is occurring in the first place, and whether teams are appropriately communicating patient plans for this group of higher-acuity patients. As I continue my research at the university, I hope to further identify whether effective communication is taking place for this at-risk group of floor patients.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform healthcare and revolutionize patient care. The program has been expanded for the 2017-18 year, offering two options for students to receive funding and engage in scholarly work during their first, second and third years of medical school. As a part of the program, recipients are required to write about their experience on a biweekly basis.
As I walk the University of Chicago Hospital observing various health care practitioners, I am continually impressed with the businesslike approach and productivity of each individual. The hospital staff is composed of highly intelligent, experienced, and talented physicians, but I have come to understand that in this large system it can be difficult to maintain quality patient care with both increased census and increased handoffs.
The research project I am working on focuses on shared mental models between the MICU and the general floor on what the most important factor of care is while they are on the floor, and to identify how prominent it is for shared mental models to be present between the transferring and receiving teams. After reading various papers, I am beginning to understand the various complexities present in translating information when transferring patients from any department onto the floor.
I continue to discuss these topics with my mentors, Dr. Vineet Arora and Dr. Juan Rojas, in order to appropriately categorize all survey responses and identify whether there is concordance between teams. I am glad to be able to rely on their insight concerning methods of coding the data, as well as what type of medical care each responding individual receives, and remaining on track with my estimated timeline of completion.
Past research supports the idea that increased times, distractions, and workloads in regard to handoffs result in potential errors, decreasing the quality of patient care and potentially resulting in worse patient outcomes. MICU patients are at a particular risk, since ineffective communication could lead to readmission, which could result in worsened health outcomes.
I believe that this current research project is highly significant since it highlights whether effective communication is occurring in the first place, and whether teams are appropriately communicating patient plans for this group of higher-acuity patients. As I continue my research at the university, I hope to further identify whether effective communication is taking place for this at-risk group of floor patients.
Anton Garazha is a medical student at Chicago Medical School at Rosalind Franklin University in North Chicago. He received his bachelor of science degree in biology from Loyola University in Chicago in 2015 and his master of biomedical science degree from Rosalind Franklin University in 2016. Anton is very interested in community outreach and quality improvement, and in his spare time tutors students in science-based subjects.
How hospitalists can help reduce readmissions
Hospital readmissions are frequent, harmful, and costly. Consider the fact that 18% of Medicare patients can expect to be readmitted within 30 days at a cost of more than $17 billion.1 Recent changes in health care policy aimed at reducing readmission have substantially increased attention to this major health care issue.2
The Affordable Care Act has mandated that the Centers for Medicare & Medicaid Services reduce payment to hospitals with higher-than-expected 30-day readmissions, with its Hospital Readmissions Reduction Program. This has driven rapid growth in the study of patients rehospitalized within 30 days of discharge.3 So what are some strategies that have either been proven to reduce readmissions or show promise in doing so? 
An ounce of prevention
In studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission,4 Shoshana Herzig, MD, MPH, assistant professor of medicine, Harvard Medical School, and director of Hospital Medicine Research, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, and her colleagues identified some potential preventive strategies.
The most commonly endorsed strategy to prevent readmissions by both primary care physicians and hospitalists surveyed involved improving self-management plans at discharge. “This refers to actions such as providing patient-centered discharge instructions (that is, making sure they are written in language that patients can understand) or asking transition coaches to help facilitate a successful transition,” Dr. Herzig said. “This finding is consistent with the fact that the factor most commonly identified as contributing to readmissions was insufficient patient understanding or ability to self-manage. Combined, these findings suggest that strategies to enhance patient understanding of their illness, care plan, and what to expect after hospital discharge, are likely to be important components of successful readmission reduction programs.”
Provisioning of resources to patients to help them manage their care after discharge is also recommended. For example, engaging nurses or pharmacists who can help with issues that arise after discharge may help keep patients out of the hospital.
“Hospitalists should be aware of what resources are available to help patients manage their care,” Dr. Herzig said. For example, if a patient needs periodic blood pressure monitoring, the hospitalist can tell the patient about free blood pressure checkpoints or suggest a home-automated blood pressure monitor.
The study also showed that improved coordination of care between inpatient and outpatient providers, such as sharing medical records, could reduce readmission rates. “This allows for better inpatient care and increased ability for primary care physicians to react appropriately to issues arising after discharge,” Dr. Herzig said. “In the absence of a shared system, hospitalists should complete discharge summaries in a timely fashion and ensure that they’re promptly transmitted to primary care physicians.”
Dr. Herzig said it’s important to note that hospitalists and primary care physicians had different appraisals of reasons for readmission. Therefore, when designing readmission reduction programs or determining specific services to prevent a readmission for a given patient, it is important for hospitalists to obtain input from primary care physicians to ensure that they address all of the potential contributors to readmission for a given patient.
Interviewing patients regarding readmissions
After involved clinicians and independent physician reviewers performed extensive case reviews of more than 700 readmitted patients,6 Ashley Busuttil, MD, FHM, associate section chief, Hospital Medicine, University of California, Los Angeles Department of Medicine; and executive medical director, Medicine Services, UCLA Department of Medicine, and Erin Dowling, MD, assistant clinical professor, General Internal Medicine, Hospitalist Services, UCLA Medical Center, Santa Monica, Calif., and their colleagues were unable to identify which readmissions could have easily been prevented, and found that readmission causality varied extensively.
Through interviews with patients, the researchers determined that patients were more likely to think that their readmission was preventable if they felt unready for discharge during their initial hospitalization. This was despite the fact that patients met what clinicians would consider “ready” by objective, provider-centric criteria: they were medically stable, they had in-home support services, they had follow-up arranged, and so forth. As such, they wanted to put effort into educating and preparing patients for what home will look and feel like posthospitalization to address their feelings of unreadiness.
To that end, the researchers created an enhanced transition initiative that included showing an educational video near the time of admission and a patient-centered discharge checklist to help patients identify questions they might have after discharge. The discharge checklist asks patients to put themselves in the position of being at home and working through scenarios they may face so they will know how to deal with them. For example, if you have pain, who should you call? What should you do if you run out of medication?
Dr. Dowling believes that the hospitalist will, over time, become essential to assessing patient readiness. “As we learn more about how patients approach discharge, hospitalists’ understanding of patient needs beyond straightforward medical care will be crucial to having smoother transitions of care,” she said.
The researchers also explored pain control. As a health system, UCLA Medical Center has formed a multidisciplinary task force to optimize its approach to pain control. “If we can address comfort – for both patients at high risk of readmission and those that aren’t – we hope we can improve symptom control overall,” Dr. Busuttil said. “It’s not uncommon for patients to feel inadequate symptom control at discharge. While this is likely only one component of all the readmission pieces, a patient who feels that their symptoms are not controlled is likely to feel less ready for discharge. Increasing patient readiness, perhaps by increasing symptom control and improving communication regarding symptom management expectations, is a task that the hospitalist is well positioned to address.”
The researchers also wanted to find out why patients may not use available outpatient resources, and assessed them for decisional conflict – a measure of certainty with decision making – when selecting from multiple options for accessing medical care if they were home postdischarge and began to feel ill again. “Patients with decisional conflict were more likely to state that they would go the emergency room rather than call their primary medical physician or visit an urgent care center,” Dr. Busuttil said.
The health system continues to screen patients for decisional conflict. “When positive, we provide bedside education on when to seek medical care through primary care, urgent care, or the emergency department,” Dr. Busuttil said. “We also provide patients with information on how to access each of these resources.”
While a prior discharge plan may have seemed ideal on paper, time and time again it’s not logistically possible for certain patients. “By having this knowledge gleaned from patient interviews, hospitalists are able to provide feedback to health systems regarding different options of outpatient care that may work for the different patient populations they serve,” Dr. Dowling said.
To understand why one particular patient population is being readmitted requires taking the time to understand that population, Dr. Dowling noted. “While many validated risk stratification tools are available, they may only serve as general guides,” she said. “To impact the population you serve, you must first understand the readmission process as it looks to them.”
Employing the HOSPITAL score
In another effort to reduce hospital readmissions, Jacques Donzé, MD, MSc, associate physician, Bern University Hospital, Switzerland, and research associate, Brigham and Women’s Hospital, Boston, and his colleagues used the HOSPITAL score to identify patients at high risk of 30-day potentially avoidable readmission.
To most efficiently reduce hospital readmissions, hospitals need to target complex and intensive discharge interventions for patients at high risk of potentially avoidable readmission who are more likely to benefit.2 “However, prior research indicates that clinical health care providers are not able to accurately identify which patients are at high risk for readmission,” Dr. Donzé said.
Dr. Donzé believes that several factors may influence the performance of a prediction model, such as the initial selection of the potential predictors, the quality of the derivation method, including readily available predictors commonly available, and including reliable factors that aren’t subject to subjective evaluation. “All of these factors can play a role in the performance and generalizability of the HOSPITAL score,” he said.
When a patient is identified as high risk to be readmitted, hospitalists can take certain actions to prevent readmission. “Interventions are more likely to be effective when they include several components,” Dr. Donzé said. “These include follow-up phone calls and/or home visits, review of the patient’s medication list, patient education, and sending a discharge summary to the patient’s primary care physician in a timely manner. For now, enough evidence for a specific effective multimodal intervention to be generalizable to the majority of patients is lacking.”
Currently, the HOSPITAL score has been validated in approximately 180,000 patients in 14 hospitals across five countries and three continents – always showing good performance and generalizability. The HOSPITAL score includes seven variables readily available before hospital discharge, is easy to use, and is the most widely validated prediction model for readmission, Dr. Donzé said.
Before being implemented into practice, a score should ideally reach the highest level of validation, that is, show its clinical impact. “We expect that the score will not only be able to accurately predict high-risk patients, but using the score will also impact patient care by reducing readmissions when coupled with an appropriate intervention,” Dr. Donzé said.
In summary, research has shown that a variety of methods can be used to reduce hospital readmissions, including studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission; interviewing patients regarding readmissions; and identifying patients at high risk of readmission using the HOSPITAL score.
Many researchers are continuing their studies in these areas.
Karen Appold is a medical writer in Pennsylvania.
Using hospitalist reflections as a means to reduce readmissions
Readmission studies and the development of readmission scoring systems and prediction tools rely on data from a large number of patients, typically extracted from administrative databases.
To complement this data, Deanne Kashiwagi, MD, consultant, Hospital Internal Medicine, Mayo Clinic, Rochester, Minn., and her colleagues asked hospitalists to reflect upon the readmissions of patients for whom they cared to add insight into the culture of patient care transitions within the health system.
“We felt there was some value in considering these nuances of the local care environment, which may not be represented in studies drawing from large databases, as potential targets for readmission efforts,” she said.
Dr. Kashiwagi believes that including elements of local practice and culture was the strength of their work. “Groups interested in replicating this reflective process should consider including factors specific to their practices that may contribute to readmission,” she said.
Asking hospitalists to perform reviews has led to implementing changes. Physicians were prompted to schedule earlier follow-up appointments and nurse practitioners and physician assistants have worked to improve the quality of their discharge summaries. The exercise also engaged hospitalists to suggest system changes that might contribute to decreased readmissions, such as a geriatrician-run service (which was recently begun) to provide multidisciplinary acute geriatric care for hospitalized older adults.
“Although large-scale studies are clearly important, readmission review at a more granular level may have merit as well,” Dr. Kashiwagi said, noting that such reviews identify local practice factors that groups may quickly act upon to help decrease readmissions. “Hospitalists readily engaged in this reflective exercise, which yielded actionable information to decrease readmissions.”
In commenting on why a different similar study7 didn’t mimic the results of Mayo Clinic’s study, Dr. Kashiwagi said there were some differences in methodology that may explain the difference in readmission rates. “First, this group excluded patients on dialysis, which in our study was a common comorbidity of our readmitted patients,” she said. “It is also notable that the chart review tool was different. Perhaps there is less representation of local factors, unique to that hospitalist group and their practice culture, than on our review form. These investigators also discussed their readmissions at routine intervals. Additionally, their preintervention readmission rate was lower than Mayo Clinic’s group, and although the readmission rate trended downward postintervention, it did not reach statistical significance.”
References
1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-28.
2. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL Score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016 Apr;176(4):496-502.
3. Kashiwagi DT, Burton MC, Hakim FA, et al. Reflective practice: a tool for readmission reduction. Am J Med Qual. 2016 May;31(3):265-71.
4. Herzig SJ, Schnipper JL, Doctoroff L, et al. Physician perspectives on factors contributing to readmissions and potential prevention strategies: a multicenter survey. J Gen Intern Med. 2016 Nov;31(11):1287-93. Epub 2016 Jun 9.
5. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011 Jul;26(7):771-6.
6. Busuttil A, Howard-Anderson J, Dowling EP, et al. Building a comprehensive patient-centered readmission reduction program [abstract]. J Hosp Med. 2016;11(suppl 1).
7. Rana V, Thapa B, Saini SC, et al. Self-reflection as a tool to increase hospitalist participation in readmission quality improvement. Qual Manag Health Care. 2016 Oct/Dec;25(4):219-24.
Hospital readmissions are frequent, harmful, and costly. Consider the fact that 18% of Medicare patients can expect to be readmitted within 30 days at a cost of more than $17 billion.1 Recent changes in health care policy aimed at reducing readmission have substantially increased attention to this major health care issue.2
The Affordable Care Act has mandated that the Centers for Medicare & Medicaid Services reduce payment to hospitals with higher-than-expected 30-day readmissions, with its Hospital Readmissions Reduction Program. This has driven rapid growth in the study of patients rehospitalized within 30 days of discharge.3 So what are some strategies that have either been proven to reduce readmissions or show promise in doing so?
An ounce of prevention
In studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission,4 Shoshana Herzig, MD, MPH, assistant professor of medicine, Harvard Medical School, and director of Hospital Medicine Research, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, and her colleagues identified some potential preventive strategies.
The most commonly endorsed strategy to prevent readmissions by both primary care physicians and hospitalists surveyed involved improving self-management plans at discharge. “This refers to actions such as providing patient-centered discharge instructions (that is, making sure they are written in language that patients can understand) or asking transition coaches to help facilitate a successful transition,” Dr. Herzig said. “This finding is consistent with the fact that the factor most commonly identified as contributing to readmissions was insufficient patient understanding or ability to self-manage. Combined, these findings suggest that strategies to enhance patient understanding of their illness, care plan, and what to expect after hospital discharge, are likely to be important components of successful readmission reduction programs.”
Provisioning of resources to patients to help them manage their care after discharge is also recommended. For example, engaging nurses or pharmacists who can help with issues that arise after discharge may help keep patients out of the hospital.
“Hospitalists should be aware of what resources are available to help patients manage their care,” Dr. Herzig said. For example, if a patient needs periodic blood pressure monitoring, the hospitalist can tell the patient about free blood pressure checkpoints or suggest a home-automated blood pressure monitor.
The study also showed that improved coordination of care between inpatient and outpatient providers, such as sharing medical records, could reduce readmission rates. “This allows for better inpatient care and increased ability for primary care physicians to react appropriately to issues arising after discharge,” Dr. Herzig said. “In the absence of a shared system, hospitalists should complete discharge summaries in a timely fashion and ensure that they’re promptly transmitted to primary care physicians.”
Dr. Herzig said it’s important to note that hospitalists and primary care physicians had different appraisals of reasons for readmission. Therefore, when designing readmission reduction programs or determining specific services to prevent a readmission for a given patient, it is important for hospitalists to obtain input from primary care physicians to ensure that they address all of the potential contributors to readmission for a given patient.
Interviewing patients regarding readmissions
After involved clinicians and independent physician reviewers performed extensive case reviews of more than 700 readmitted patients,6 Ashley Busuttil, MD, FHM, associate section chief, Hospital Medicine, University of California, Los Angeles Department of Medicine; and executive medical director, Medicine Services, UCLA Department of Medicine, and Erin Dowling, MD, assistant clinical professor, General Internal Medicine, Hospitalist Services, UCLA Medical Center, Santa Monica, Calif., and their colleagues were unable to identify which readmissions could have easily been prevented, and found that readmission causality varied extensively.
Through interviews with patients, the researchers determined that patients were more likely to think that their readmission was preventable if they felt unready for discharge during their initial hospitalization. This was despite the fact that patients met what clinicians would consider “ready” by objective, provider-centric criteria: they were medically stable, they had in-home support services, they had follow-up arranged, and so forth. As such, they wanted to put effort into educating and preparing patients for what home will look and feel like posthospitalization to address their feelings of unreadiness.
To that end, the researchers created an enhanced transition initiative that included showing an educational video near the time of admission and a patient-centered discharge checklist to help patients identify questions they might have after discharge. The discharge checklist asks patients to put themselves in the position of being at home and working through scenarios they may face so they will know how to deal with them. For example, if you have pain, who should you call? What should you do if you run out of medication?
Dr. Dowling believes that the hospitalist will, over time, become essential to assessing patient readiness. “As we learn more about how patients approach discharge, hospitalists’ understanding of patient needs beyond straightforward medical care will be crucial to having smoother transitions of care,” she said.
The researchers also explored pain control. As a health system, UCLA Medical Center has formed a multidisciplinary task force to optimize its approach to pain control. “If we can address comfort – for both patients at high risk of readmission and those that aren’t – we hope we can improve symptom control overall,” Dr. Busuttil said. “It’s not uncommon for patients to feel inadequate symptom control at discharge. While this is likely only one component of all the readmission pieces, a patient who feels that their symptoms are not controlled is likely to feel less ready for discharge. Increasing patient readiness, perhaps by increasing symptom control and improving communication regarding symptom management expectations, is a task that the hospitalist is well positioned to address.”
The researchers also wanted to find out why patients may not use available outpatient resources, and assessed them for decisional conflict – a measure of certainty with decision making – when selecting from multiple options for accessing medical care if they were home postdischarge and began to feel ill again. “Patients with decisional conflict were more likely to state that they would go the emergency room rather than call their primary medical physician or visit an urgent care center,” Dr. Busuttil said.
The health system continues to screen patients for decisional conflict. “When positive, we provide bedside education on when to seek medical care through primary care, urgent care, or the emergency department,” Dr. Busuttil said. “We also provide patients with information on how to access each of these resources.”
While a prior discharge plan may have seemed ideal on paper, time and time again it’s not logistically possible for certain patients. “By having this knowledge gleaned from patient interviews, hospitalists are able to provide feedback to health systems regarding different options of outpatient care that may work for the different patient populations they serve,” Dr. Dowling said.
To understand why one particular patient population is being readmitted requires taking the time to understand that population, Dr. Dowling noted. “While many validated risk stratification tools are available, they may only serve as general guides,” she said. “To impact the population you serve, you must first understand the readmission process as it looks to them.”
Employing the HOSPITAL score
In another effort to reduce hospital readmissions, Jacques Donzé, MD, MSc, associate physician, Bern University Hospital, Switzerland, and research associate, Brigham and Women’s Hospital, Boston, and his colleagues used the HOSPITAL score to identify patients at high risk of 30-day potentially avoidable readmission.
To most efficiently reduce hospital readmissions, hospitals need to target complex and intensive discharge interventions for patients at high risk of potentially avoidable readmission who are more likely to benefit.2 “However, prior research indicates that clinical health care providers are not able to accurately identify which patients are at high risk for readmission,” Dr. Donzé said.
Dr. Donzé believes that several factors may influence the performance of a prediction model, such as the initial selection of the potential predictors, the quality of the derivation method, including readily available predictors commonly available, and including reliable factors that aren’t subject to subjective evaluation. “All of these factors can play a role in the performance and generalizability of the HOSPITAL score,” he said.
When a patient is identified as high risk to be readmitted, hospitalists can take certain actions to prevent readmission. “Interventions are more likely to be effective when they include several components,” Dr. Donzé said. “These include follow-up phone calls and/or home visits, review of the patient’s medication list, patient education, and sending a discharge summary to the patient’s primary care physician in a timely manner. For now, enough evidence for a specific effective multimodal intervention to be generalizable to the majority of patients is lacking.”
Currently, the HOSPITAL score has been validated in approximately 180,000 patients in 14 hospitals across five countries and three continents – always showing good performance and generalizability. The HOSPITAL score includes seven variables readily available before hospital discharge, is easy to use, and is the most widely validated prediction model for readmission, Dr. Donzé said.
Before being implemented into practice, a score should ideally reach the highest level of validation, that is, show its clinical impact. “We expect that the score will not only be able to accurately predict high-risk patients, but using the score will also impact patient care by reducing readmissions when coupled with an appropriate intervention,” Dr. Donzé said.
In summary, research has shown that a variety of methods can be used to reduce hospital readmissions, including studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission; interviewing patients regarding readmissions; and identifying patients at high risk of readmission using the HOSPITAL score.
Many researchers are continuing their studies in these areas.
Karen Appold is a medical writer in Pennsylvania.
Using hospitalist reflections as a means to reduce readmissions
Readmission studies and the development of readmission scoring systems and prediction tools rely on data from a large number of patients, typically extracted from administrative databases.
To complement this data, Deanne Kashiwagi, MD, consultant, Hospital Internal Medicine, Mayo Clinic, Rochester, Minn., and her colleagues asked hospitalists to reflect upon the readmissions of patients for whom they cared to add insight into the culture of patient care transitions within the health system.
“We felt there was some value in considering these nuances of the local care environment, which may not be represented in studies drawing from large databases, as potential targets for readmission efforts,” she said.
Dr. Kashiwagi believes that including elements of local practice and culture was the strength of their work. “Groups interested in replicating this reflective process should consider including factors specific to their practices that may contribute to readmission,” she said.
Asking hospitalists to perform reviews has led to implementing changes. Physicians were prompted to schedule earlier follow-up appointments and nurse practitioners and physician assistants have worked to improve the quality of their discharge summaries. The exercise also engaged hospitalists to suggest system changes that might contribute to decreased readmissions, such as a geriatrician-run service (which was recently begun) to provide multidisciplinary acute geriatric care for hospitalized older adults.
“Although large-scale studies are clearly important, readmission review at a more granular level may have merit as well,” Dr. Kashiwagi said, noting that such reviews identify local practice factors that groups may quickly act upon to help decrease readmissions. “Hospitalists readily engaged in this reflective exercise, which yielded actionable information to decrease readmissions.”
In commenting on why a different similar study7 didn’t mimic the results of Mayo Clinic’s study, Dr. Kashiwagi said there were some differences in methodology that may explain the difference in readmission rates. “First, this group excluded patients on dialysis, which in our study was a common comorbidity of our readmitted patients,” she said. “It is also notable that the chart review tool was different. Perhaps there is less representation of local factors, unique to that hospitalist group and their practice culture, than on our review form. These investigators also discussed their readmissions at routine intervals. Additionally, their preintervention readmission rate was lower than Mayo Clinic’s group, and although the readmission rate trended downward postintervention, it did not reach statistical significance.”
References
1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-28.
2. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL Score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016 Apr;176(4):496-502.
3. Kashiwagi DT, Burton MC, Hakim FA, et al. Reflective practice: a tool for readmission reduction. Am J Med Qual. 2016 May;31(3):265-71.
4. Herzig SJ, Schnipper JL, Doctoroff L, et al. Physician perspectives on factors contributing to readmissions and potential prevention strategies: a multicenter survey. J Gen Intern Med. 2016 Nov;31(11):1287-93. Epub 2016 Jun 9.
5. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011 Jul;26(7):771-6.
6. Busuttil A, Howard-Anderson J, Dowling EP, et al. Building a comprehensive patient-centered readmission reduction program [abstract]. J Hosp Med. 2016;11(suppl 1).
7. Rana V, Thapa B, Saini SC, et al. Self-reflection as a tool to increase hospitalist participation in readmission quality improvement. Qual Manag Health Care. 2016 Oct/Dec;25(4):219-24.
Hospital readmissions are frequent, harmful, and costly. Consider the fact that 18% of Medicare patients can expect to be readmitted within 30 days at a cost of more than $17 billion.1 Recent changes in health care policy aimed at reducing readmission have substantially increased attention to this major health care issue.2
The Affordable Care Act has mandated that the Centers for Medicare & Medicaid Services reduce payment to hospitals with higher-than-expected 30-day readmissions, with its Hospital Readmissions Reduction Program. This has driven rapid growth in the study of patients rehospitalized within 30 days of discharge.3 So what are some strategies that have either been proven to reduce readmissions or show promise in doing so?
An ounce of prevention
In studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission,4 Shoshana Herzig, MD, MPH, assistant professor of medicine, Harvard Medical School, and director of Hospital Medicine Research, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, and her colleagues identified some potential preventive strategies.
The most commonly endorsed strategy to prevent readmissions by both primary care physicians and hospitalists surveyed involved improving self-management plans at discharge. “This refers to actions such as providing patient-centered discharge instructions (that is, making sure they are written in language that patients can understand) or asking transition coaches to help facilitate a successful transition,” Dr. Herzig said. “This finding is consistent with the fact that the factor most commonly identified as contributing to readmissions was insufficient patient understanding or ability to self-manage. Combined, these findings suggest that strategies to enhance patient understanding of their illness, care plan, and what to expect after hospital discharge, are likely to be important components of successful readmission reduction programs.”
Provisioning of resources to patients to help them manage their care after discharge is also recommended. For example, engaging nurses or pharmacists who can help with issues that arise after discharge may help keep patients out of the hospital.
“Hospitalists should be aware of what resources are available to help patients manage their care,” Dr. Herzig said. For example, if a patient needs periodic blood pressure monitoring, the hospitalist can tell the patient about free blood pressure checkpoints or suggest a home-automated blood pressure monitor.
The study also showed that improved coordination of care between inpatient and outpatient providers, such as sharing medical records, could reduce readmission rates. “This allows for better inpatient care and increased ability for primary care physicians to react appropriately to issues arising after discharge,” Dr. Herzig said. “In the absence of a shared system, hospitalists should complete discharge summaries in a timely fashion and ensure that they’re promptly transmitted to primary care physicians.”
Dr. Herzig said it’s important to note that hospitalists and primary care physicians had different appraisals of reasons for readmission. Therefore, when designing readmission reduction programs or determining specific services to prevent a readmission for a given patient, it is important for hospitalists to obtain input from primary care physicians to ensure that they address all of the potential contributors to readmission for a given patient.
Interviewing patients regarding readmissions
After involved clinicians and independent physician reviewers performed extensive case reviews of more than 700 readmitted patients,6 Ashley Busuttil, MD, FHM, associate section chief, Hospital Medicine, University of California, Los Angeles Department of Medicine; and executive medical director, Medicine Services, UCLA Department of Medicine, and Erin Dowling, MD, assistant clinical professor, General Internal Medicine, Hospitalist Services, UCLA Medical Center, Santa Monica, Calif., and their colleagues were unable to identify which readmissions could have easily been prevented, and found that readmission causality varied extensively.
Through interviews with patients, the researchers determined that patients were more likely to think that their readmission was preventable if they felt unready for discharge during their initial hospitalization. This was despite the fact that patients met what clinicians would consider “ready” by objective, provider-centric criteria: they were medically stable, they had in-home support services, they had follow-up arranged, and so forth. As such, they wanted to put effort into educating and preparing patients for what home will look and feel like posthospitalization to address their feelings of unreadiness.
To that end, the researchers created an enhanced transition initiative that included showing an educational video near the time of admission and a patient-centered discharge checklist to help patients identify questions they might have after discharge. The discharge checklist asks patients to put themselves in the position of being at home and working through scenarios they may face so they will know how to deal with them. For example, if you have pain, who should you call? What should you do if you run out of medication?
Dr. Dowling believes that the hospitalist will, over time, become essential to assessing patient readiness. “As we learn more about how patients approach discharge, hospitalists’ understanding of patient needs beyond straightforward medical care will be crucial to having smoother transitions of care,” she said.
The researchers also explored pain control. As a health system, UCLA Medical Center has formed a multidisciplinary task force to optimize its approach to pain control. “If we can address comfort – for both patients at high risk of readmission and those that aren’t – we hope we can improve symptom control overall,” Dr. Busuttil said. “It’s not uncommon for patients to feel inadequate symptom control at discharge. While this is likely only one component of all the readmission pieces, a patient who feels that their symptoms are not controlled is likely to feel less ready for discharge. Increasing patient readiness, perhaps by increasing symptom control and improving communication regarding symptom management expectations, is a task that the hospitalist is well positioned to address.”
The researchers also wanted to find out why patients may not use available outpatient resources, and assessed them for decisional conflict – a measure of certainty with decision making – when selecting from multiple options for accessing medical care if they were home postdischarge and began to feel ill again. “Patients with decisional conflict were more likely to state that they would go the emergency room rather than call their primary medical physician or visit an urgent care center,” Dr. Busuttil said.
The health system continues to screen patients for decisional conflict. “When positive, we provide bedside education on when to seek medical care through primary care, urgent care, or the emergency department,” Dr. Busuttil said. “We also provide patients with information on how to access each of these resources.”
While a prior discharge plan may have seemed ideal on paper, time and time again it’s not logistically possible for certain patients. “By having this knowledge gleaned from patient interviews, hospitalists are able to provide feedback to health systems regarding different options of outpatient care that may work for the different patient populations they serve,” Dr. Dowling said.
To understand why one particular patient population is being readmitted requires taking the time to understand that population, Dr. Dowling noted. “While many validated risk stratification tools are available, they may only serve as general guides,” she said. “To impact the population you serve, you must first understand the readmission process as it looks to them.”
Employing the HOSPITAL score
In another effort to reduce hospital readmissions, Jacques Donzé, MD, MSc, associate physician, Bern University Hospital, Switzerland, and research associate, Brigham and Women’s Hospital, Boston, and his colleagues used the HOSPITAL score to identify patients at high risk of 30-day potentially avoidable readmission.
To most efficiently reduce hospital readmissions, hospitals need to target complex and intensive discharge interventions for patients at high risk of potentially avoidable readmission who are more likely to benefit.2 “However, prior research indicates that clinical health care providers are not able to accurately identify which patients are at high risk for readmission,” Dr. Donzé said.
Dr. Donzé believes that several factors may influence the performance of a prediction model, such as the initial selection of the potential predictors, the quality of the derivation method, including readily available predictors commonly available, and including reliable factors that aren’t subject to subjective evaluation. “All of these factors can play a role in the performance and generalizability of the HOSPITAL score,” he said.
When a patient is identified as high risk to be readmitted, hospitalists can take certain actions to prevent readmission. “Interventions are more likely to be effective when they include several components,” Dr. Donzé said. “These include follow-up phone calls and/or home visits, review of the patient’s medication list, patient education, and sending a discharge summary to the patient’s primary care physician in a timely manner. For now, enough evidence for a specific effective multimodal intervention to be generalizable to the majority of patients is lacking.”
Currently, the HOSPITAL score has been validated in approximately 180,000 patients in 14 hospitals across five countries and three continents – always showing good performance and generalizability. The HOSPITAL score includes seven variables readily available before hospital discharge, is easy to use, and is the most widely validated prediction model for readmission, Dr. Donzé said.
Before being implemented into practice, a score should ideally reach the highest level of validation, that is, show its clinical impact. “We expect that the score will not only be able to accurately predict high-risk patients, but using the score will also impact patient care by reducing readmissions when coupled with an appropriate intervention,” Dr. Donzé said.
In summary, research has shown that a variety of methods can be used to reduce hospital readmissions, including studying inpatient and outpatient physicians’ perspectives regarding factors contributing to readmission; interviewing patients regarding readmissions; and identifying patients at high risk of readmission using the HOSPITAL score.
Many researchers are continuing their studies in these areas.
Karen Appold is a medical writer in Pennsylvania.
Using hospitalist reflections as a means to reduce readmissions
Readmission studies and the development of readmission scoring systems and prediction tools rely on data from a large number of patients, typically extracted from administrative databases.
To complement this data, Deanne Kashiwagi, MD, consultant, Hospital Internal Medicine, Mayo Clinic, Rochester, Minn., and her colleagues asked hospitalists to reflect upon the readmissions of patients for whom they cared to add insight into the culture of patient care transitions within the health system.
“We felt there was some value in considering these nuances of the local care environment, which may not be represented in studies drawing from large databases, as potential targets for readmission efforts,” she said.
Dr. Kashiwagi believes that including elements of local practice and culture was the strength of their work. “Groups interested in replicating this reflective process should consider including factors specific to their practices that may contribute to readmission,” she said.
Asking hospitalists to perform reviews has led to implementing changes. Physicians were prompted to schedule earlier follow-up appointments and nurse practitioners and physician assistants have worked to improve the quality of their discharge summaries. The exercise also engaged hospitalists to suggest system changes that might contribute to decreased readmissions, such as a geriatrician-run service (which was recently begun) to provide multidisciplinary acute geriatric care for hospitalized older adults.
“Although large-scale studies are clearly important, readmission review at a more granular level may have merit as well,” Dr. Kashiwagi said, noting that such reviews identify local practice factors that groups may quickly act upon to help decrease readmissions. “Hospitalists readily engaged in this reflective exercise, which yielded actionable information to decrease readmissions.”
In commenting on why a different similar study7 didn’t mimic the results of Mayo Clinic’s study, Dr. Kashiwagi said there were some differences in methodology that may explain the difference in readmission rates. “First, this group excluded patients on dialysis, which in our study was a common comorbidity of our readmitted patients,” she said. “It is also notable that the chart review tool was different. Perhaps there is less representation of local factors, unique to that hospitalist group and their practice culture, than on our review form. These investigators also discussed their readmissions at routine intervals. Additionally, their preintervention readmission rate was lower than Mayo Clinic’s group, and although the readmission rate trended downward postintervention, it did not reach statistical significance.”
References
1. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-28.
2. Donzé JD, Williams MV, Robinson EJ, et al. International validity of the HOSPITAL Score to predict 30-day potentially avoidable hospital readmissions. JAMA Intern Med. 2016 Apr;176(4):496-502.
3. Kashiwagi DT, Burton MC, Hakim FA, et al. Reflective practice: a tool for readmission reduction. Am J Med Qual. 2016 May;31(3):265-71.
4. Herzig SJ, Schnipper JL, Doctoroff L, et al. Physician perspectives on factors contributing to readmissions and potential prevention strategies: a multicenter survey. J Gen Intern Med. 2016 Nov;31(11):1287-93. Epub 2016 Jun 9.
5. Allaudeen N, Schnipper JL, Orav EJ, et al. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011 Jul;26(7):771-6.
6. Busuttil A, Howard-Anderson J, Dowling EP, et al. Building a comprehensive patient-centered readmission reduction program [abstract]. J Hosp Med. 2016;11(suppl 1).
7. Rana V, Thapa B, Saini SC, et al. Self-reflection as a tool to increase hospitalist participation in readmission quality improvement. Qual Manag Health Care. 2016 Oct/Dec;25(4):219-24.