Perspectives

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

Journal of Clinical Outcomes Management. 2023 May;30(3):67-70 | 10.12788/jcom.0130

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Artificial Intelligence and Diagnostic Errors

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

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

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

Conclusion

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

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

Disclosures: None reported.

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

Artificial Intelligence and Diagnostic Errors

Conclusion

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