Co-production of Diagnostic Excellence – Patients, Clinicians, and Artificial Intelligence; Comment on “Achieving Diagnostic Excellence: Roadmaps to Develop and Use Patient-Reported Measures With an Equity Lens”

Articles in Press

Document Type : Commentary

Authors

1 Division of Hospital Medicine, Department of Medicine, San Francisco General Hospital, San Francisco, CA, USA

2 Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California San Francisco, San Francisco, CA, USA

3 Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA

Abstract

Patients often experience long journeys within the healthcare system before obtaining a diagnosis. Though progress has been made in measuring the quality of diagnosis, existing measures largely fail to capture the diagnostic process from the patient’s perspective. McDonald and colleagues’ paper presents 7 overarching goals for the use of patientreported measures (PRMs) in diagnostic excellence and presents visual roadmaps to guide the development, implementation, and evaluation of these measures. To accelerate the real-world use of PRMs, organizations should initially prioritize the use of patient-reported metrics that are already in development, such as patient-reported experience measures. Pairing PRMs with artificial intelligence (AI) techniques, such as “diagnostic wayfinding” (a dynamic diagnostic refinement process that also includes analysis of electronic health record data and metadata to characterize the diagnostic journey), should also improve diagnostic performance. Ultimately, combining PRMs with technological advancements holds the potential to achieve true co-production of diagnostic excellence. 

Keywords

References

  1. Agency for Healthcare Research and Quality. AHRQ Quality Indicators. Table 1. Summary of measures under consideration by AHRQ Quality Indicators (QI) Program. 2024. https://qualityindicators.ahrq.gov/Downloads/Resources/Diagnostic%20Excellence%20Measure%20for%20RFI.pdf. Accessed December 29, 2024.
  2. Epstein HM, Haskell H, Hemmelgarn C, et al. The Patient’s Role in Diagnostic Safety and Excellence: From Passive Reception towards Co-Design. Rockville, MD: Agency for Healthcare Research and Quality; 2024. Report No.: AHRQ Publication No. 24-0010-8-EF. https://www.ahrq.gov/diagnostic-safety/resources/issue-briefs/dxsafety-patient-role.html.  Accessed December 26, 2024.
  3. McDonald KM, Gleason KT, Jajodia A, Haskell H, Dukhanin V. Achieving diagnostic excellence: roadmaps to develop and use patient-reported measures with an equity lens. Int J Health Policy Manag. 2024;13:8048. doi:34172/ijhpm.8048
  4. Bull C, Byrnes J, Hettiarachchi R, Downes M. A systematic review of the validity and reliability of patient-reported experience measures. Health Serv Res. 2019;54(5):1023-1035. doi:1111/1475-6773.13187
  5. Dukhanin V, Gamper MJ, Gleason KT, McDonald KM. Patient-reported outcome and experience domains for diagnostic excellence: a scoping review to inform future measure development. Qual Life Res. 2024;33(11):2883-2897. doi:1007/s11136-024-03709-w
  6. Bull C, Teede H, Watson D, Callander EJ. Selecting and implementing patient-reported outcome and experience measures to assess health system performance. JAMA Health Forum. 2022;3(4):e220326. doi:1001/jamahealthforum.2022.0326
  7. Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial intelligence in medicine: where are we now? Acad Radiol. 2020;27(1):62-70.
  8. Goh E, Gallo R, Hom J, et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open. 2024;7(10):e2440969. doi:1001/jamanetworkopen.2024.40969
  9. Shah-Mohammadi F, Finkelstein J. Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department. Diagnostics (Basel). 2024;14(16):1779. doi:3390/diagnostics14161779
  10. Hoppe JM, Auer MK, Strüven A, Massberg S, Stremmel C. ChatGPT With GPT-4 outperforms emergency department physicians in diagnostic accuracy: retrospective analysis. J Med Internet Res. 2024;26:e56110. doi:2196/56110
  11. Millenson ML, Baldwin JL, Zipperer L, Singh H. Beyond Dr. Google: the evidence on consumer-facing digital tools for diagnosis. Diagnosis (Berl). 2018;5(3):95-105. doi:1515/dx-2018-0009
  12. White RW, Horvitz E. Cyberchondria: Studies of the escalation of medical concerns in Web search. ACM Trans Inf Syst. 2009 ;27(4):1-37.
  13. Barnard F, Van Sittert M, Rambhatla S. Self-diagnosis and large language models: a new front for medical misinformation. 2023. https://arxiv.org/pdf/2307.04910. Accessed December 26, 2024.
  14. Adler-Milstein J, Chen JH, Dhaliwal G. Next-generation artificial intelligence for diagnosis: from predicting diagnostic labels to "Wayfinding." JAMA. 2021;326(24):2467-2468. doi:1001/jama.2021.22396
International Journal of Health Policy and Management

Articles in Press, Corrected Proof
Available Online from 19 May 2025

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History

  • Received Date: 31 December 2024
  • Revised Date: 14 May 2025
  • Accepted Date: 18 May 2025
  • First Published Date: 19 May 2025

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