A framework for defining diagnostically challenging conditions identifiable through electronic algorithms
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Andrew P.J. Olson
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Jennifer Sloane
Abstract
Diagnostic delays and errors are serious and costly, affecting approximately 5 % of US adults in the outpatient setting annually. Patients with difficult-to-diagnose conditions may spend months or years undergoing diagnostic evaluation in search of a correct diagnosis. Methods are needed to identify patients with diagnostically challenging conditions (DCCs) who are experiencing diagnostic odysseys and, as a result, potential missed opportunities in their diagnosis. Given the increasing availability of longitudinal EHR data to map a patient’s journey, we propose a new framework to proactively identify patients with DCCs using electronic data. These patients are at risk for missed opportunities in diagnosis, and a timelier diagnosis can improve their outcomes. We propose criteria for identifying specific DCCs where the diagnostic process for that condition makes them amenable to detection using EHR-based algorithms. We discuss the application of the proposed framework to an exemplary case study of fibrotic interstitial lung disease and provide examples of algorithms that could be implemented in the future. This work can help identify patients earlier in their diagnostic journeys, resulting in adequate follow-up and fewer missed or delayed diagnoses. Our proposed framework can inform research and potential solutions that are more real-time to potentially mitigate and avoid delays in care and resulting harm.
Funding source: Agency for Healthcare Research and Quality
Award Identifier / Grant number: R01HS028595
Award Identifier / Grant number: R18HS029347
Funding source: Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety
Award Identifier / Grant number: CIN13–413”
Funding source: Pulmonary Fibrosis Foundation
Acknowledgments
The ideas and opinions expressed in this article are solely those of the authors and have not been endorsed by any government or private entity.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This project was partially supported by the Houston Veterans Administration (VA) Health Systems Research (HSR) Center for Innovations in Quality, Effectiveness and Safety (CIN13–413). Dr. Singh is funded in part by the Agency for Healthcare Research and Quality (AHRQ) (R01HS028595 and R18HS029347). Dr. Kaul reports support from the Pulmonary Fibrosis Foundation and a VA HSR Career Development Award (CDA 23-137). Dr. Olson is supported by a grant to study rural health workforce from 3 M, an AHRQ grant (R01HS029318-01A1) to study diagnosis, and a grant to study diagnosis and AI from the Gordon and Betty Moore Foundation, none of which were relevant to this study. These funding sources had no role in the design and conduct of the study, and preparation, review, or approval of the manuscript.
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Data availability: Not applicable.
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