Accepted for/Published in: JMIR Medical Education
Date Submitted: Jul 31, 2023
Open Peer Review Period: Jul 24, 2023 - Sep 18, 2023
Date Accepted: Nov 10, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Generative Language Models Writing Open Notes: Exploring the Promise and Limitations
ABSTRACT
Patient online record access (‘ORA’) is growing worldwide. In some countries, including the U.S. and Sweden, access is advanced with patients obtaining rapid online access to their full records including lab and test results, lists of prescribed medications, vaccinations, and even the very narrative reports written by clinicians (the latter, commonly referred to as ‘open notes’). In the US, patient’s ORA is also available in a downloadable form for use with other apps. While survey research shows some patients report many benefits from ORA, there remain challenges with implementation around writing clinical documentation that patients may now read. With ORA, the functionality of the record is evolving: it is no longer only an aide memoire for doctors but also a communication tool for patients. As a result, some studies suggest clinicians are changing how they write documentation, inviting concerns about the accuracy and completeness of documentation. Other concerns include work burdens: while few objective studies have examined the impact of ORA on workload, some research suggests clinicians are spending more time writing notes and answering queries related to patients’ records. Aimed at addressing some of these concerns, clinician and patient education strategies have been proposed. In this Viewpoint we explore these approaches and suggest another longer-term strategy: the use of generative AI both to support clinicians in documenting narrative summaries that patients will find easier to understand. Applied to narrative clinical documentation, we suggest that such approaches may significantly help preserve the accuracy of notes, strengthen writing clarity and signals of empathy and patient-centered care, and serve as a buffer against documentation work burdens. However, we also consider the current risks associated with existing generative AI. Among other considerations, we emphasize that for this innovation to play a key role in ORA and the co-creation of clinical notes will be imperative. We also caution that clinicians will need to be supported in how to work alongside generative AI to optimize its considerable potential.
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Copyright
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