Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

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)

The final, peer-reviewed published version of this preprint can be found here:

Generative Language Models and Open Notes: Exploring the Promise and Limitations

Blease C, Torous J, McMillan B, Hägglund M, Mandl KD

Generative Language Models and Open Notes: Exploring the Promise and Limitations

JMIR Med Educ 2024;10:e51183

DOI: 10.2196/51183

PMID: 38175688

PMCID: 10797501

Warning: This is an author submission that is not peer-reviewed or edited.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

  • Charlotte Blease; 
  • John Torous; 
  • Brian McMillan; 
  • Maria Hägglund; 
  • Kenneth D Mandl

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.


 Citation

Please cite as:

Blease C, Torous J, McMillan B, Hägglund M, Mandl KD

Generative Language Models and Open Notes: Exploring the Promise and Limitations

JMIR Med Educ 2024;10:e51183

DOI: 10.2196/51183

PMID: 38175688

PMCID: 10797501

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.