Tracing the Pen: Electronic Health Records Amid the Rise of Generative AI
Large language models (LLMs) are poised to transform physician interactions with electronic health records (EHRs) by assisting clinical documentation, drafting preliminary diagnostic reports, and supporting patient communication. While LLMs reduce administrative burden, their integration in clinical workflows introduces the risk of blending AI- and human-generated content within EHRs. This perspective reviews technological and policy solutions to ensure traceability of AI-generated content in EHRs to preserve clinical integrity.
Authors
- Adam Landman (ORCID: https://orcid.org/0000-0002-2166-0521)
- Eric G. Poon (ORCID: https://orcid.org/0000-0002-7251-5842)
- Rebecca G. Mishuris (ORCID: https://orcid.org/0000-0002-4804-3128)
- Marc D. Succi (ORCID: https://orcid.org/0000-0002-1518-3984)
- Jacqueline G. You
- Danielle S. Bitterman
- Arash A. Nargesi
Institutions
- Broad Institute (US)
- Political Research Associates (US)
- Brigham and Women's Hospital (US)
- Duke University (US)
- Brown University (US)
- Massachusetts General Hospital (US)
- Mass General Brigham (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1038/s41746-026-02508-6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute