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ChatGPT's Ability to Assist with Clinical Documentation: A Randomized Controlled Trial
Abstract
INTRODUCTION: Clinical documentation is a critical aspect of health care that enables healthcare providers to communicate effectively with each other and maintain accurate patient care records. Artificial intelligence tools, such as chatbots and virtual assistants, have the potential to assist healthcare providers in clinical documentation. ChatGPT is an artificial intelligence conversational model that generates human-like responses to text-based prompts. In this study, we sought to investigate ChatGPT's ability to assist with writing a history of present illness based on standardized patient histories. METHODS: A blinded, randomized controlled study was conducted to compare the use of typing, dictation, and ChatGPT as tools to document history of present illness (HPI) of standardized patient histories. Eleven study participants, consisting of medical students, orthopaedic surgery residents, and attending surgeons, completed three HPIs using a different documentation technique for each one. Participants were randomized into cohorts based on the type of documentation technique. Participants were asked to interview standardized patients and document the patient's history of present illness using their assigned method. RESULTS: ChatGPT was found to be intermediate for speed; dictation was fastest, but produced markedly longer and higher quality patient histories based on Physician Documentation Quality Instrument score compared with dictation and typing. However, ChatGPT included erroneous information in 36% of the documents. Poor agreement existed on the quality of patient histories between reviewers. DISCUSSION: Our study suggests that ChatGPT has the potential to improve clinical documentation by producing more comprehensive and organized HPIs. ChatGPT can generate longer and more detailed documentation compared with typing or dictation documentation methods. However, additional studies are needed to investigate and address concerns regarding privacy, bias, and accuracy of information.
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“When compared to previous literature AI-driven documentation varied between “bad (29.7)” and “excellent (35.6)” highlighting the variability in documentation produced by AI systems [23, 25, 28].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
In the citing paper
“authors concluded that the combination of dictation and chat GPT would create the greatest efficiency gains [23].”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
In the citing paper
“Baker et al. identified dictation as a non-significant faster alternative to Chat GPT”
From Artificial Intelligence (AI) – Powered Documentation Systems in Healthcare: A Systematic Review (2025) · cites this paper as [23]
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Metadata
- Authors
- Baker, H. P., Dwyer, E., Kalidoss, S., Hynes, K., Wolf, J., & Strelzow, J. A.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Baker, Hayden P.
- Venue
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2023-11-17
- Volume
- 32
- Issue
- 3
- Pages
- 123-129
- ISSN
- 1067-151X (ISSN-L)
- 1940-5480
- OpenAlex
- W4388753652
- Cited by
- 115
- References
- 7
- Citations by year
- 2026: 30 · 2025: 56 · 2024: 29
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Academy of Orthopaedic Surgeons (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Nursing › Research and Theory › Nursing Diagnosis and Documentation
- Keywords
- ChatGPT, clinical documentation, history of present illness, artificial intelligence in healthcare, dictation, typing, documentation quality, standardized patients, orthopaedic residents, medical students
- MeSH terms
- Artificial Intelligence, Communication, Documentation, Health Facilities, Humans, Surgeons
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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