Evidence map›Paper›PMID 39493632›Full record

ArticleDigital health

Generative artificial intelligence writing open notes: A mixed methods assessment of the functionality of GPT 3.5 and GPT 4.0.

Anna Kharko, Brian McMillan, Josefin Hagström, Irene Muli, Gail Davidge, Maria Hägglund, Charlotte Blease

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Anna KharkoParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0003-0908-6173
Brian McMillanCentre for Primary Care and Health Services Research, The University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-0683-3877
Josefin HagströmParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Irene MuliParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Gail DavidgeCentre for Primary Care and Health Services Research, The University of Manchester, Manchester, UK.
Maria HägglundParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Charlotte BleaseParticipatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Worldwide, patients are increasingly being offered access to their full online clinical records including the narrative reports written by clinicians (so-called "open notes"). Against these developments, there is growing interest in the use of generative artificial intelligence (AI) such as OpenAI's ChatGPT to co-assist clinicians with patient-facing documentation. Objective: This study aimed to explore the effectiveness of OpenAI's ChatGPT 3.5 and GPT 4.0 in generating three patient-facing clinical notes from fictional general practice narrative reports. Methods: On 1 October 2023 and 1 November 2023, we used ChatGPT 3.5 and 4.0 to generate notes for three validated fictional general practice notes, using a prompt in the style of a British primary care note for three commonly presented conditions: (1) type 2 diabetes, (2) major depressive disorder, and (3) a differential diagnosis for suspected bowel cancer. Outputs were analyzed for reading ease, sentiment analysis, empathy, and medical fidelity. Results: ChatGPT 3.5 and 4.0 wrote longer notes than the original, and embedded more second person pronouns, with ChatGPT 3.5 scoring higher on both. ChatGPT expanded abbreviations, but readability metrics showed that the notes required a higher reading proficiency, with ChatGPT 3.5 demanding the most advanced level. Across all notes, ChatGPT offered higher signatures of empathy across cognitive, compassion/sympathy, and prosocial cues. Medical fidelity ratings varied across all three cases with ChatGPT 4.0 rated superior. Conclusions: While ChatGPT improved sentiment and empathy metrics in the transformed notes, compared to the original they also required higher reading proficiency and omitted details impacting medical fidelity.

Indexed as

documentationelectronic health recordsgeneral practiceGenerative artificial intelligenceonline record accessopen notespatient-centered careprimary care

Identifiers

PMID39493632
PMCPMC11528788

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.