Evidence map›Paper›PMID 40502261›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025

Feasibility of Automated Precharting using GPT-4 in New Specialty Referrals.

April S Liang, Juan M Banda, Thomas Savage, Abby Pandya, Rebecca Carey, Uchechukwu C Megwalu, Michael T Chang, Dev Dash, Conor K Corbin, Aditya Sharma and 5 more

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In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

15 authors.

April S LiangDivision of Clinical Informatics, Stanford University, Palo Alto, CA.
Juan M BandaStanford Health Care, Palo Alto, California.
Thomas SavageDepartment of Medicine, Stanford University, Palo Alto, CA.
Abby PandyaStanford Health Care, Palo Alto, California.
Rebecca CareyStanford Health Care, Palo Alto, California.
Uchechukwu C MegwaluDepartment of Otolaryngology, Stanford University, Palo Alto, CA.
Michael T ChangDepartment of Otolaryngology, Stanford University, Palo Alto, CA.
Dev DashDepartment of Emergency Medicine, Stanford University, Palo Alto, CA.
Conor K CorbinSmarterDx, New York, NY.
Aditya SharmaStanford Health Care, Palo Alto, California.
Rahul ThapaCenter for Biomedical Informatics Research, Stanford University, Palo Alto, CA.
Nikesh KotechaStanford Health Care, Palo Alto, California.
Nigam H ShahDepartment of Medicine, Stanford University, Palo Alto, CA.
Jennifer Y LeeDepartment of Otolaryngology, Stanford University, Palo Alto, CA.
Jonathan H ChenDepartment of Medicine, Stanford University, Palo Alto, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the feasibility of using GPT-4 to automate precharting for specialty referrals, focusing on new patients referred to an otolaryngology clinic for nasal congestion. We describe the design decisions and strategies tested in creating this precharting utility, including methods for prompt design and token limit handling. Through iterative testing and building, our tool achieved 95.0% agreement with physician consensus in a small retrospective test sample. Results from a small prospective pilot showed favorable feedback of summaries in a real-world clinical setting, though there was a discrepancy between high intention to use the summary but lower perception of time savings. Our results demonstrate that automated pre-charting with accuracy and clinical relevance can be feasible with large language models such as GPT-4. Our design features can inform the development of vendor chart summarization solutions.

Identifiers

PMID40502261
PMCPMC12150724

What OpenQuestion holds

Textmetadata
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.