Evidence map›Paper›PMID 41726458›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert Assessment.

Tyler Osborne, Sadia Abbasi, Stephanie Hong, Robert Sexton, Jonathan Ambut, Neil J Patel, Richard N Rosenthal, Lyncean Ung, Fusheng Wang, Rachel Wong

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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

10 authors.

Tyler OsborneDepartment of Computer Science, Stony Brook University, Stony Brook, New York.
Sadia AbbasiRenaissance School of Medicine, Stony Brook University, Stony Brook, New York.
Stephanie HongRenaissance School of Medicine, Stony Brook University, Stony Brook, New York.
Robert SextonRenaissance School of Medicine, Stony Brook University, Stony Brook, New York.
Jonathan AmbutDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
Neil J PatelDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
Richard N RosenthalRenaissance School of Medicine, Stony Brook University, Stony Brook, New York.
Lyncean UngDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, New York.
Fusheng WangDepartment of Computer Science, Stony Brook University, Stony Brook, New York.
Rachel WongDepartment of Biomedical Informatics, Stony Brook University, Stony Brook, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models (LLMs) have demonstrated potential to automate clinical documentation tasks that may reduce clinician burden, such as generation of hospital discharge summaries. Prior research used older LLMs and limited data, raising concerns about fabrications and omissions. In this study, we evaluated the automatic generation of inpatient Internal Medicine discharge summaries using a HIPAA-compliant Microsoft Azure instance of OpenAI's GPT-4o. Both human-written and AI-generated discharge summaries were scored by Internal Medicine hospital faculty for quality, readability/conciseness, factuality and completeness, presence of hallucinations/omissions and their impact on safety, and compared with the actual discharge summaries. Our results showed that the AI-generated discharge summaries significantly outperformed actual human written summaries in both quality and readability/conciseness and were comparable to humans in factuality and completeness, with a minimal cost.

Indexed as

Electronic Health RecordsLarge Language ModelsPatient Discharge SummariesGenerative Artificial IntelligenceHumansNatural Language ProcessingUnited States

Identifiers

PMID41726458
PMCPMC12919585

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.