Article

Physician- and Large Language Model-Generated Hospital Discharge Summaries: A Blinded, Comparative Quality and Safety Study

2024-09-30

Abstract excerpt

<h4>Importance</h4> High quality discharge summaries are associated with improved patient outcomes but contribute to clinical documentation burden. Large language models (LLMs) provide an opportunity to support physicians by drafting discharge summary narratives. <h4>Objective</h4> To determine whether LLM-generated discharge summary narratives are of comparable quality and safety to those of physicians. <h4>Desig...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 220429dc-b9b8-5ae0-b92d-fb6e4d8036dc
DOI: 10.1101/2024.09.29.24314562

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Physician- and Large Language Model-Generated Hospital Discharge Summaries: A Blinded, Comparative Quality and Safety StudyDOI 10.1101/2024.09.29.24314562Read

Clinician-Centered Evaluation of Large Language Model-Generated Discharge Summaries for Longer Hospitalizations: Insights from Hospitalists and Primary Care PhysiciansSemantic proximity Read

Physician- versus Large Language Model-Generated Clinical Summaries in the Emergency DepartmentSemantic proximity Read

MedAgentBrief for Hospital Course Summarization: Safety, Use, and Discharge Documentation BurdenSemantic proximity Read

Evaluating Large Language Models for Drafting Emergency Department Discharge SummariesSemantic proximity Read

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

Evaluating an LLM-Assisted Workflow for Clinical Documentation: <i>A Pilot Randomized Controlled Trial on Time and Quality</i>Semantic proximity Read

AI-generated patient-friendly discharge summaries to empower patientsSemantic proximity Read

Assessing the Limitations of Large Language Models in Clinical Practice Guideline-concordant Treatment Decision-making on Real-world DataSemantic proximity Read

Large Language Models Are Highly Vulnerable to Adversarial Hallucination Attacks in Clinical Decision Support: A Multi-Model Assurance AnalysisSemantic proximity Read

A Framework to Assess Clinical Safety and Hallucination Rates of LLMs for Medical Text SummarisationSemantic proximity Read

AI as a signal assessor – Can a Large Language Model perform causality assessment on a case series?Semantic proximity Read

An interdisciplinary, randomized, single-blind evaluation of state-of-the-art large language models for their implications and risks in medical diagnosis and managementSemantic proximity Read