The Accuracy and Efficacy of Large Language Model Written Hospital Course Summaries

Summary

Background: Physicians worldwide face an increasing administrative burden that diverts time from direct patient care. Among inpatient documentation tasks, authoring hospital course summaries is particularly time-consuming and critical for safe care transitions. Large language models (LLMs) have shown promise for clinical text generation; however, robust evidence from randomized, evaluator-blinded trials conducted in routine hospital practice remains limited. Objectives: The CLEAN study aims to evaluate whether LLM-assisted, specialistedited generation of hospital course summaries is non-inferior in safety compared with standard clinician-written documentation in routine inpatient care. Secondary objectives include noninferiority assessments of resident-edited and unedited LLMgenerated summaries. Additional objectives are to evaluate summary quality across predefined domains, quantify physician documentation time, assess LLM generation stability, measure clinician adoption following the randomized phase, and examine inter-, intra-observer, and test-retest reliability of expert assessments. Methods: This is a single-centre, double-campus, exploratory randomized controlled non-inferiority trial conducted at a tertiary university hospital. Consecutive hospital discharges across multiple clinical departments are randomized 1:1 to either an LLM-assisted documentation workflow or standard manual authorship. The intervention integrates an on-premise LLM into a parallel hospital information system, generating draft hospital course summaries from complete, uncurated clinical documentation, which physicians may review and edit prior to finalization. Safety, the primary outcome, defined as presence of all important information and absence of incorrect/hallucinated information, is assessed by an adjudication committee blinded to documentation workflow. Secondary outcomes include content validity, workflow efficiency, generation stability, post-trial clinician adoption, and reliability metrics. A total of 786 discharge episodes are required to assess non-inferiority using a predefined margin of 5 percentage points. Ethics and Dissemination: The study will be conducted in accordance with the Declaration of Helsinki, Good Clinical Practice, and the General Data Protection Regulation. A waiver of informed consent is sought due to minimal risk and exclusive use of routine clinical data. Results will be disseminated through peer-reviewed publication and engagement with healthcare stakeholders.

Conditions

• Hospitalizations

Interventions

DEVICE

LLM assisted workflow for generating the hospital course summary

The intervention consists of an LLM assisted workflow for generating the hospital course summary at discharge. The treating physician initiates discharge using an application - CorteVision Hospital Suite - connected to the hospital Informix database. The output of the model - generated draft is returned to the application interface, where the treating physician reviews and may edit, correct, expand, or shorten the text before finalization. The finalized hospital course summary is entered into the medical record only after physician review and confirmation.

Sponsors & Collaborators

• Louis Pasteur University Hospital, Košice

  collaborator UNKNOWN

• Pavol Jozef Safarik University

  lead OTHER

Principal Investigators

• Jakub Gazda, MD, PhD · Pavol Jozef Safarik University

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-06-01

Primary Completion

2026-12-31

Completion

2027-12-31

Countries

• Slovakia

Study Locations