The Accuracy and Efficacy of Large Language Model Written Hospital Course Summaries (CLEAN)
Safety and Workflow Impact of Large Language Model-Assisted Hospital Course Summaries: Protocol for a Randomized, Evaluator-Blinded Non-Inferiority Trial
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Jakub Gazda, MD, PhD
- Phone Number: +421556403517
- Email: jakub.gazda@upjs.sk
Study Contact Backup
- Name: Martin Janicko, MD, PhD
- Phone Number: +421556403527
- Email: martin.janicko@upjs.sk
Study Locations
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-
Košice Region
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Košice, Košice Region, Slovakia, 04001
- Louis Pasteur University Hospital Kosice
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- All consecutively discharged patients, including those who died during hospitalization, will be eligible for inclusion.
Exclusion Criteria:
- No exclusion criteria
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: LLM assisted
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.
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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.
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|
No Intervention: Control
Standard, manual generation of hospital course summary manually by responsible physician.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Safety assessed by outcome evaluator on an ordinal scale (1,2,3)
Time Frame: Assessment at one time point - hospital discharge (up to 5 days)
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Safety will be assessed using an ordinal scale ranging from "acceptable without further changes", through "acceptable with minor revisions", to not "unacceptable in its current form" ( a combination of either absence of important information or presence of incorrect/hallucinated information)
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Assessment at one time point - hospital discharge (up to 5 days)
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Safety of the edited and unedited LLM generated course summaries evaluated on an ordinal scale (1,2,3)
Time Frame: At hospital discharge (up to 5 days)
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Safety of LLM-generated, resident-edited and un-edited hospital course summaries compared with standard specialist-written documentation.
Safety, will be assessed using an 3 categories ordinal scale ranging from "acceptable without further changes" (best outcome), through "acceptable with minor revisions" (uncertain outcome), to "not unacceptable in its current form" (absence of important information or presence of incorrect/hallucinated information; worst outcome)
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At hospital discharge (up to 5 days)
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|
Secondary outcome: Content validity across the following predefined domains (5C).
Time Frame: At hospital discharge (up to 5 days)
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Secondary outcomes measures: detailed evaluation of quality across the following predefined domains (5C): completeness, conciseness, cohesiveness, absence of critical errors (including hallucinations), and cultural and linguistic fidelity.
Individual domains will be separately assessed using a 5-point Likert scale, (1 - complete absence of agreement, 2 - partial absence of agreement, 3 - neutral agreement, 4- partial agreement, 5- complete agreement)
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At hospital discharge (up to 5 days)
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Time to complete hospital summary (seconds)
Time Frame: At hospital discharge (up to 5 days)
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Workflow-related secondary outcomes include time efficiency of hospital course documentation when using the LLM-generated workflow compared with standard practice.
The outcome measure will be the time taken to complete the hospital course summary (seconds), from initiating discharge to "ready to signing".
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At hospital discharge (up to 5 days)
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Generation stability assessed on an ordinal scale (1,2,3)
Time Frame: From hospital discharge to 30 days after hospital discharge
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Generation stability will assess the consistency of LLM generated unedited hospital course summaries when the model is applied repeatedly to identical source clinical documentation.
Generation stability will be evaluated by the same adjudicator on the scale of 1 - stable, 2 - acceptable variation, 3 - unacceptable variation
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From hospital discharge to 30 days after hospital discharge
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Adoption. Percentage of hospital discharges where the LLM generated course summary was utilized.
Time Frame: From hospital discharge to 30 days after hospital discharge
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After completing the randomized controlled trial, the adoption of the LLM-generated workflow will be evaluated over a one-month period.
The outcome measure will be defined as the proportion of eligible discharges for which LLM-derived summary generation was utilized.
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From hospital discharge to 30 days after hospital discharge
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Inter-rater reliability. Percentage agreement between two adjudicators of a hospital course summary on safety and content validity ratings.
Time Frame: From hospital discharge to 30 days after hospital discharge
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Reliability-related outcomes will assess the consistency of expert evaluations of hospital course summaries.
Percentage will quantify the agreement between evaluators on safety and content validity ratings.
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From hospital discharge to 30 days after hospital discharge
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Intra-rater reliability. Concordance of safety and content validity ratings between two evaluations of the same hospital course and same adjudicator.
Time Frame: At hospital discharge (up to 5 days)
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Intra-rater reliability will quantify the consistency of repeated evaluations by the same evaluator based on the concordance of safety and content validity ratings (percentage of agreement)
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At hospital discharge (up to 5 days)
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Temporal stability (test-retest reliability) - concordance between the ratings of the same adjudicator after washout period.
Time Frame: 30 days after hospital discharge
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Temporal stability of evaluator judgments will be assessed through repeated evaluation of identical hospital course summaries by the same evaluator after a predefined 30 day washout period (test-retest reliability).
Outcome measures will be based on the concordance of safety and content validity ratings (percentage)
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30 days after hospital discharge
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jakub Gazda, MD, PhD, Pavol Jozef Šafárik University
Publications and helpful links
General Publications
- Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007 Feb 28;297(8):831-41. doi: 10.1001/jama.297.8.831.
- Ganzinger M, Kunz N, Fuchs P, Lyu CK, Loos M, Dugas M, Pausch TM. Automated generation of discharge summaries: leveraging large language models with clinical data. Sci Rep. 2025 May 12;15(1):16466. doi: 10.1038/s41598-025-01618-7.
- Burden M, Astik G, Auerbach A, Bowling G, Kangelaris KN, Keniston A, Kochar A, Leykum LK, Linker AS, Sakumoto M, Rogers K, Schwatka N, Westergaard S. Identifying and Measuring Administrative Harms Experienced by Hospitalists and Administrative Leaders. JAMA Intern Med. 2024 Sep 1;184(9):1014-1023. doi: 10.1001/jamainternmed.2024.1890.
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- 2IK2026_1
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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