HospitalizationsCLEANNCT07491068
The Accuracy and Efficacy of Large Language Model Written Hospital Course Summaries
A study testing LLM assisted workflow for generating the hospital course summary in people with Hospitalizations, run by Pavol Jozef Safarik University at 1 site in Slovakia.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hospitalizations
- What is being tested?
- LLM assisted workflow for generating the hospital course summary
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 786 participants
- Where
- 1 siteSlovakia
- Study dates
- 1 Jun 2026to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All consecutively discharged patients, including those who died during hospitalization, will be eligible for inclusion.
Who cannot join
- No exclusion criteria
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Slovakia1 site
Louis Pasteur University Hospital Kosice
Košice, Košice Region, 04001
About this study
Official title: Safety and Workflow Impact of Large Language Model-Assisted Hospital Course Summaries: Protocol for a Randomized, Evaluator-Blinded Non-Inferiority Trial
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.
Read the detailed description
The World Health Organization has identified a severe global health workforce crisis, estimating a shortage of approximately 12.7 million physicians worldwide in 2020. Projections indicate that this deficit will continue to worsen in the coming years [1]. Furthermore, these shortages occur alongside rising healthcare demand driven by population ageing and the increasing prevalence of multimorbidity. Despite that, administrative burden remains a persistent and systemic challenge across healthcare systems worldwide. Initiatives aimed at reducing physicians' non-clinical workload have achieved only limited success, leading to the continued diversion of clinical expertise away from direct patient care [2]. Among administrative responsibilities, the preparation of discharge letters is one of the most time-consuming tasks for physicians caring for hospitalized patients, yet it is also central to ensuring continuity of care after discharge. Despite major time allocation, discharge summaries frequently fail to meet expected standards, with many physicians reporting shortcomings in both their completeness and quality [3], [4]. The adoption of electronic medical records has prompted efforts to alleviate this burden by transitioning from fully manual authorship toward partial automation, enabling selected sections of discharge letters to be generated automatically. However, the hospital course-the most clinically informative component of the discharge letter-remains almost universally dependent on manual narrative documentation by the treating physician. B. Objectives The primary objective of this study is to evaluate whether LLM-assisted, specialist-edited generation of hospital course summaries is safe for use in routine inpatient care, compared with standard clinician-written documentation. Secondary objectives include evaluating the safety of resident-edited and unedited LLM-generated hospital course summaries. Additional objectives are to assess the quality of LLM-assisted summaries across predefined domains, evaluate the stability of generated outputs, quantify the impact of AI-assisted workflows on physician documentation time, examine clinician adoption of LLM-supported documentation following completion of the randomized phase, and assess the reliability of evaluator ratings. Collectively, these objectives aim to determine the safety and feasibility of integrating LLM-based narrative generation into real-world hospital discharge workflows.
- Conditions
- Hospitalizations
- Being tested
- LLM assisted workflow for generating the hospital course summary
- Keywords
- generative large language models, automated discharge summary, AI, documentation, Electronic health records
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
LLM assisted workflow for generating the hospital course summaryDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| LLM assisted | Experimental | 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. Receives: Device: LLM assisted workflow for generating the hospital course summary |
| Control | No intervention | Standard, manual generation of hospital course summary manually by responsible physician. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Safety assessed by outcome evaluator on an ordinal scale (1,2,3)
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)
Measured: Assessment at one time point - hospital discharge (up to 5 days)
Secondary outcomes (8)
Safety of the edited and unedited LLM generated course summaries evaluated on an ordinal scale (1,2,3)
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)
Measured: At hospital discharge (up to 5 days)
Secondary outcome: Content validity across the following predefined domains (5C).
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)
Measured: At hospital discharge (up to 5 days)
Time to complete hospital summary (seconds)
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".
Measured: At hospital discharge (up to 5 days)
Generation stability assessed on an ordinal scale (1,2,3)
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
Measured: From hospital discharge to 30 days after hospital discharge
Adoption. Percentage of hospital discharges where the LLM generated course summary was utilized.
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.
Measured: From hospital discharge to 30 days after hospital discharge
Inter-rater reliability. Percentage agreement between two adjudicators of a hospital course summary on safety and content validity ratings.
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.
Measured: From hospital discharge to 30 days after hospital discharge
Intra-rater reliability. Concordance of safety and content validity ratings between two evaluations of the same hospital course and same adjudicator.
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)
Measured: At hospital discharge (up to 5 days)
Temporal stability (test-retest reliability) - concordance between the ratings of the same adjudicator after washout period.
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)
Measured: 30 days after hospital discharge
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2026
- Study start1 Jun 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for HospitalizationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 786 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Pavol Jozef Safarik University
- Sponsor type
- University, hospital or other
- Collaborators
- Louis Pasteur University Hospital, Košice
- Responsible party
- Principal investigator, Martin Janičko, Pavol Jozef Safarik University
- Investigators
- Jakub Gazda, MD, PhD, Principal investigator, Pavol Jozef Safarik University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07491068
- Sponsor's protocol ID
- 2IK2026_1
- Acronym
- CLEAN
- Status last verified
- Mar 2026
- First submitted
- 25 Feb 2026
- Last updated
- 30 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 40355506Kripalani 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.
PubMed 17327525Burden 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.
PubMed 38913371
Study at a glance
From the registry record, last updated by the sponsor on 30 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Safety and Workflow Impact of Large Language Model-Assisted Hospital Course Summaries: Protocol for a Randomized, Evaluator-Blinded Non-Inferiority Trial
- ClinicalTrials.gov ID
- NCT07491068
- Acronym / sponsor ID
- CLEAN / 2IK2026_1
- Conditions
- Hospitalizations
- Keywords
- generative large language models, automated discharge summary, AI, documentation, Electronic health records
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 786 participants (estimated)
- Lead sponsor
- Pavol Jozef Safarik UniversityUniversity, hospital or other
- Collaborators
- Louis Pasteur University Hospital, Košice
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 24 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The World Health Organization has identified a severe global health workforce crisis, estimating a shortage of approximately 12.7 million physicians worldwide in 2020. Projections indicate that this deficit will continue to worsen in the coming years [1]. Furthermore, these shortages occur alongside rising healthcare demand driven by population ageing and the increasing prevalence of multimorbidity. Despite that, administrative burden remains a persistent and systemic challenge across healthcare systems worldwide. Initiatives aimed at reducing physicians' non-clinical workload have achieved only limited success, leading to the continued diversion of clinical expertise away from direct patient care [2]. Among administrative responsibilities, the preparation of discharge letters is one of the most time-consuming tasks for physicians caring for hospitalized patients, yet it is also central to ensuring continuity of care after discharge. Despite major time allocation, discharge summaries frequently fail to meet expected standards, with many physicians reporting shortcomings in both their completeness and quality [3], [4]. The adoption of electronic medical records has prompted efforts to alleviate this burden by transitioning from fully manual authorship toward partial automation, enabling selected sections of discharge letters to be generated automatically. However, the hospital course-the most clinically informative component of the discharge letter-remains almost universally dependent on manual narrative documentation by the treating physician. B. Objectives The primary objective of this study is to evaluate whether LLM-assisted, specialist-edited generation of hospital course summaries is safe for use in routine inpatient care, compared with standard clinician-written documentation. Secondary objectives include evaluating the safety of resident-edited and unedited LLM-generated hospital course summaries. Additional objectives are to assess the quality of LLM-assisted summaries across predefined domains, evaluate the stability of generated outputs, quantify the impact of AI-assisted workflows on physician documentation time, examine clinician adoption of LLM-supported documentation following completion of the randomized phase, and assess the reliability of evaluator ratings. Collectively, these objectives aim to determine the safety and feasibility of integrating LLM-based narrative generation into real-world hospital discharge workflows.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| LLM assisted | Experimental | Device: LLM assisted workflow for generating the hospital course summaryThe 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. |
| Control | No intervention | –Standard, manual generation of hospital course summary manually by responsible physician. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All consecutively discharged patients, including those who died during hospitalization, will be eligible for inclusion.
Key exclusion criteria (1 listed)
- No exclusion criteria
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| LLM assisted workflow for generating the hospital course summaryThe 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. | Device | LLM assisted |
Primary outcome
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)
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)
8 secondary outcomes
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)
Secondary outcome: Content validity across the following predefined domains (5C).
Time frame: At hospital discharge (up to 5 days)
Time to complete hospital summary (seconds)
Time frame: At hospital discharge (up to 5 days)
Generation stability assessed on an ordinal scale (1,2,3)
Time frame: From hospital discharge to 30 days after hospital discharge
Adoption. Percentage of hospital discharges where the LLM generated course summary was utilized.
Time frame: From hospital discharge to 30 days after hospital discharge
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
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)
Temporal stability (test-retest reliability) - concordance between the ratings of the same adjudicator after washout period.
Time frame: 30 days after hospital discharge
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Mar 2026.
- Background: PubMed 40355506Ganzinger 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.
- Background: PubMed 17327525Kripalani 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.
- Background: PubMed 38913371Burden 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jakub Gazda, MD, PhD principal investigator
Pavol Jozef Safarik University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing LLM assisted workflow for generating the hospital course summary in people with Hospitalizations, run by Pavol Jozef Safarik University at 1 site in Slovakia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 30 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Slovakia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jakub Gazda, MD, PhD (+421556403517, jakub.gazda@upjs.sk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07491068, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Mar 2026.