[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100659599":3},{"organization":4,"outcomesModule":7,"designInfo":57,"detailedDescription":69,"studyPopulation":62,"armGroups":70,"interventions":81,"overallOfficials":86,"centralContacts":90,"locations":62,"responsibleParty":95,"collaborators":97,"id":100,"slug":101,"hasResults":102,"nctId":103,"briefTitle":104,"officialTitle":105,"acronym":106,"eligibilityCriteria":107,"healthyVolunteers":102,"sex":108,"minAge":109,"maxAge":62,"enrollmentInfo":110,"targetDuration":62,"studyType":113,"phases":114,"briefSummary":116,"conditions":117,"keywords":124,"overallStatus":136,"whyStopped":62,"lastUpdateSubmitDate":137,"lastUpdatePostDateStruct":138,"startDateStruct":141,"completionDateStruct":143,"leadSponsor":145,"locationsCount":62},{"fullName":5,"class":6},"Unity Health Toronto","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":32},[9],{"measure":10,"description":11,"timeFrame":12},"Time-in-note (minutes per discharge summary)","Total active time (minutes) spent writing and editing the discharge summary by all clinicians contributing to the note (staff physician and, on teaching services, resident physicians), measured per discharge episode using Epic audit log \"time in note\" metadata. Includes teaching and non-teaching services.","Per discharge episode, from note creation to final signature (including staff attestation where applicable), assessed up to 30 days after discharge.",[14,18,21,24,28],{"measure":15,"description":16,"timeFrame":17},"Time from discharge to discharge summary signature (hours)","Time in hours between the patient's recorded hospital discharge and the final signature of the discharge summary, from Epic metadata. Summaries signed before discharge are recorded as negative values.","Per discharge episode, assessed up to 30 days after discharge.",{"measure":19,"description":20,"timeFrame":17},"Expert-rated documentation quality","Blinded manual review of a random sample of 200 discharge summaries (100 per arm), each rated independently by a hospitalist and a primary care physician using the validated and standardized Provider Documentation Summarization Quality Instrument (PDSQI-9). Eight attributes (accurate, cited, comprehensible, organized, succinct, synthesized, thorough, useful) are each rated on a 5-point Likert scale (1 to 5) and summed to a total score ranging from 8 to 40; higher scores indicate better quality. The ninth attribute (free of stigmatizing language) is rated yes\u002Fno and reported separately. Reviewers will also verify accuracy against the medical record by chart review in Epic, recording omissions and factual errors in: administrative details (admission and discharge dates), diagnoses, medications and treatments, investigations, and subspecialty consultations.",{"measure":22,"description":23,"timeFrame":17},"Automated documentation quality (PDSQI-9, LLM-as-judge)","PDSQI-9 total score for all discharge summaries, assigned by a large language model using the published PDSQI-9 LLM-as-judge approach (Croxford et al., JAMIA 2025). Total score range 9 to 45; higher scores indicate better quality.",{"measure":25,"description":26,"timeFrame":27},"Physician task load (PTL-4)","Four NASA-TLX subscales (mental demand, physical demand, temporal demand, effort), each rated 0 to 100 while reflecting on a representative clinical workday in the past 1 to 2 weeks. Subscales are summed into a composite score of 0 to 400; higher scores indicate greater workload. Measured once per clinician.","Once per clinician, within 2 weeks of completion of an inpatient service block during the active study period (for clinicians completing more than one block, the first completed block).",{"measure":29,"description":30,"timeFrame":31},"Frequency of AI tool use","Proportion of discharge summaries in which an AI-generated hospital course draft was generated, reported by arm (use in the control arm measures contamination).","Per discharge episode during the active study period.",[33,37,40,43,46,50,54],{"measure":34,"description":35,"timeFrame":36},"Total attending time in the patient chart on the day of discharge (minutes)","Total minutes the staff physician spends with the patient's chart open on the calendar day of discharge (00:00 to 23:59), measured from Epic audit log metadata.","From randomization through the end of the active study period.",{"measure":38,"description":39,"timeFrame":36},"Discharge time of day","Clock time of the patient's recorded hospital discharge, from Epic metadata, analyzed as a continuous measure and as the proportion of patients discharged before 11:00.",{"measure":41,"description":42,"timeFrame":17},"Staff physician attestation lag (hours)","Time in hours between the resident's signature and the staff physician's attestation of the discharge summary. Teaching services only.",{"measure":44,"description":45,"timeFrame":27},"Physician wellbeing (Professional Fulfillment Index)","Stanford Professional Fulfillment Index subscale scores (professional fulfillment, work exhaustion, interpersonal disengagement), each scaled 0 to 100. Measured per clinician.",{"measure":47,"description":48,"timeFrame":49},"30-day all-cause readmission","Proportion of discharge episodes followed by an all-cause readmission to the discharging hospital within 30 days.","Up to 30 days after patient discharge",{"measure":51,"description":52,"timeFrame":53},"30-day emergency department visit","Proportion of discharge episodes followed by an ED visit to the discharging hospital within 30 days.","Up to 30 days after patient discharge.",{"measure":55,"description":56,"timeFrame":27},"System Usability Scale (SUS) score for the AI hospital course tool (intervention arm only)","Perceived usability of the Epic Hospital Course Generator, measured with the System Usability Scale (Brooke, 1996). The SUS has 10 items rated on a 5-point Likert scale (strongly disagree to strongly agree), with alternating positively and negatively worded items. Item scores are converted to a total score from 0 to 100; higher scores indicate better usability. Item wording will refer to the Hospital Course Generator in place of \"this system.\" Completed once by staff physicians randomized to the intervention arm and reported descriptively; no between-arm comparison.",{"allocation":58,"interventionModel":59,"interventionModelDescription":60,"primaryPurpose":61,"observationalModel":62,"timePerspective":62,"maskingInfo":63},"RANDOMIZED","PARALLEL","Multicenter, pragmatic, parallel-group randomized controlled trial with 1:1 allocation of staff (attending) physicians to access to the Epic Hospital Course Generator or usual care. The staff physician is the unit of randomization; resident physicians on teaching services inherit the allocation of their supervising staff physician. Outcomes are measured at the discharge episode level, with clustering by staff physician accounted for in the analysis.","HEALTH_SERVICES_RESEARCH",null,{"masking":64,"maskingDescription":65,"whoMasked":66},"DOUBLE","The trial statistician will remain masked to allocation until completion of the primary analysis.",[67,68],"INVESTIGATOR","OUTCOMES_ASSESSOR","Discharge summaries are a key communication tool at care transitions, but they are time-consuming to write and are often delayed or incomplete. Generative AI tools embedded in the electronic health record can draft a summary of the hospital course from existing chart data, but their effect on physician time, documentation quality, and workload has not been evaluated in a randomized trial.\n\nThis multicenter pragmatic trial randomizes staff physicians on general medicine\u002Fhospitalist services to access to the Epic Hospital Course Generator, a large language model tool that drafts a hospital course narrative on request, or to usual care. Documentation time and completion timing are measured with Epic audit log metadata. Documentation quality is assessed by expert physician review of a sample of discharge summaries and by an automated LLM-as-judge approach using the PDSQI-9. Physician workload and tool usability are assessed by survey.",[71,77],{"label":72,"type":73,"description":74,"interventionNames":75},"Epic Hospital Course Generator","EXPERIMENTAL","Staff physicians randomized to this arm, and resident physicians working under them on teaching services, will have access to the Epic Hospital Course Generator within the Epic electronic health record during the study period. Clinicians in this arm will be notified that they have access to the tool and will be given instructions on how to use it. Use of the tool is optional. Clinicians remain responsible for reviewing any AI-generated content and deciding whether and how to incorporate it into the discharge summary.",[76],"Other: Epic Discharge Summary Hospital Course Generator",{"label":78,"type":79,"description":80,"interventionNames":62},"Control","NO_INTERVENTION","Staff physicians randomized to this arm, and resident physicians working under them on teaching services, will not have the Epic Hospital Course Generator available for use during the study period and will complete discharge summaries using usual documentation workflows.",[82],{"type":6,"name":83,"description":84,"armGroupLabels":85,"otherNames":62},"Epic Discharge Summary Hospital Course Generator","A generative AI (large language model) tool embedded in Epic that drafts a narrative of the patient's hospital course from existing chart data, including clinician notes, investigations, and medications and treatments. A draft is generated only when a clinician requests it, can be generated at any time during the hospitalization, and can be regenerated an unlimited number of times. Clinicians can choose a narrative (chronological) format, a problem-based format with a numbered entry for each clinical problem managed during the hospitalization, or an organ system-based format. The draft appears in a separate tab and is not automatically inserted into the discharge summary; clinicians may copy, edit, or disregard it.",[72],[87],{"name":88,"affiliation":5,"role":89},"Michael Colacci, MD PhD","PRINCIPAL_INVESTIGATOR",[91],{"name":88,"role":92,"phone":93,"phoneExt":62,"email":94},"CONTACT","416-360-4000","michael.colacci@unityhealth.to",{"type":96,"investigatorFullName":62,"investigatorTitle":62,"investigatorAffiliation":62,"oldNameTitle":62,"oldOrganization":62},"SPONSOR",[98],{"name":99,"class":6},"University of California, San Diego","100659599","a-pragmatic-trial-of-ai-assisted-discharge-summary-generation-100659599",false,"NCT07866001","A Pragmatic Trial of AI-Assisted Discharge Summary Generation","An EHR-embedded Pragmatic Parallel-group, Operationally-driven Clinical Trial of AI-assisted Discharge Summary Generation","DRAFT-AI","Inclusion Criteria:\n\n* Healthcare provider at participating hospital on a hospitalist ward (teaching or non-teaching ward)\n* English speaking\n\nExclusion Criteria:\n\n* Planned leave during study period","ALL","18 Years",{"count":111,"type":112},80,"ESTIMATED","INTERVENTIONAL",[115],"NA","The goal of this study is to learn whether an artificial intelligence (AI) tool built into the electronic health record helps hospital physicians write discharge summaries. A discharge summary is the document that tells a patient's family doctor and other providers what happened during a hospital stay.\n\nThe main questions this study aims to answer are:\n\nDoes access to the AI tool reduce the time physicians spend writing discharge summaries? Does it change the quality of discharge summaries or how soon they are completed? How does it affect physician workload?\n\nPhysicians will be randomly assigned to either have access to the AI tool or complete discharge summaries as they usually do. Physicians with access can choose whether to use the tool, and they review and edit any AI-generated text before it becomes part of the discharge summary.",[118,119,120,121,122,123],"Inpatient Internal Medicine Patients","Patient Discharge","Documentation","Artificial Intelligence (AI)","Electronic Health Records","Workload",[125,126,127,128,129,130,131,132,133,134,135],"Discharge Summary","AI Note Writing Tool","Generative artificial intelligence","Large language model","Clinical documentation","Electronic health record","Epic","Documentation burden","Hospital medicine","Physician workload","Pragmatic randomized controlled trial","NOT_YET_RECRUITING","2026-10-06",{"date":139,"type":140},"2026-10-08","ACTUAL",{"date":142,"type":112},"2026-10-07",{"date":144,"type":112},"2027-02",{"name":5,"class":6}]