An interventional study of AI Clinical Decision Support: Automated EHR Data Retrieval and AI Clinical Decision Support: Admission H&P Note in Clinical Decision Support, sponsored by Cedars-Sinai Medical Center. Not yet recruiting at 2 sites in United States. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-22.
Sponsored by Cedars-Sinai Medical Center · Not applicable, Interventional, and Health services research
This study will evaluate the feasibility of using artificial intelligence (AI)-generated clinical decision support during routine inpatient hospital medicine care. Participating hospitalist physicians may receive AI-generated recommendations for eligible hospitalized patients under their care. Eligible patient cases will be randomly assigned to one of three AI prompting strategies that differ in the clinical information provided to the AI system. After reviewing each recommendation, participating physicians will complete a brief survey assessing the clinical usefulness of the recommendation, how they interpreted it, and whether it was or will be incorporated into patient management.
The study will assess whether AI-generated recommendations can be successfully generated and delivered during routine clinical care and whether physicians complete the study surveys and adopt the recommendations. It will also provide preliminary information about physicians' perceptions of AI-generated recommendations across the three prompting strategies. The findings will be used to inform the design of a future larger randomized trial of AI-initiated clinical decision support.
This is a three-arm randomized feasibility trial evaluating AI-initiated clinical decision support in routine inpatient hospital medicine practice. Eligible patient cases cared for by participating hospitalist physicians will be randomized to one of three AI prompting strategies: automated electronic health record (EHR) data retrieval, automated EHR data retrieval with explicit inclusion of the admission history and physical (H\&P) note, or the admission H\&P note alone. OpenEvidence will generate a clinical decision support recommendation for each randomized case.
The recommendation will be delivered to the attending hospitalist, who will then complete a brief electronic survey assessing the clinical utility and interpretation of the recommendation and whether it was implemented or planned for implementation. The study will assess the feasibility of generating and delivering AI-initiated recommendations during routine clinical care, physician survey completion, timely survey completion, and recommendation adoption. Findings from this feasibility trial will inform the design of a future larger randomized trial.
Cedars-Sinai Medical Center is the lead sponsor of 455 studies on the registry; 108 are open to participants now.
Of its 65 completed or terminated interventional studies of FDA-regulated products, 49 (75%) have results posted.
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Exclusion Criteria:
Arm D (Data Retrieval Only): Eligible patient cases assigned to this arm will have the OpenEvidence recommendation generated using automated electronic health record (EHR) data retrieval without explicit inclusion of the admission history and physical (H\&P) note.
Other: AI Clinical Decision Support: Automated EHR Data Retrieval
Arm DH (Data Retrieval + History and Physical): Eligible patient cases assigned to this arm will have the OpenEvidence recommendation generated using automated EHR data retrieval with explicit inclusion of the admission H\&P note.
Other: AI Clinical Decision Support: Automated EHR Data Retrieval · Other: AI Clinical Decision Support: Admission H&P Note
Arm H (History and Physical Only): Eligible patient cases assigned to this arm will have the OpenEvidence recommendation generated using the admission H\&P note only, without automated EHR data retrieval.
Other: AI Clinical Decision Support: Admission H&P Note
OpenEvidence generates an AI-based clinical decision support recommendation using automated retrieval of available EHR data.
The admission H\&P note is explicitly provided to OpenEvidence as clinical context for generation of the AI-based clinical decision support recommendation.
Successful AI Recommendation Delivery Rate
Proportion of randomized patient cases in which an AI-generated recommendation is successfully delivered to the attending hospitalist.
Time frame: Within 24 hours of hospital admission
Physician Survey Completion Rate
Proportion of successfully delivered AI-generated recommendations for which the attending hospitalist completes the electronic physician survey.
Time frame: Within 48 hours after AI recommendation delivery
Timely Physician Survey Completion Rate
Proportion of successfully delivered AI-generated recommendations for which the attending hospitalist completes the electronic physician research survey within 12 hours of recommendation delivery.
Time frame: Within 12 hours of AI recommendation delivery
Recommendation Adoption Rate
Proportion of AI-generated recommendations that the attending hospitalist reports as implemented or planned for implementation. Responses will be recorded separately as implemented, planned but not yet implemented, or not adopted.
Time frame: Within 48 hours after AI recommendation delivery
Physician-Perceived Clinical Utility
Physician-perceived clinical utility of the AI-generated recommendation, assessed at the patient-case level using a single-item 0-10 numeric rating scale, with anchors at 0 ("Not at all useful"), 5 ("Moderately useful"), and 10 ("Extremely useful").
Time frame: Within 48 hours after AI recommendation delivery
Interpretation of AI-Generated Recommendations
Physician interpretation of the AI-generated recommendation, assessed using six predefined response categories in the physician research survey.
Time frame: Within 48 hours after AI recommendation delivery
Physician-Reported Recommendation Adoption Status
Physician-reported adoption of the AI-generated recommendation, categorized as implemented, planned but not yet implemented, or not adopted.
Time frame: Within 48 hours after AI recommendation delivery
Plan to share: No — Individual participant data are not planned for public sharing as part of this feasibility study.
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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