Evaluating Artificial Intelligence-Based Clinical Decision Support for Sepsis and ARDS

Not Applicable

Not yet recruiting

• Conditions: Sepsis; Acute Respiratory Distress Syndrome (ARDS)

• Registration Number: NCT07025096
• Lead Sponsor: University of Pennsylvania

Brief Summary

Sepsis and acute respiratory distress syndrome (ARDS) are common in intensive care units. Managing sepsis and ARDS is inherently complex and requires making numerous decisions under uncertainty. Artificial intelligence (AI) clinical decision support systems (CDSSs) offer a promising approach to support care management for sepsis and ARDS.

The goal of this randomized, survey-based study is to compare treatment recommendations enacted by clinicians to those generated by an AI CDSS. The study will investigate whether an AI CDSS can generate treatment recommendations that are safe, appropriate, and indistinguishable to those provided by real clinicians.

In this study, participants (i.e., critical care clinicians) will review a series of critical care cases (vignettes) in an electronic survey. Each vignette will contain a de-identified case of a patient with sepsis and ARDS as well as treatment recommendations for the case. Participants will assess the safety and appropriateness of each treatment recommendations and answer whether they think the treatment recommendations came from the clinician or an AI CDSS.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2025-05-30
• Status Verified: 2025-06
• Study Start: 2025-06
• Study First Submitted QC: 2025-06-09
• Last Update Submitted: 2025-06-09
• Study First Posted: 2025-06-17
• Last Update Posted: 2025-06-17
• Primary Completion: 2025-08
• Study Completion: 2025-08

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 350

Inclusion Criteria

• Working as a physician (i.e., MD, DO) or an advanced practice provider (i.e., nurse practitioner, physician assistant)
• Working at a hospital or medical center in medical critical care, anesthesia critical care, surgical critical care, or emergency medicine

Exclusion Criteria

• Has not completed a residency training program (i.e., medical intern or resident)

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Accuracy of Predicting the Source of Treatment Recommendation	From enrollment to the end of the survey, an average of 45 minutes	Participants will answer if they think the treatment recommendations came from artificial intelligence (AI) or a clinician for each clinical vignette. Accuracy will be measured by participants correctly identifying the source of treatment recommendation.

Secondary Outcome Measures

Name	Time	Method
Confidence of Predicting the Source of Treatment Recommendation	From enrollment to the end of the survey, an average of 45 minutes	Participants will respond to their confidence in their prediction in whether the treatment recommendations of a vignette came from artificial intelligence or from a clinician. Confidence will measured on a Likert scale ranging from 0 (Not at all confident) to 7 (Extremely confident).
Appropriateness of Treatment Recommendations	From enrollment to the end of the survey, an average of 45 minutes	Appropriateness will be measured by participants' assessments of the clinical appropriateness of the treatment recommendations in the vignettes via Yes-No and free-text responses.
Safety of Treatment Recommendations	From enrollment to the end of the survey, an average of 45 minutes	Safety will be measured by participants' assessments of the overall safety of the treatment recommendations in the vignettes via Yes-No and free-text responses.

Trial Locations

Locations (1)

University of Pennsylvania; 🇺🇸 Philadelphia, Pennsylvania, United States