Clinical trial NCT07025096: Evaluating Artificial Intelligence-Based Clinical Decision Support for Sepsis and ARDS
Official title: Evaluating Artificial Intelligence-Based Comprehensive Clinical Decision Support for Sepsis and ARDS
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Sepsis
Acute Respiratory Distress Syndrome (ARDS)
Keywords: Sepsis
Acute Respiratory Distress Syndrome
Clinical Decision Support
Clinical Decision Support System
Invasive Mechanical Ventilation
Artificial Intelligence
Lead sponsor: University of Pennsylvania
Sponsor class: OTHER
Collaborators: name: National Institute of General Medical Sciences (NIGMS)
First posted: 2025-06-17
Enrollment: 350
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Participants will review a series of clinical vignettes. Each vignette will be randomized to show a treatment recommendation either from an artificial intelligence-based clinical decision support system (AI CDSS) or from the clinician in the case, reflecting actual clinical practice. Vignettes will be randomized equally, and participants will see an equal number of vignettes from each arm.
Primary purpose: HEALTH_SERVICES_RESEARCH

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.


Eligibility criteria: 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)

Primary outcomes:
measure: Accuracy of Predicting the Source of Treatment Recommendation
timeFrame: From enrollment to the end of the survey, an average of 45 minutes
Secondary outcomes:
measure: Confidence of Predicting the Source of Treatment Recommendation
timeFrame: From enrollment to the end of the survey, an average of 45 minutes
measure: Appropriateness of Treatment Recommendations
timeFrame: From enrollment to the end of the survey, an average of 45 minutes
measure: Safety of Treatment Recommendations
timeFrame: From enrollment to the end of the survey, an average of 45 minutes
Other outcomes:
Locations:
city: Philadelphia
country: United States
facility: University of Pennsylvania
state: Pennsylvania
Study officials:
affiliation: University of Pennsylvania
name: Gary Weissman, MD, MSHP
role: PRINCIPAL_INVESTIGATOR
References: