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

University of Pennsylvania

Status: Invitation-only

Conditions: Acute Respiratory Distress Syndrome (ARDS); Sepsis

Treatments: Other: Artifical Intelligence-Generated Treatment Recommendations

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT07025096

858201

R35GM155262 (U.S. NIH Grant/Contract)

Details and patient eligibility

About

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.

Enrollment: 350 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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)

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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350 participants in 2 patient groups

Artificial Intelligence

Experimental group

Description:

Critical care cases / vignettes in this arm will contain treatment recommendations generated by an artificial intelligence-based clinical decision support system. Each participant will review four vignettes from this arm.

Treatment:

Other: Artifical Intelligence-Generated Treatment Recommendations

Human Clinician

No Intervention group

Description:

Critical care cases / vignettes in this arm will contain treatment recommendations that were enacted by the clinician in the actual case. Each participant will review four vignettes from this arm.