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OpenTrials
Completed

NCT Number: NCT07025096

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

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.

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Key information

Conditions

Sepsis Acute Respiratory Distress Syndrome (ARDS) Infections Inflammation Lung Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Respiration Disorders Respiratory Distress Syndrome Respiratory Tract Diseases Systemic Inflammatory Response Syndrome

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of Pennsylvania

Philadelphia, Pennsylvania, 19104, United States

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

Artifical Intelligence-Generated Treatment Recommendations

Other

The clinical vignette will contain treatment recommendations which were generated by an artificial intelligence-based clinical decision support system.

Primary outcomes

  1. Accuracy of Predicting the Source of Treatment Recommendation

    Time frame: 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 outcomes

  1. Confidence of Predicting the Source of Treatment Recommendation

    Time frame: 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).

  2. Appropriateness of Treatment Recommendations

    Time frame: 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.

  3. Safety of Treatment Recommendations

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

University of Pennsylvania

Other

Collaborators

  • National Institute of General Medical Sciences (NIGMS)

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jun 17, 2025
Registry last updated
Jul 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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