Acute Respiratory Distress Syndrome (ARDS), Sepsis
Conditions
Keywords
Sepsis, Acute Respiratory Distress Syndrome, Clinical Decision Support, Clinical Decision Support System, Invasive Mechanical Ventilation, Artificial Intelligence
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.
Interventions
OTHERArtifical Intelligence-Generated Treatment Recommendations
The clinical vignette will contain treatment recommendations which were generated by an artificial intelligence-based clinical decision support system.
Sponsors
University of Pennsylvania
National Institute of General Medical Sciences (NIGMS)
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Subject)
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORGary Weissman, MD, MSHP
University of Pennsylvania
Outcome results
None listed