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| ID | Type | Description | Link |
|---|---|---|---|
| R35GM155262 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of General Medical Sciences (NIGMS) | NIH |
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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Artificial Intelligence | Experimental | 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. |
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| Human Clinician | No Intervention | 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of Predicting the Source of Treatment Recommendation | 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. | From enrollment to the end of the survey, an average of 45 minutes |
| Measure | Description | Time Frame |
|---|---|---|
| Confidence of Predicting the Source of Treatment Recommendation | 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). | From enrollment to the end of the survey, an average of 45 minutes |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Gary Weissman, MD, MSHP | University of Pennsylvania | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Pennsylvania | Philadelphia | Pennsylvania | 19104 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41448698 | Derived | Angeli Gazola A, Bishop NS, Schmid BE, Pirracchio R, Valley TS, Bhavani SV, Krutsinger DC, Giannini HM, Lu Y, Ungar LH, Meyer NJ, Kerlin MP, Weissman GE. Evaluating AI-based comprehensive clinical decision support for sepsis and ARDS: protocol for a Clinician Turing Test. BMJ Open. 2025 Dec 24;15(12):e106757. doi: 10.1136/bmjopen-2025-106757. |
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| ID | Term |
|---|---|
| D018805 | Sepsis |
| D012128 | Respiratory Distress Syndrome |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
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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.
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| Appropriateness of Treatment Recommendations | 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. | From enrollment to the end of the survey, an average of 45 minutes |
| Safety of Treatment Recommendations | 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. | From enrollment to the end of the survey, an average of 45 minutes |
| D013568 |
| Pathological Conditions, Signs and Symptoms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D012120 | Respiration Disorders |