SepsisNCT07025096
Evaluating Artificial Intelligence-Based Clinical Decision Support for Sepsis and ARDS
A study testing Artifical Intelligence-Generated Treatment Recommendations in people with Sepsis and Acute Respiratory Distress Syndrome (ARDS), run by University of Pennsylvania at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Sepsis, Acute Respiratory Distress Syndrome (ARDS)
- What is being tested?
- Artifical Intelligence-Generated Treatment Recommendations
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 355 participants
- Where
- 1 siteUnited States
- Study dates
- 5 Dec 2025to 3 Jul 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Has not completed a residency training program (i.e., medical intern or resident)
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Pennsylvania
Philadelphia, Pennsylvania, 19104
About this study
Official title: Evaluating Artificial Intelligence-Based Comprehensive 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.
- Conditions
- Sepsis, Acute Respiratory Distress Syndrome (ARDS)
- Being tested
- Artifical Intelligence-Generated Treatment Recommendations
- Keywords
- Sepsis, Acute Respiratory Distress Syndrome, Clinical Decision Support, Clinical Decision Support System, Invasive Mechanical Ventilation, Artificial Intelligence
- Medical subject headings
- Sepsis, Respiratory Distress Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artifical Intelligence-Generated Treatment RecommendationsOther
The clinical vignette will contain treatment recommendations which were generated by an artificial intelligence-based clinical decision support system.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Artifical Intelligence-Generated Treatment Recommendations |
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From enrollment to the end of the survey, an average of 45 minutes
Secondary outcomes (3)
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).
Measured: From enrollment to the end of the survey, an average of 45 minutes
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.
Measured: 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.
Measured: From enrollment to the end of the survey, an average of 45 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jun 2025
- Study start5 Dec 2025
- Primary completion3 Jul 2026
- Study completion3 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for SepsisStudies of the same condition, open and under way.
- More trials run by University of PennsylvaniaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 355 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Pennsylvania
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Responsible party
- Sponsor
- Investigators
- Gary Weissman, MD, MSHP, Principal investigator, University of Pennsylvania
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07025096
- Sponsor's protocol ID
- 858201
- Other IDs
- R35GM155262
- Status last verified
- Jul 2026
- First submitted
- 30 May 2025
- Last updated
- 23 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 41448698
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating Artificial Intelligence-Based Comprehensive Clinical Decision Support for Sepsis and ARDS
- ClinicalTrials.gov ID
- NCT07025096
- Acronym / sponsor ID
- 858201
- 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
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 355 participants (actual)
- Lead sponsor
- University of PennsylvaniaUniversity, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Study start
- 5 Dec 2025 (actual)
- Primary completion
- 3 Jul 2026 (actual)
- Study completion
- 3 Jul 2026 (actual)
- First posted
- 17 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- 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.
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence | Experimental | Other: Artifical Intelligence-Generated Treatment RecommendationsCritical 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. |
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (1 listed)
- Has not completed a residency training program (i.e., medical intern or resident)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artifical Intelligence-Generated Treatment RecommendationsThe clinical vignette will contain treatment recommendations which were generated by an artificial intelligence-based clinical decision support system. | Other | Artificial Intelligence |
Primary outcome
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.
3 secondary outcomes
Confidence of Predicting the Source of Treatment Recommendation
Time frame: From enrollment to the end of the survey, an average of 45 minutes
Appropriateness of Treatment Recommendations
Time frame: From enrollment to the end of the survey, an average of 45 minutes
Safety of Treatment Recommendations
Time frame: From enrollment to the end of the survey, an average of 45 minutes
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2026.
- Derived from this study: PubMed 41448698Angeli 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Gary Weissman, MD, MSHP principal investigator
University of Pennsylvania
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artifical Intelligence-Generated Treatment Recommendations in people with Sepsis and Acute Respiratory Distress Syndrome (ARDS), run by University of Pennsylvania at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 23 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Predicting the Source of Treatment Recommendation (time frame: From enrollment to the end of the survey, an average of 45 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07025096, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2026.