Using AI to Improve Sepsis Quality of Care in the Emergency Department
Public ClinicalTrials.gov record NCT07581340. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.
Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.
Official title
Impact of Automated Sepsis Metric Evaluation on Provider Performance
Brief summary
Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.
Sepsis is a life-threatening condition caused by the body's response to infection and is a leading cause of death worldwide. Hospitals use a complex quality measure called SEP-1 to track whether patients with severe sepsis or septic shock receive recommended care, such as timely antibiotics, fluids, and laboratory testing. However, evaluating SEP-1 is difficult. It requires manual review of medical records, is time-consuming and expensive, and typically provides feedback to clinicians months after care is delivered. This delay limits the ability to improve care in real time. This study tested whether artificial intelligence (AI), specifically a type of system called a large language model (LLM), could improve the quality of sepsis care by providing faster and more detailed feedback to physicians. The study was conducted at two emergency departments within a large academic health system. Sixty-six attending physicians were randomly assigned to one of two groups. In the intervention group, the AI system reviewed each patient's medical record at the time of hospital discharge and determined whether SEP-1 care standards were met. Physicians then received near real-time, individualized feedback about their performance, including specific areas for improvement. In the control group, physicians received standard feedback based on a small sample of cases reviewed months later using traditional methods.
Study identification
- NCT ID
- NCT07581340
- Recruitment status
- Completed
- Study type
- Interventional
- Phase
- Not applicable
- Enrollment
- 66 participants
Conditions and interventions
Conditions
Eligibility (public fields only)
- Age range
- 18 Years and older
- Sex
- All
- Healthy volunteers
- Healthy volunteers not accepted
This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.
Study timeline
- Start date
- Nov 30, 2024
- Primary completion
- Jul 31, 2025
- Completion
- Dec 11, 2025
- Last update posted
- May 11, 2026
2024 – 2025
United States locations
- U.S. sites
- 1
- U.S. states
- 1
- U.S. cities
- 1
| Facility | City | State | ZIP | Site status |
|---|---|---|---|---|
| UC San Diego Health | San Diego | California | 92103-1911 | — |
Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.
About this trial record page
- What this page shows
- Public field values for ClinicalTrials.gov record NCT07581340, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
- What this page does not do
- No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
- Where the data comes from
- Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
- Last refresh
- Last update posted May 11, 2026 · Synced Sep 4, 2026
Related: full search, browse by condition, browse by drug or therapy, browse by sponsor, browse by U.S. city.
Open the official record
The complete protocol, eligibility criteria, and contact information for NCT07581340 live on ClinicalTrials.gov.