Implementation and Evaluations of Sepsis Watch
NCT03655626 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 32003
Last updated 2019-08-01
Summary
The purpose of this study is to study the implementation and impact of an early warning system to detect and treat sepsis in the emergency room. We are observing the implementation of a Sepsis Machine Learning Model on all Adult patients. All data (observations field notes, interview recording \& transcripts, and survey responses) will be stored on HIPAA-compliant Duke servers behind the Duke firewall, and requiring password-protected user authentication to access. The risk to patients is minimal. The two risks to interviewed clinical staff we have identified involve loss of work time and anonymity.
Conditions
- Sepsis
- Severe Sepsis
- Septic Shock
Interventions
- OTHER
-
Sepsis Watch
The operational intervention comprises of a sepsis machine learning model, custom dashboard to present risk scores, and a rapid response team to monitor patients at-risk of sepsis and deliver sepsis treatment. Sepsis Watch was developed under operational management. The rapid response team will utilize information presented on the dashboard and follow a protocol that will enable them to support the primary teams of hospitalized patients.
Sponsors & Collaborators
-
Data & Society Research Institute
collaborator UNKNOWN -
Duke Clinical Research Institute
collaborator OTHER - lead OTHER
Principal Investigators
-
Cara O'Brien, MD · Duke Health
-
Mark Sendak, MD · Duke Institute for Health Innovation
Study Design
- Allocation
- NA
- Purpose
- TREATMENT
- Masking
- NONE
- Model
- SINGLE_GROUP
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2018-11-05
- Primary Completion
- 2019-07-05
- Completion
- 2019-07-05
Countries
- United States
Study Locations
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