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Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock

Ginestra, Jennifer C. MD1; Giannini, Heather M. MD1; Schweickert, William D. MD2,3; Meadows, Laurie RN, CCRN4; Lynch, Michael J. RN, CEN4; Pavan, Kimberly MSN, CRNP5; Chivers, Corey J. PhD3; Draugelis, Michael , BS3; Donnelly, Patrick J. RN, MS6; Fuchs, Barry D. MD, MS2,3; Umscheid, Craig A. MD, MS3,7,8

doi: 10.1097/CCM.0000000000003803
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Objective: To assess clinician perceptions of a machine learning–based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).

Design: Prospective observational study.

Setting: Tertiary teaching hospital in Philadelphia, PA.

Patients: Non-ICU admissions November–December 2016.

Interventions: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert’s helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert.

Measurements and Main Results: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient’s risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours.

Conclusions: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning–based sepsis alerts.

1Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.

2Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

3University of Pennsylvania Health System, Philadelphia, PA.

4Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.

5Department of Clinical Informatics, Penn Presbyterian Medical Center, Philadelphia, PA.

6Pennsylvania Hospital, Philadelphia, PA.

7Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

8Center for Evidence-based Practice, University of Pennsylvania Health System, Philadelphia, PA.

The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Dr. Ginestra, Dr. Schweickert, Ms. Meadows, Mr. Lynch, and Ms. Pavan helped with data collection; Dr. Ginestra helped with analysis and interpretation of the data, and drafting of the article; and all authors helped with conception and design, and critical revision of the article for important intellectual content.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http;/journals.lww.com/ccmjournal).

Dr. Umscheid’s contribution to this project was supported, in part, by the National Center for Research Resources (grant no: UL1RR024134), which is now at the National Center for Advancing Translational Sciences (grant no: UL1TR000003).

Presented as a poster at the 2017 Society of Hospital Medicine Annual Meeting, Las Vegas, NV.

Dr. Schweickert has received funding from the American College of Physicians and Arjo. Dr. Umscheid’s institution has received funding from the National Institutes of Health, Food and Drug Administration, and Agency for Healthcare Research and Quality Evidence-based Practice Center contracts, and he has received funding from the Patient-Centered Outcomes Research Institute Advisory Panel and Northwell Health (grand rounds honoraria). The remaining authors have disclosed that they do not have any potential conflicts of interest.

Address requests for reprints to: Craig A. Umscheid, MD, MS, Office of Clinical Excellence, University of Chicago Medicine, American School Building, 850 E. 58th Street, Suite 123, Office 128, MC 1135, Chicago, IL 60637. E-mail: craigumscheid@medicine.bsd.uchicago.edu

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