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Appl Clin Inform 2025; 16(04): 903-910
DOI: 10.1055/a-2648-4817
Special Issue on CDS Failures

Implementation of Passive Deterioration Index Alerts in an Intermediate Care Unit: A Failed Early Warning System Strategy

Authors

  • Thomas F. Byrd IV

    1   Department of Medicine, Division of Hospital Medicine, University of Minnesota, Minneapolis, Minnesota, United States
    2   Center for Learning Health System Sciences, University of Minnesota, Minneapolis, Minnesota, United States
    3   Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States

  • Molly Mattson

    4   Fairview Health Services, Minneapolis, Minnesota, United States

  • Mary Polt

    4   Fairview Health Services, Minneapolis, Minnesota, United States

  • Katie Pint

    4   Fairview Health Services, Minneapolis, Minnesota, United States

  • Kiril Dimitrov

    1   Department of Medicine, Division of Hospital Medicine, University of Minnesota, Minneapolis, Minnesota, United States

  • Angelica Willis

    1   Department of Medicine, Division of Hospital Medicine, University of Minnesota, Minneapolis, Minnesota, United States

  • Julia Lister

    1   Department of Medicine, Division of Hospital Medicine, University of Minnesota, Minneapolis, Minnesota, United States

  • Evan Beacom

    1   Department of Medicine, Division of Hospital Medicine, University of Minnesota, Minneapolis, Minnesota, United States

  • Chris Tignanelli

    2   Center for Learning Health System Sciences, University of Minnesota, Minneapolis, Minnesota, United States
    3   Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States
    5   Department of Surgery, Division of Acute Care Surgery, University of Minnesota, Minneapolis, Minnesota, United States

Funding This study was supported by grant P30HS029744 from the Agency for Healthcare Research and Quality (AHRQ). The University of Minnesota Office of Academic Clinical Affairs, Clinical Translational Science Institute, and Center for Learning Health System Sciences offered additional support through the Minnesota Learning Health System (MN-LHS) Mentored Career Development Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ or MN-LHS.
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Abstract

Objectives

Traditional early warning systems (EWS) have shown uncertain efficacy in real-world settings. More recently, machine learning models like the Epic Deterioration Index (DTI) have been developed, promising greater accuracy. Recognizing the potential of DTI, but also the pervasive issue of alert fatigue with interruptive (i.e., pop-up) EWS alerts, our institution implemented a DTI-enabled EWS with passive alerts (colored icons visible in prespecified locations within the electronic health record). We hypothesized that our intervention would reduce the time to treatment for deteriorating patients.

Methods

We piloted a DTI-enabled EWS in a 30-bed intermediate care unit at a large academic medical center. DTI scores, alert icons, and vital signs appeared on a custom Patient List interface. In the event of an alert, charge nurses were expected to conduct a bedside assessment and escalate care as necessary. We compared the 111-day pre- and postimplementation periods, with alert-to-action time as the primary outcome. Secondary outcomes included mortality, length of stay, ICU transfer, documentation rate, and provider acceptance.

Results

Among 301 patients with an elevated-risk score (156 pre- and 145 postimplementation), we found no significant differences in alert-to-action time (469 vs. 359 minutes before alert; p = 0.96), with provider actions typically occurring several hours before the alert in both periods. There were no significant differences in mortality (10.3% vs. 13.1%; p = 0.56), length of stay (15.7 vs. 12.8 days; p = 0.23), or ICU transfer (8.3% vs. 6.2%; p = 0.63). Charge nurses documented acknowledgment of the alert in 18.6% of cases, and acceptance was poor. Most nurses expressed a preference for interruptive alerts and more prominent DTI display locations.

Conclusion

In this single-unit pilot, passive DTI-enabled EWS alerts did not improve time to intervention or clinical outcomes. High-risk DTI scores often occurred after clinical deterioration had already been recognized.

Keywords

severity of illness index - machine learning - decision support systems - length of stay - mortality

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the University of Minnesota Institutional Review Board.


Supplementary Material



Publication History

Received: 10 February 2025

Accepted: 03 July 2025

Article published online:
27 August 2025

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