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Data-driven approach to Early Warning Score-based alert management
  1. Muge Capan1,
  2. Stephen Hoover2,
  3. Kristen E Miller3,
  4. Carmen Pal4,
  5. Justin M Glasgow2,
  6. Eric V Jackson2,
  7. Ryan C Arnold5
  1. 1Decision Sciences & MIS, LeBow College of Business, Drexel University, Philadelphia, Pennsylvania, USA
  2. 2Christiana Care Health System, Value Institute, Newark, Delaware, USA
  3. 3National Center for Human Factors in Healthcare, MedStar Health, Columbia, Maryland, USA
  4. 4Christiana Care Health System, Information Technology Clinical Application Services, Newark, Delaware, USA
  5. 5Department of Emergency Medicine, College of Medicine, Drexel University, Philadelphia, Pennsylvania, USA
  1. Correspondence to Dr Muge Capan; Muge.Capan{at}drexel.edu

Abstract

Background Increasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals.

Methods We quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure.

Results We compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics.

Conclusion EWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.

  • trigger tools
  • patient safety
  • adverse events, epidemiology and detection

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors MC, SH and KEM contributed to literature search, tables and figures, and study design. KEM and CP provided expertise in alert design and information technology supporting alerting systems in the electronic health records. JMG, EVJ and RCA provided clinical expertise. MC, SH, KEM, CP, JMG, EVJ and RCA contributed to the analysis, data interpretation and writing.

  • Funding This work was supported by the National Science Foundation Smart and Connected Health (Award Number: 1522072) and National Library of Medicine of the National Institutes of Health (Grant Number: 1R01LM012300-01A1, Award Number: R01LM012300).

  • Competing interests None declared.

  • Patient consent Not required.

  • Ethics approval The study was approved by the Christiana Care’s Institutional Review Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.