Data-driven approach to Early Warning Score-based alert management

• Published in: BMJ open quality Vol. 7; no. 3; p. e000088
• Main Authors: Capan, Muge, Hoover, Stephen, Miller, Kristen E, Pal, Carmen, Glasgow, Justin M, Jackson, Eric V, Arnold, Ryan C
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 2018; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: adverse events, epidemiology and detection; Algorithms; Blood pressure; Computerized physician order entry; Electronic health records; Emergency communications systems; Epidemiology; Inpatient care; Intensive care; Mortality; Nursing; Original; Original article; Patient safety; Physiology; Primary care; Sepsis; trigger tools; Vital signs; United States > US; patient safety; adverse events, epidemiology and detection; trigger tools
• ISSN: 2399-6641; 2399-6641
• DOI: 10.1136/bmjoq-2017-000088

BackgroundIncreasing 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.MethodsWe 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.ResultsWe 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.ConclusionEWS-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.