Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

<p><strong>BACKGROUND: </strong>Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.</p>

<p><strong>OBJECTIVE: </strong>(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.</p>

<p><strong>MATERIALS AND METHODS: </strong>We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.</p>

<p><strong>RESULTS: </strong>Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.</p>

<p><strong>CONCLUSION: </strong>Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.</p>

<p><strong>OBJECTIVES: </strong>To evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.</p>

<p><strong>DESIGN: </strong>Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.</p>

<p><strong>SETTING: </strong>Two quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center.</p>

<p><strong>PATIENTS: </strong>The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.</p>

<p><strong>INTERVENTIONS: </strong>We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.</p>

<p><strong>CONCLUSIONS: </strong>Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.</p>