OBJECTIVE: To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings. MATERIALS AND METHODS: We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification. Phase 2 identified the most frequently interruptive alerts at participating sites. Phase 3 applied the codebook to classify alerts. Inter-rater reliability (IRR) for the codebook and descriptive statistics for alert design contents were reported. RESULTS: We classified alert content on design elements such as the rationale for the alert's appearance, the hazard of ignoring it, directive versus informational content, administrative purpose, and whether it aligned with one of the Institute of Medicine's (IOM) domains of healthcare quality. Most design elements achieved an IRR above 0.7, with the exceptions for identifying directive content outside of an alert (IRR 0.58) and whether an alert was for administrative purposes only (IRR 0.36). IRR was poor for all IOM domains except equity. Institutions varied widely in the number of unique alerts and their designs. 78% of alerts stated their purpose, over half were directive, and 13% were informational. Only 2%-20% of alerts explained the consequences of inaction. DISCUSSION: This study raises important questions about the optimal balance of alert functions and desirable features of alert representation. CONCLUSION: Our study provides the first multi-center analysis of EHR alert design elements in pediatric care settings, revealing substantial variation in content and design. These findings underline the need for future research to experimentally explore EHR alert design best practices to improve efficiency and effectiveness.

INTRODUCTION: Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS: In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

BACKGROUND: 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. OBJECTIVE: (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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

Objective: Electronic health record (EHR) simulation with realistic test patients has improved recognition of safety concerns in test environments. We assessed if simulation affects EHR use patterns in real clinical settings. Materials and Methods: We created a 1-hour educational intervention of a simulated admission for pediatric interns. Data visualization and information retrieval tools were introduced to facilitate recognition of the patient's clinical status. Using EHR audit logs, we assessed the frequency with which these tools were accessed by residents prior to simulation exposure (intervention group, pre-simulation), after simulation exposure (intervention group, post-simulation), and among residents who never participated in simulation (control group). Results: From July 2015 to February 2017, 57 pediatric residents participated in a simulation and 82 did not. Residents were more likely to use the data visualization tool after simulation (73% in post-simulation weeks vs 47% of combined pre-simulation and control weeks, P <. 0001) as well as the information retrieval tool (85% vs 36%, P < .0001). After adjusting for residents' experiences measured in previously completed inpatient weeks of service, simulation remained a significant predictor of using the data visualization (OR 2.8, CI: 2.1-3.9) and information retrieval tools (OR 3.0, CI: 2.0-4.5). Tool use did not decrease in interrupted time-series analysis over a median of 19 (IQR: 8-32) weeks of post-simulation follow-up. Discussion: Simulation was associated with persistent changes to EHR use patterns among pediatric residents. Conclusion: EHR simulation is an effective educational method that can change participants' use patterns in real clinical settings.