Thomas Minich

Summary: Objective: More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider’s response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts.Methods: We performed a retrospective study of medication alerts over a 24-month period (1/2013–12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions.Results: While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience.Conclusion: Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.Citation: Dexheimer JW, Kirkendall ES, Kouril M, Hagedorn PA, Minich T, Duan LL, Mahdi M, Szczesniak R, Spooner SA. The effects of medication alerts on prescriber response in a pediatric hospital. Appl Clin Inform 2017; 8: 491–501 https://doi.org/10.4338/ACI-2016-10-RA-0168

This study characterizes the frequency of and risk associated with weight entry errors at a single pediatric institution.

Objectives: Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.Materials and Methods: We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs.Results: Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts.Discussion: Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts.Conclusion: The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

Summary: Background: Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes. Objectives: To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users’ responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them. Methods: We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action. Results: We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ¼ of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4–10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation. Conclusions: Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses ( of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors. Citation: Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis of electronic medication orders with large overdoses: Opportunities for mitigating dosing errors. Appl Clin Inf 2014; 5: 25–45 http://dx.doi.org/10.4338/ACI-2013-08-RA-0057