Development and performance of electronic acute kidney injury triggers to identify pediatric patients at risk for nephrotoxic medication-associated harm.

BACKGROUND: Nephrotoxic medication-associated acute kidney injury (NTMx-AKI) is a costly clinical phenomenon and more common than previously recognized. Prior efforts to use technology to identify AKI have focused on detection after renal injury has occurred. OBJECTIVES: Describe an approach and provide a technical framework for the creation of risk-stratifying AKI triggers and the development of an application to manage the AKI trigger data. Report the performance characteristics of those triggers and the refinement process and on the challenges of implementation. METHODS: Initial manual trigger screening guided design of an automated electronic trigger report. A web-based application was designed to alleviate inefficiency and serve as a user interface and central workspace for the project. Performance of the NTMx exposure trigger reports from September 2011 to September 2013 were evaluated using sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). RESULTS: Automated reports were created to replace manual screening for NTMx-AKI. The initial performance of the NTMx exposure triggers for SN, SP, PPV, and NPV all were ≥0.78, and increased over the study, with all four measures reaching ≥0.95 consistently. A web-based application was implemented that simplifies data entry and couriering from the reports, expedites results viewing, and interfaces with an automated data visualization tool. Sociotechnical challenges were logged and reported. CONCLUSION: We have built a risk-stratifying system based on electronic triggers that detects patients at-risk for NTMx-AKI before injury occurs. The performance of the NTMx-exposed reports has neared 100% through iterative optimization. The complexity of the trigger logic and clinical workflows surrounding NTMx-AKI led to a challenging implementation, but one that has been successful from technical, clinical, and quality improvement standpoints. This report summarizes the construction of a trigger-based application, the performance of the triggers, and the challenges uncovered during the design, build, and implementation of the system.

Analysis of electronic medication orders with large overdoses: opportunities for mitigating dosing errors.

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 » 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 (≥500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.