Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit

Abstract

BackgroundTimely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows.MethodsOur prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children’s Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools).ResultsPhysicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P < .001).ConclusionsThe automated system demonstrated improved capacity for identifying MAEs while guarding against alert fatigue. It also showed promise for reducing patient exposure to potential harm following MAE events.

Identification of potential drug name confusion errors in the Sentinel System

“…Another group used a comparable method to ours in that they developed an automated detection algorithm using medication orders and diagnostic codes in claims data, but they did not analyze “known pairs” as we did . Others also developed an automated detection system that incorporated natural language processing for use in a hospital electronic health record system . Our work furthers the field by developing a practical, semiautomated approach to identify potential medication errors that can inform regulatory decision making when reports or signals of drug name confusion are reported to FDA.…”

“…18 Others also developed an automated FIGURE 1 Hypothetical claims profile review notes for a potential erroneous dispensing of (A) Brintellix and (B) Brilinta detection system that incorporated natural language processing for use in a hospital electronic health record system. 19 Our work furthers the field by developing a practical, semiautomated approach to identify potential medication errors that can inform regulatory decision making when reports or signals of drug name confusion are reported to FDA. Out of nearly 30 000 new users of the two drugs, the claims-based algorithm identified 78 potential errors for manual data review.…”

Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study

“…Our research is specifically directed at developing accurate and scalable informatics technologies to monitor the medication use process and detect medication administration errors. In our previous studies, we developed artificial intelligence–based algorithms for monitoring the use of high-risk medications including vasopressors, narcotics, insulin, total parenteral nutrition (TPN), and fluids [ 1 , 9 , 10 ]. By analyzing order and medication administration record (MAR) data residing in EHRs, the algorithms identified discrepancies and potential errors in how medications were being ordered and documented as administered.…”

“…Despite their viability in discrepancy detection, the algorithms relied on a single data source that resulted in a number of false positives and false negatives. For instance, the algorithms might miss an error in administration (a false negative) if an order adjustment was not placed in the EHR or was incorrectly documented in the MAR [ 10 ]. To improve the accuracy of error detection, we sought to integrate smart pump information into the computerized algorithms.…”

Leveraging real‐time data to drive quality improvement in a pediatric health system

“…Several studies have been published on the topic of EHR‐triggers utilized in reducing harm outcomes, such as nephrotoxic kidney injury sepsis . EHR has also been utilized to leverage an automated system for real‐time medication administration error . On the contrary, real‐time measures of nationally‐reported harm are lagging indicators.…”