Screening for medication errors using an outlier detection system

Abstract

Objective: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening.

Materials and methods: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts.

Results: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues.

Discussion: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data.

Conclusion: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients' clinical contexts and details.

Keywords: clinical decision support; electronic health records; machine learning; medication alert systems; patient safety.