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J Am Med Inform Assoc. 2017 Mar 1;24(2):281-287. doi: 10.1093/jamia/ocw171.

Screening for medication errors using an outlier detection system.

Author information

Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
Center for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
Clinical and Quality Analysis, Partners HealthCare, Boston, MA, USA.



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.


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.


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.


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.


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

[Indexed for MEDLINE]

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