Clinical decisions support malfunctions in a commercial electronic health record

Steven Z. Kassakian, Thomas R. Yackel, Paul N. Gorman, David A. Dorr

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Objectives: Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection. Methods: We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated. Results: Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44. Discussion: CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively. Conclusion: CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Original languageEnglish (US)
Pages (from-to)910-923
Number of pages14
JournalApplied Clinical Informatics
Volume8
Issue number3
DOIs
StatePublished - Sep 6 2017

Keywords

  • Alerts
  • Clinical decision support
  • Electronic health record
  • Electronic medical record
  • Errors
  • Malfunction

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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