Analysis of clinical decision support system malfunctions: a case series and survey.

  • Wright A
  • Hickman T
  • McEvoy D
 et al. 
  • 18

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Abstract

OBJECTIVE To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions. MATERIALS AND METHODS We identified and investigated several CDSS malfunctions at Brigham and Women's Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions. RESULTS We identified four CDSS malfunctions at Brigham and Women's Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually. DISCUSSION CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate. CONCLUSION CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.

Author-supplied keywords

  • anomaly detection
  • clinical decision support
  • electronic health records
  • machine learning
  • safety

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Authors

  • Adam Wright

  • Thu-Trang T Hickman

  • Dustin McEvoy

  • Skye Aaron

  • Angela Ai

  • Jan Marie Andersen

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