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Using statistical anomaly detection models to find clinical decision support malfunctions.

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Clinical Decision Support (CDS) malfunctions are common. Anomaly detection models successfully identified known issues in CDS alerts, highlighting their utility for improving patient safety and care.

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Area of Science:

  • Health Informatics
  • Medical Device Software
  • Clinical Decision Support Systems

Background:

  • Clinical Decision Support (CDS) systems can malfunction, leading to adverse patient outcomes.
  • Identifying and rectifying these malfunctions is crucial for maintaining patient safety.

Purpose of the Study:

  • To identify malfunctions within Clinical Decision Support (CDS) systems.
  • To evaluate the efficacy of various anomaly detection models in identifying known CDS malfunctions.

Main Methods:

  • Evaluated six anomaly detection models: Poisson Changepoint, ARIMA, HDC, Bayesian Changepoint, SHESD, and EDM.
  • Analyzed four real-world CDS alerts with known malfunctions related to lead testing, aspirin therapy, pneumococcal vaccination, and thyroid testing.

Main Results:

  • Several models (Poisson changepoint, ARIMA, HDC, Bayesian changepoint, SHESD) detected anomalies in alerts for lead screening and pneumococcal vaccination.
  • The EDM model successfully identified anomalies in an alert for thyroid function monitoring in patients taking amiodarone.

Conclusions:

  • Malfunctions in CDS alert systems occur frequently and require prompt detection.
  • Anomaly detection models serve as valuable tools for identifying and addressing these critical system anomalies.