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Statistical process control methods for expert system performance monitoring

M G Kahn1, T C Bailey, S A Steib

  • 1Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.

Journal of the American Medical Informatics Association : JAMIA
|July 1, 1996
PubMed
Summary
This summary is machine-generated.

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Monitoring deployed medical expert systems requires efficient methods. Statistical process control offers effective tools for continuous performance evaluation, ensuring system reliability over time.

Area of Science:

  • Medical Informatics
  • Health Systems Engineering

Background:

  • Existing performance evaluation methods for medical expert systems are often unsuitable for deployed systems.
  • Long-term maintenance requires efficient monitoring techniques due to high resource demands.

Purpose of the Study:

  • To introduce Statistical Process Control (SPC) as a viable methodology for evaluating deployed medical expert systems.
  • To demonstrate the application of SPC tools for continuous performance monitoring.

Main Methods:

  • Adaptation of SPC tools and theory from industrial process monitoring to medical expert systems.
  • Utilizing p-charts for monitoring error rates and case volume.
  • Demonstrating inspection strategies using the GermWatcher expert system case data.

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Main Results:

  • SPC provides visual methods and heuristic guidelines for detecting performance changes.
  • Statistical theory enables estimation of detection capabilities for different evaluation strategies.
  • P-charts effectively monitor deployed systems like GermWatcher.

Conclusions:

  • Statistical Process Control is a transferable and effective methodology for the continuous performance evaluation of medical expert systems.
  • SPC facilitates efficient monitoring during the maintenance phase, ensuring system reliability.