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Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making

Matthijs Berkhout1, Koen Smit2, Johan Versendaal2,3

  • 1Digital Ethics, HU University of Applied Sciences Utrecht, Heidelberglaan 15, Utrecht, 3584 CS, The Netherlands. Matthijs.berkhout@hu.nl.

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|April 18, 2024
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Summary

This study developed an algorithm to discover and visualize healthcare decisions from logged data. This aids in improving clinical decision-making and evaluating healthcare protocols.

Keywords:
CDSSDMNDecision managementDecision miningDecision-makingDiscovery

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Area of Science:

  • Healthcare Informatics
  • Decision Science
  • Data Mining

Background:

  • Healthcare decision-making is complex, especially in high-information environments like emergency, oncology, and psychiatry departments.
  • Existing challenges in healthcare decision-making necessitate novel approaches for data analysis and process improvement.

Purpose of the Study:

  • To discover and visualize clinical decisions from logged data within hospital settings.
  • To enhance the decision-making processes for healthcare professionals.
  • To support the periodic evaluation of established healthcare protocols and guidelines.

Main Methods:

  • Employed the Design Science Research Methodology (DSRM) to create a decision discovery and visualization artifact (algorithm).
  • Developed a fuzzy classifier algorithm tailored for extracting decisions from decision logs.
  • Validated the algorithm using an authentic synthetic dataset, covering design, development, demonstration, and evaluation phases.

Main Results:

  • Successfully designed and simulated an algorithm capable of discovering and visualizing decisions from logged data.
  • The fuzzy classifier algorithm effectively identified decisions within logs.
  • Visualizations were generated adhering to the Decision Model and Notation (DMN) standard.

Conclusions:

  • Demonstrated that decisions can be effectively extracted from decision logs.
  • The developed visualization method supports improved decision-making for healthcare professionals.
  • The approach provides a valuable tool for evaluating and refining healthcare protocols and guidelines.