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Conformance analysis for comorbid patients in Answer Set Programming.

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This study introduces a new method for analyzing patient treatment logs after care, focusing on identifying deviations and unmanaged interactions from clinical guidelines for comorbid patients. It enhances patient safety by evaluating past treatment decisions retrospectively.

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support Systems
  • Health Data Analysis

Background:

  • Treating comorbid patients with multiple Computer-Interpretable Guidelines (CIGs) poses risks due to potential guideline interactions.
  • Existing research primarily addresses "a priori" or "execution-time" analyses of CIG interactions.
  • A gap exists in the "a posteriori" analysis of patient treatment logs.

Purpose of the Study:

  • To develop a method for the retrospective analysis of clinical actions performed on comorbid patients.
  • To explain executed clinical actions based on CIG recommendations, potential interactions, and interaction management strategies.
  • To identify deviations from CIGs and unmanaged interactions in patient treatment logs.

Main Methods:

  • Utilizing Answer Set Programming (ASP) for retrospective analysis.
  • Incorporating patient history and clinical action logs.
  • Focusing on the temporal dimension of clinical decision-making and guideline interactions.

Main Results:

  • The approach can explain clinical actions by linking them to CIGs and their interactions.
  • It identifies deviations from CIG recommendations not explained by interaction management.
  • It highlights instances of unmanaged interactions within the patient's treatment history.

Conclusions:

  • The proposed "a posteriori" analysis method offers a complementary approach to existing CIG interaction analysis.
  • This retrospective analysis can improve patient safety by identifying and rectifying issues in comorbid patient care.
  • The temporal focus enhances the method's applicability to real-world clinical scenarios.