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Eliciting and Exploiting Utility Coefficients in an Integrated Environment for Shared Decision-Making.

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This study introduces an integrated framework to aid clinicians in selecting patient preference elicitation methods and using these utility coefficients (UCs) in shared decision-making. The developed tool supports clinicians by recommending optimal methods and integrating UC elicitation with decision tree analysis.

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Area of Science:

  • Health decision science
  • Clinical informatics
  • Utility theory

Background:

  • Shared decision-making requires quantifying patient preferences using utility coefficients (UCs).
  • Selecting appropriate UC elicitation methods is challenging for clinicians unfamiliar with utility theory.
  • Existing tools lack integration between UC elicitation and decision modeling functionalities.

Purpose of the Study:

  • To provide clinical decision support for selecting optimal patient preference elicitation methods.
  • To integrate UC elicitation with decision tree analysis for shared decision-making.
  • To bridge the gap between eliciting patient utility coefficients and their application in clinical practice.

Main Methods:

  • Developed production rules based on utility theory to recommend elicitation methods tailored to patient profiles and health states.
  • Integrated a decision support system for elicitation method selection with a decision tree quantification and execution functionality.
  • Utilized TreeAge software for defining and running decision trees incorporating elicited utility coefficients.

Main Results:

  • An integrated framework for shared decision-making was developed, featuring a decision support tool for elicitation method selection.
  • The elicitation tool was tested on 51 volunteers, with collected utility coefficients validating the system's recommendation rules.
  • Usability testing of the tool yielded positive results, indicating user-friendliness and effectiveness.

Conclusions:

  • An integrated environment was created to enhance shared decision-making in clinical practice.
  • The developed framework facilitates the use of patient-specific utility coefficients in clinical decision models.
  • Future work includes validating the entire framework and exploring its application in cost-utility analyses for specific patient populations.