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BAIT: A New Medical Decision Support Technology Based on Discrete Choice Theory.

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|March 30, 2021
PubMed
Summary

We developed a new method using choice experiments to capture medical expertise for decision support. This explainable system aids experts by revealing implicit factor weights and providing probabilistic assessments.

Keywords:
decision aidsdecision modelsdecision support systemsdecision support techniquesend-of-life decisionnecrotizing enterocolitis

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Area of Science:

  • Medical Decision Support Systems
  • Econometrics in Healthcare
  • Clinical Expertise Codification

Background:

  • Medical decision-making relies heavily on expert knowledge, which is challenging to codify.
  • Existing decision support systems often lack explainability or adaptability.
  • Econometric techniques offer a novel framework for analyzing complex choices.

Purpose of the Study:

  • To present a novel approach for codifying medical expertise using econometric techniques.
  • To develop an explainable and tractable decision support system for medical experts.
  • To illustrate the application of this approach in critical neonatal care decisions.

Main Methods:

  • Utilized conjoint analysis (discrete choice theory) adapted from consumer behavior research.
  • Collected expert decisions through systematically designed choice experiments with hypothetical scenarios.
  • Estimated implicit weights assigned by experts to various decision factors.

Main Results:

  • Developed a choice model capable of generating probabilistic assessments for real-life medical decisions.
  • The model provides explanations for its assessments by identifying key decision factors.
  • Demonstrated the approach's utility in the complex decision of surgery versus comfort care for critically ill neonates.

Conclusions:

  • This econometric approach offers a viable method for creating explainable medical decision support systems.
  • The system can codify and leverage expert judgment effectively.
  • Further research should explore advantages and limitations compared to rule-based and machine learning methods.