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Bayesian learning-based agent negotiation model to support doctor-patient shared decision making.

Xin Chen1, Yong Liu1, Fei-Ping Hong2

  • 1Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China.

BMC Medical Informatics and Decision Making
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian learning-based bilateral fuzzy constraint agent negotiation model (BLFCAN) for healthcare. BLFCAN enhances patient-doctor agreement satisfaction and social welfare while reducing negotiation time and costs.

Keywords:
AgentBayesian learningDoctor-patient negotiationFuzzy constraintShared decision making

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Area of Science:

  • Artificial Intelligence
  • Health Informatics
  • Computational Social Science

Background:

  • Agent negotiation is prevalent in e-commerce and business but underutilized in healthcare due to decision-making complexities.
  • Medical decision-making involves fuzzy preferences, ethical considerations, and high stakes, posing unique negotiation challenges.
  • Existing negotiation models often fail to address the nuanced nature of patient-provider interactions.

Purpose of the Study:

  • To propose a novel negotiation model for healthcare settings that accommodates fuzzy preferences and improves mutual agreement.
  • To enhance the efficiency and satisfaction of treatment negotiations between doctors and patients.
  • To leverage Bayesian learning for predicting opponent preferences and optimizing negotiation outcomes.

Main Methods:

  • Developed a Bayesian learning-based bilateral fuzzy constraint agent negotiation model (BLFCAN).
  • Represented imprecise doctor and patient preferences using fuzzy constrained agents.
  • Employed Bayesian learning to predict opponent preferences, aiming to boost negotiation efficiency and social welfare.

Main Results:

  • Achieved individual satisfaction rates of 55.4%-64.2% for doctors and 69%-74.5% for patients.
  • Increased overall satisfaction by 26.5%-29% within fewer negotiation rounds.
  • Demonstrated flexible adaptation of negotiation strategies across diverse scenarios.

Conclusions:

  • BLFCAN effectively reduces communication time, costs, and potential conflicts in medical negotiations.
  • The model mitigates the impact of emotions and biases on decision-making.
  • BLFCAN improves agreement satisfaction for both parties and enhances overall social welfare in healthcare negotiations.