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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 10, 2023
Open Peer Review Period: Dec 10, 2023 - Feb 5, 2024
Date Accepted: Nov 30, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Enhancing Doctor-Patient Shared Decision-Making: Design of a Novel Collaborative Decision Description Language

Guo X, Xiao L, Chen J, Tong Z

Enhancing Doctor-Patient Shared Decision-Making: Design of a Novel Collaborative Decision Description Language

J Med Internet Res 2025;27:e55341

DOI: 10.2196/55341

PMID: 40053763

PMCID: 11920667

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

A Novel Collaborative Decision Description Language (CoDeL): for Modelling Personalized Doctor-Patient Shared Decision Making

  • XiaoRui Guo; 
  • Liang Xiao; 
  • Jianxia Chen; 
  • Zefang Tong

ABSTRACT

Background:

Effective shared decision making between patients and physicians is crucial for enhancing healthcare quality and reducing medical errors. But the shared decision making process itself is difficult to understand, and it is not clear where to begin to solve the problems that arise during this interaction.

Objective:

Therefore, we propose a Collaborative Decision Description Language (CoDel) to help better understand and characterize the interactive process by modeling the common decision scenario, so that each decision process has a reliable theoretical basis and traceability.

Methods:

The CoDel we define consists of four factors: A decision-maker interaction process protocol based on Lightweight Social Calculs (LSC); Using Speech Acts to define the semantic relations in the interaction process; Arguments in semantics guided by clinical guidelines; Incorporate constraints for personality modeling.

Results:

Finally, we validate and demonstrate the applicability of our approach in conjunction with the interaction between patients and physicians with atrial fibrillation. In addition, we combine our method with GPT to solve the unexplainability of GPT, and at the same time validate our method and enrich our argument.

Conclusions:

Through the defined decision language, the communication between doctors and patients can be improved.


 Citation

Please cite as:

Guo X, Xiao L, Chen J, Tong Z

Enhancing Doctor-Patient Shared Decision-Making: Design of a Novel Collaborative Decision Description Language

J Med Internet Res 2025;27:e55341

DOI: 10.2196/55341

PMID: 40053763

PMCID: 11920667

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