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Decision analysis and reinforcement learning in surgical decision-making.

Tyler J Loftus1, Amanda C Filiberto1, Yanjun Li2

  • 1Department of Surgery, University of Florida Health, Gainesville, FL.

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Summary

Surgical decision-making errors can be reduced using decision analysis and reinforcement learning (RL). These methods offer improved patient care by addressing uncertainty and time constraints in clinical judgment.

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Area of Science:

  • Medical Decision Making
  • Artificial Intelligence in Surgery
  • Machine Learning Applications

Background:

  • Surgical patients experience preventable harm due to cognitive and judgment errors under time pressure and diagnostic uncertainty.
  • Decision analysis and reinforcement learning (RL) offer theoretical solutions but are underutilized in clinical practice.
  • This review aims to clarify the application of decision analysis and RL in surgical decision-making.

Purpose of the Study:

  • To review and synthesize the literature on decision analysis and reinforcement learning for surgical decision-making.
  • To promote understanding of these advanced analytical techniques among clinicians.
  • To highlight the potential of these methods in improving surgical outcomes.

Main Methods:

  • Systematic literature search of Cochrane, EMBASE, and PubMed databases up to June 2019.
  • Inclusion of 41 articles focusing on cognitive/diagnostic errors, decision-making, decision analysis, and machine learning.
  • Categorization of articles following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.

Main Results:

  • Traditional decision-support tools are limited by manual data entry and generalized patient models.
  • Decision analysis provides population-based recommendations but lacks individual patient precision.
  • Reinforcement learning (RL) offers personalized decision-making by analyzing patient-specific data for optimal action identification.

Conclusions:

  • Decision analysis and RL can complement each other to enhance surgical decision-making.
  • These methods have the potential to improve the quality and reduce errors in surgical care.
  • Successful clinical integration requires addressing data security, ethical considerations, and patient preferences.