Gamification for Machine Learning in Surgical Patient Engagement

Area of Science:

• Surgical Decision Making
• Health Informatics
• Machine Learning in Healthcare

Background:

• Shared decision-making in surgery involves complex choices tailored to individual patient risk factors and values.
• Objective risk assessment can be challenging, and modifiable risk factors often impact care plans.
• Current machine learning (ML) tools for outcome prediction face usability and interpretability limitations.

Purpose of the Study:

• To propose a theoretical mobile application integrating ML and gamification for surgical patients.
• To enhance pre-operative risk optimization, reduce in-hospital complications, and accelerate recovery.
• To support evidence-based decision-making and align surgical outcomes with patient values.

Main Methods:

• Conceptualization of a mobile application design.
• Integration of machine learning for real-time risk prediction.
• Application of gamification principles to engage patients in risk management.

Main Results:

• The proposed application aims to provide patients and surgeons with tangible goals for optimizing surgical outcomes.
• Gamification may improve patient engagement in managing pre-operative risks.
• The system seeks to bridge the gap between ML capabilities and clinical usability.

Conclusions:

• A gamified mobile application integrating ML holds potential for improving surgical decision-making and patient outcomes.
• Personalized, evidence-based goal-setting can empower patients and surgeons.
• Further development is needed to address usability and interpretability challenges in clinical ML applications.