From Knowledge Graphs to Digital Twins: Perspectives on Modeling Patient Outcomes for Health Care Quality Assessment

Area of Science:

• Utilizes mathematical modeling, artificial intelligence (AI), and advanced computational techniques.
• Focuses on quantitative analysis and data-driven approaches in healthcare.

Background:

• Mathematical modeling, encompassing machine learning, knowledge graphs, and health digital twins, is crucial for predicting patient outcomes.
• Existing healthcare quality management lacks a standardized quantitative framework for patient-centered care assessment.

Purpose of the Study:

• To examine the contribution of mathematical modeling to healthcare quality management.
• To provide a quantitative framework for assessing patient-centered quality of care.
• To highlight the role of graph-based methods in improving healthcare quality.

Main Methods:

• Defines procedures, patient outcomes, and quality metrics with a quantitative focus.
• Categorizes patient-centered quality of care into patient safety, procedure accuracy, and procedure efficacy.
• Identifies modeling tasks essential for managing patient-centered quality and reviews relevant publications.

Main Results:

• Mathematical modeling offers a structured approach to defining and measuring quality in healthcare.
• Graph-based methods, such as knowledge graphs and health digital twins, show significant potential for enhancing quality management.
• Specific modeling tasks are identified for different levels of quality management.

Conclusions:

• Mathematical modeling provides a robust framework for advancing healthcare quality management.
• Knowledge graphs and health digital twins are promising tools for improving patient safety, accuracy, and efficacy.
• Further research and practical implementation are needed to fully realize the potential of these methods.