[An overview of validation methods based on the medical claims database]

Area of Science:

• Health Informatics
• Epidemiology
• Data Science

Background:

• Medical claims databases are vital for understanding disease characteristics and burden.
• Accurate patient identification using International Classification of Diseases (ICD) and text algorithms is essential for reliable data.
• Validation of these identification algorithms is critical for policy development and research.

Purpose of the Study:

• To introduce and evaluate traditional and emerging methods for validating patient identification algorithms in medical claims data.
• To propose a suitable validation strategy tailored for the current context in China.
• To facilitate the application of big data analytics in medical research and epidemiology within China.

Main Methods:

• Review of traditional validation techniques for medical claims data.
• Exploration of emerging validation methods, including machine learning (ML), natural language processing (NLP), and database linkage.
• Adaptation and proposal of a validation framework for the Chinese medical claims database landscape.

Main Results:

• A comprehensive overview of diverse validation methodologies is presented.
• The study highlights the strengths and limitations of various approaches in the context of medical claims data.
• A context-specific validation approach for China is outlined, emphasizing practical application.

Conclusions:

• Effective validation of patient identification algorithms is paramount for robust epidemiological studies using medical claims data.
• Emerging techniques like ML and NLP offer advanced capabilities for algorithm validation.
• The proposed China-specific validation method aims to enhance the utility of big data in national health research and policy.