Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction...

Area of Science:

• Clinical Informatics
• Health Data Science
• Predictive Analytics

Background:

• Dynamic predictive modeling using electronic health record (EHR) data is increasingly important.
• Model reliability hinges on high-quality EHR data, influenced by extraction and preparation processes.
• Over 40 challenges exist in EHR data extraction and preparation for predictive modeling.

Purpose of the Study:

• To identify and categorize challenges in EHR data extraction and preparation.
• To provide actionable recommendations for overcoming these challenges.
• To promote best practices for developing reliable dynamic prediction models.

Main Methods:

• Systematic identification of challenges during EHR data extraction and preparation.
• Categorization of challenges into cohort definition, outcome definition, feature engineering, and data cleaning.
• Development of practical recommendations for each challenge category.

Main Results:

• Over 40 distinct challenges were identified across the four categories.
• Actionable recommendations were formulated to address each identified challenge.
• A structured guide was created to improve data quality for predictive models.

Conclusions:

• Addressing data extraction and preparation challenges is crucial for trustworthy EHR-based predictive models.
• The identified challenges and recommendations serve as a practical guide for data engineers and researchers.
• Implementing these best practices will enhance the quality and clinical applicability of dynamic prediction models.