Key considerations when using health insurance claims data in advanced data analyses: an experience report

Area of Science:

• Health Services Research
• Medical Informatics
• Health Economics

Background:

• Health insurance claims data are widely used in healthcare analytics for various applications.
• Researchers often face challenges with multiple coding systems and data irregularities in claims data.
• Effective utilization of claims data is crucial for accurate disease burden estimation, policy evaluation, and predictive modeling.

Purpose of the Study:

• To highlight common challenges encountered when using US medical and pharmaceutical claims data for research.
• To provide practical approaches and suggestions for addressing these challenges in data analysis.
• To illustrate these experiences using the example of defining episodes of care for bundled payment systems.

Main Methods:

• Review of a seven-year repository of US medical and pharmaceutical claims data.
• Analysis of challenges related to coding systems and data irregularities.
• Case study on defining episodes of care for bundled payment reimbursement.

Main Results:

• Identification of significant challenges in handling claims records, including diverse coding systems and irregularities.
• Development of strategies to mitigate these challenges, reducing implementation time and avoiding common pitfalls.
• Successful application of these strategies in defining episodes of care for a US bundled payment system.

Conclusions:

• Researchers can improve the efficiency and accuracy of healthcare analytics by understanding and addressing the complexities of claims data.
• Proactive strategies for managing coding systems and data irregularities are essential for successful research outcomes.
• The findings offer valuable insights for researchers and policymakers involved in healthcare data analysis and reimbursement models.