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Detecting and Resolving Data Conflicts when Using International Claims Data for Research.

Christian Haux¹, Olivier Kalmus², Anna-Lena Trescher²

¹University of Heidelberg, Institute of Medical Biometry and Informatics, Heidelberg, Germany.

Studies in Health Technology and Informatics

|April 22, 2018

Summary

This summary is machine-generated.

International research using health insurance claims data faces challenges due to varying anonymization and reimbursement systems. This study presents a method to identify and resolve these data conflicts, successfully applied in a multi-country European project.

Keywords:

Administrative data claims data data conflicts secondary use

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Area of Science:

Health Informatics
Data Science
International Health Research

Background:

Claims data analysis is common but typically limited to single countries.
Multi-national claims data research is scarce due to anonymization and reimbursement system differences.
These data discrepancies hinder the integration and analysis of international health insurance claims.

Purpose of the Study:

To analyze data conflicts arising from international claims data sets.
To develop a generic process for detecting and resolving these cross-border data conflicts.
To demonstrate the applicability of the developed process in a real-world multi-national project.

Main Methods:

Analysis of data conflicts in international claims data.
Development of a generic process to detect and resolve data discrepancies.
Application and validation of the process within the EU-funded ADVOCATE project.

Main Results:

Identified specific data conflicts inherent in multi-national claims data.
Developed and tested a robust process for conflict detection and resolution.
Successfully utilized the process to integrate claims data from six European countries in the ADVOCATE project.

Conclusions:

A standardized process can effectively address data conflicts in international claims data research.
Overcoming these data challenges facilitates robust multi-national health research.
The developed method enhances the utility of claims data for global health insights.