Challenges and Lessons Learned in Mapping HL7 v2 Data to openEHR: Insights from UKSH Medical Data Integration Center

Area of Science:

• Health Informatics
• Clinical Data Standards
• Interoperability Research

Background:

• Health Level Seven version 2 (HL7 v2) is a widely used standard for healthcare data exchange.
• openEHR is an emerging standard for archetyped clinical data, promoting semantic interoperability.
• Integrating data from legacy systems like HL7 v2 into modern standards like openEHR presents significant challenges.

Purpose of the Study:

• To explore the challenges encountered during the mapping of HL7 v2 messages to openEHR.
• To identify critical data inconsistencies hindering automated transformation.
• To provide lessons learned for improving healthcare data integration.

Main Methods:

• Analysis of HL7 v2 message structures within the Medical Data Integration Center (MeDIC) at University Hospital, Schleswig-Holstein (UKSH).
• Comparative study of HL7 v2 data against openEHR archetypes to identify mapping discrepancies.
• Qualitative assessment of data anomalies encountered during the transformation process.

Main Results:

• Identified critical inconsistencies including missing timestamps, units of measurement, decimal separator variations, and unexpected data types.
• Highlighted the limitations of off-the-shelf tools in automating HL7 v2 to openEHR data transformation.
• Demonstrated the complexity of achieving seamless data interoperability between HL7 v2 and openEHR.

Conclusions:

• Automated transformation of HL7 v2 to openEHR is challenging due to inherent data quality issues.
• Implementing robust data quality measures and governance is essential for successful data integration.
• Addressing these anomalies is crucial for enhancing data interoperability, supporting research, and optimizing clinical decisions.