Common Challenges Faced During HL7 to FHIR Conversion and How to Overcome Them

Are you planning to migrate from Health Level Seven (HL7) to Fast Healthcare Interoperability Resources (FHIR) format? Exciting times ahead! FHIR is a game-changer in the world of healthcare information exchange, allowing for more efficient and secure data sharing.

However, the conversion process from HL7 to FHIR can be challenging, especially for those who are new to FHIR. In this article, we'll discuss some of the common challenges faced during HL7 to FHIR conversion and how to overcome them.

Challenge #1: Mapping Data Fields

One of the most significant challenges during HL7 to FHIR conversion is mapping data fields. HL7 messages and FHIR resources have different structures and terminologies, making it difficult to convert data from one format to the other seamlessly.

The first step in overcoming this challenge is to understand both HL7 and FHIR data models thoroughly. You need to have a good understanding of each field's meaning and how it relates to others in both systems. Once you have this understanding, you can start mapping each field from HL7 to FHIR.

There are several tools available that can help with the process of mapping data fields. For example, the HL7 to FHIR Mapper developed by the Firely team can auto-generate conversion maps based on the input HL7 message and the desired output FHIR resource.

Challenge #2: Converting Message Structure

Another common challenge is converting the message structure from HL7 to FHIR. HL7 messages have a hierarchical structure, whereas FHIR resources are flat and modular.

To overcome this challenge, you need to first identify the logical grouping of data elements in the HL7 message and map them to FHIR resources. You may need to split some HL7 segments into multiple FHIR resources or combine some FHIR resources into a single message.

Another way to simplify the conversion is to use pre-built FHIR profiles, which contain predefined FHIR resources that map to specific HL7 message types. Using a pre-built profile can significantly reduce the time and effort required to convert HL7 messages to FHIR resources.

Challenge #3: Handling Data Types

HL7 messages and FHIR resources use different data types, which can make it challenging to convert data elements from one format to another. For example, HL7 uses a coded field for gender (M, F, U, etc.), while FHIR uses an enumerated field (male, female, other, unknown).

To overcome this challenge, you need to identify the data types used in both HL7 and FHIR and map them correctly. Some data types may require additional conversion steps, such as transforming a string value to a date or vice versa.

Challenge #4: Addressing Data Quality Issues

HL7 messages often contain incomplete or inconsistent data. Converting this data to FHIR can result in data quality issues, such as duplicates, missing data, or inaccurate data.

To overcome this challenge, you need to develop a data quality strategy that identifies and addresses data quality issues before the conversion process. This may involve data cleansing, data enrichment, or data normalization techniques.

Once you have completed the conversion, you need to perform a data validation process to ensure that the converted data is accurate and complete. This process may involve comparing the converted data to the input HL7 message, performing data profiling, or validating the data against a standard reference.

Challenge #5: Handling Complex Data Structures

Some HL7 messages contain complex data structures, such as nested repeating fields or components. Converting these structures to FHIR can be challenging, as FHIR does not support nested or repeating fields.

To overcome this challenge, you may need to use a combination of FHIR resources to represent the complex data structure. For example, you could use the Observation resource to represent a nested repeating field or use the Extension resource to represent additional fields.

Another option is to use a FHIR implementation guide tailored to your specific HL7 message format. Implementation guides provide detailed instructions on how to convert complex HL7 messages to FHIR resources.

Conclusion

Converting from HL7 to FHIR is an exciting but challenging process. However, with the right tools, knowledge, and strategy, you can overcome these challenges and unlock the benefits of using FHIR for healthcare data interoperability.

Remember to thoroughly understand both HL7 and FHIR data models and use mapping tools to streamline the conversion process. Use pre-built FHIR profiles when possible, and develop a data quality strategy to ensure accurate and complete data. Finally, be prepared to handle complex data structures using a combination of FHIR resources and implementation guides.

At ToFHIR.com, we are committed to helping you succeed in your FHIR conversion journey. Contact us today for more information on how we can help you overcome the common challenges of HL7 to FHIR conversion.

Editor Recommended Sites