Best Libraries for Interfacing with HL7 Medical Records and Data in 2023

When working with HL7 Health Level 7 medical records and data, several libraries can facilitate the process of parsing, generating, and managing HL7 messages. This article explores some of the best libraries available as of August 2023, providing an overview of their features, supported languages, and compatibility with HL7 standards.

Main Considerations When Choosing a Library

Choosing the right library is crucial for managing HL7 messages effectively. Here are the key considerations:

Language Compatibility: Ensure the library is compatible with your development environment. Different libraries support different programming languages, such as Java, C, Python, and JavaScript. HL7 Version Support: Verify that the library supports the specific HL7 version you are working with. Options range from HL7 v2.x to more recent versions. Community and Documentation: A well-documented library with an active community can simplify integration and troubleshooting.

Top Libraries for HL7 Data Management

HAPI HL7

Language: Java

Description: HAPI HL7 Application Programming Interface is a popular Java library that provides a comprehensive set of tools to encode, decode, and manipulate HL7 messages. It supports both v2.x and v3 HL7 standards. This library is widely used for parsing and generating HL7 messages, making it a reliable choice for developers working with HL7 data.

Website: HAPI HL7

NHapi

Language: C and .NET

Description: NHapi is a .NET port of HAPI HL7. It allows .NET developers to work with HL7 messages easily and provides similar functionality to the Java version. NHapi is useful for .NET developers who need to work with HL7 data in their projects.

Website: NHapi GitHub

HL7apy

Language: Python

Description: HL7apy is a Python library for parsing and creating HL7 messages. It supports HL7 v2.x and provides a straightforward interface for working with HL7 data. This library is ideal for Python developers who need to handle HL7 messages in their projects.

Website: HL7apy

Mirth Connect

Language: Java with a web-based interface

Description: Mirth Connect is an open-source healthcare integration engine that allows for the transformation and routing of HL7 messages. It can be used for various integration tasks and supports multiple messaging standards. While Mirth Connect is not specifically a library, it is a powerful tool for integration tasks in healthcare environments.

Website: Mirth Connect

PyHL7

Language: Python

Description: PyHL7 is a lightweight Python library for working with HL7 messages. It is suitable for simple parsing and generation tasks, making it ideal for lightweight applications. This library provides a straightforward approach to managing HL7 messages in Python projects.

Website: PyHL7 GitHub

Java HL7 Library

Language: Java

Description: The Java HL7 Library is a simpler alternative to HAPI, focusing on lightweight HL7 message parsing and generation. This library is suitable for applications that do not require the full capabilities of HAPI, making it a good choice for simpler HL7 message handling tasks.

Website: Java HL7 Library GitHub

SimpleHL7

Language: JavaScript

Description: SimpleHL7 is a lightweight library for parsing and creating HL7 messages in JavaScript. It is suitable for web applications and provides a simple API for working with HL7 data. This library is ideal for developers working with JavaScript and web-based projects.

Website: SimpleHL7 GitHub

Conclusion

Choosing the right library for interfacing with HL7 medical records and data is essential for effective data management and integration. By considering the specific needs of your project, including language compatibility, HL7 version support, and community resources, you can select the best library to meet your requirements.