The healthcare industry relies heavily on exchanging patient data efficiently and securely. One of the most common standards for exchanging healthcare data is HL7 (Health Level Seven). HL7 facilitates the transmission of health-related data across diverse systems and applications, ensuring interoperability. However, to achieve this seamless integration, interface developers need to handle HL7 messages carefully, especially when it comes to parsing and data mapping.
In this article, we’ll explore best practices for HL7 message parsing and data mapping, focusing on the essential techniques that interface developers should adopt. We will also dive into HL7 interface development, an important aspect that supports health IT infrastructure.
What is HL7?
Health Level Seven (HL7) is a set of international standards designed to enable the sharing of health information between healthcare systems. It defines the structure and encoding of data for various types of healthcare messages. These messages include information such as patient demographics, lab results, medical imaging, and billing details.
HL7 is divided into several versions, with HL7 v2.x being one of the most commonly used in the healthcare industry. This version is primarily used for transmitting electronic health records (EHRs), laboratory results, radiology information, and clinical data. HL7 v3 and FHIR (Fast Healthcare Interoperability Resources) are also evolving standards for healthcare data exchange, but v2 remains widely adopted.
To process HL7 messages, interface developers need to parse the data and map it to different systems, ensuring that the information flows seamlessly across healthcare applications.
HL7 Message Structure
Before delving into parsing and mapping techniques, it’s important to understand the structure of HL7 messages. A typical HL7 message consists of several segments, each containing fields that represent different pieces of data. These segments are delimited by specific characters:
- | (pipe) separates fields
- ^ (caret) separates components within fields
- & (ampersand) separates subcomponents
- ~ (tilde) separates repeating segments
For example, an HL7 message might look like this:
The MSH segment is the header, and subsequent segments, such as EVN and PID, carry different types of information like event type and patient data.
Challenges in HL7 Message Parsing and Data Mapping
HL7 message parsing and data mapping come with unique challenges:
- Complex Message Formats: HL7 messages are highly structured, and there can be nested segments and fields, which can make parsing difficult.
- Variability Across Versions: Different versions of HL7 (v2.x, v3, FHIR) have different formats and standards, so developers need to accommodate these variations.
- Inconsistent Data: Healthcare data can be inconsistent or incomplete, requiring robust error handling during parsing and mapping.
- Integration with Legacy Systems: Many healthcare organizations use legacy systems that may not support modern HL7 versions or have non-standard implementations.
With these challenges in mind, interface developers need to implement best practices that streamline parsing and mapping while ensuring the accuracy and integrity of the data.
Best Practices for HL7 Message Parsing
Effective HL7 message parsing is crucial for ensuring that data can be extracted, interpreted, and used in the appropriate system. Here are some best practices for parsing HL7 messages:
1. Use an HL7 Parsing Library
One of the first steps in parsing HL7 messages is selecting a reliable HL7 parsing library. Libraries are available in various programming languages, such as Java, Python, and C#. These libraries help developers avoid reinventing the wheel and ensure that HL7 messages are parsed according to the standard rules.
Some widely used HL7 libraries include:
- HAPI HL7v2 (Java)
- HL7apy (Python)
- NHapi (C#)
These libraries provide pre-built functionality to handle message segmentation, field delimiters, and encoding structures. By using a trusted library, developers can ensure consistency and reduce the risk of errors in the parsing process.
2. Validate the Message Structure
Before attempting to parse any HL7 message, it’s important to validate that the message is correctly structured. This includes ensuring that the delimiters are correct and that each segment follows the appropriate syntax. Validation should be performed to ensure compliance with the specific HL7 version being used.
For instance, an ADT^A01 message should begin with an MSH segment and should contain specific fields in a particular order. Validation can identify if the message is malformed or missing critical components, enabling developers to handle such errors gracefully.
3. Handle Variable Field Lengths
Some fields in HL7 messages can vary in length, such as patient names or addresses. Interface developers need to handle these variable-length fields by using a parsing strategy that dynamically adjusts based on the incoming data. HL7 parsing libraries can help with this, but it’s important to account for situations where data might exceed expected lengths or contain unexpected characters.
4. Implement Error Handling and Logging
HL7 messages can be unpredictable, especially when dealing with real-time data. Incomplete or incorrect messages can cause issues when trying to parse or process them. Developers must build robust error handling mechanisms to catch parsing errors, log them for future analysis, and ensure that the system doesn’t break or produce incorrect results.
For example, if a field in an HL7 message is missing or contains invalid data, the parser should gracefully handle this and allow the interface to continue processing other parts of the message.
Best Practices for Data Mapping in HL7 Integration
Once HL7 messages are parsed, the next challenge is mapping the data to the appropriate fields in the target system. Data mapping ensures that each segment of data in the HL7 message is correctly placed into the corresponding fields of the destination system.
Here are some best practices for effective data mapping:
1. Define a Clear Data Mapping Strategy
Developers should define a clear strategy for how each piece of data in an HL7 message will be mapped to the target system. This involves creating a mapping document or a mapping table that identifies where each field will go in the destination system. The mapping should account for different HL7 message types (e.g., ADT, ORM, ORU) and how data from those messages should be transformed to fit the target system’s schema.
2. Use a Middleware or Integration Engine
To manage the complexities of HL7 data mapping, many healthcare organizations use middleware or integration engines, such as Mirth Connect or Corepoint Integration Engine. These tools provide a centralized platform for handling HL7 messages and offer powerful mapping and transformation features. Middleware can also help convert between different HL7 versions (e.g., HL7 v2.x to HL7 v3) and facilitate communication between disparate systems.
3. Consider Business Rules and Transformations
HL7 message data often needs to be transformed to meet the business logic of the target system. This includes converting date formats, mapping codes (such as ICD-10 or SNOMED CT), and normalizing data to fit the destination schema. Developers should integrate these business rules into the data mapping process to ensure that the data is usable in the target system.
For instance, if an HL7 message contains a diagnosis code in ICD-9 format but the target system uses ICD-10, the developer will need to map and transform the code during the data mapping process.
4. Manage Repeating and Optional Segments
HL7 messages often contain repeating or optional segments. For example, a single HL7 message might contain multiple OBR (observation request) segments. Interface developers need to handle these repeating segments carefully, ensuring that each repetition is mapped correctly to the destination system. If certain segments are optional, developers should determine the appropriate behavior (e.g., skip the segment or use default values).
5. Perform Data Validation During Mapping
Data validation should also be performed during the data mapping phase to ensure that the data being mapped is consistent with the target system’s requirements. For example, certain fields may require specific formats (such as numeric values or valid codes), and validation should catch errors before the data is committed to the destination system.
HL7 Interface Development
HL7 interface development is a critical component of healthcare IT infrastructure. An interface developer is responsible for ensuring that data flows seamlessly between different healthcare systems, such as Electronic Health Record (EHR) systems, Laboratory Information Management Systems (LIMS), Radiology Information Systems (RIS), and more.
An HL7 interface developer's role includes:
- Designing, building, and testing HL7 interfaces
- Parsing and mapping HL7 messages to meet system requirements
- Managing HL7 message queues and data transformation
- Ensuring the security and integrity of the data being exchanged
- Handling error logging, monitoring, and troubleshooting
As healthcare systems become more complex and integrated, HL7 interface development has become a specialized skill. Developers need to understand the nuances of different HL7 message types and the systems they are integrating with.
Conclusion
HL7 message parsing and data mapping are fundamental aspects of healthcare system integration. Interface developers need to follow best practices to ensure that HL7 messages are accurately parsed, transformed, and mapped to the appropriate system. By leveraging HL7 parsing libraries, middleware, and integration engines, developers can streamline the parsing and mapping process, ensuring smooth communication across disparate healthcare systems. With careful attention to detail, robust error handling, and a well-defined data mapping strategy, developers can contribute to building a more interoperable and efficient healthcare ecosystem.
