Despite the steadily growing popularity of the FHIR standard and its clear advantages in promoting data interoperability within healthcare, its adoption and moving from legacy data standards are still packed with intricate challenges. Many organizations find themselves compelled to manually construct and customize their FHIR mapping engines to meet specific requirements. This necessity to tackle each new case or data set from the ground up often leads to delays in data integration, increased budgets, and a rise in human errors.
Let’s explore why a unified, comprehensive solution for converting older standards like HL7 to modern FHIR remains elusive and whether AI-driven automation could streamline the data conversion and onboarding process, enhancing efficiency and cost-effectiveness.
Understanding the Core Challenges
Design and Architecture Differences
The fundamental distinctions in the design and architectural philosophies of both standards are at the heart of the conversion challenge.
HL7, being among the earliest interfacing standards, is primarily event-driven, designed for messaging communication, and employs a complex data model that typically requires extensive experience to navigate and translate effectively. HL7 messages generally consist of several segments and fields, encapsulating various aspects of patient healthcare information, often tailored to the specific needs of a particular healthcare system.
In contrast, FHIR adopts a contemporary, web-based framework, leveraging RESTful APIs and a resource-based data structure designed for scalability and flexibility. FHIR resources represent individual chunks of healthcare information, such as patient demographics, clinical observations, and medication orders, each with a specified set of data elements and inter-resource relationships. This divergence in design principles complicates the conversion task, necessitating a sophisticated understanding and innovative solutions to bridge these gaps effectively.
Message to Resource Translation
The task of translating data from HL7’s composite messages, with their myriad segments and fields, to FHIR’s discrete and logically arranged resources, presents a formidable challenge. The absence of direct, one-to-one correspondence between HL7 messages and FHIR resources necessitates the development of a complex mapping strategy tailored to each unique case. The conversion process often involves mapping multiple resources to a single message or a single message to multiple resources, or even blending HL7 and FHIR format data into a single, coherent output without compromising the original data’s depth and specificity.
Variety of Terminology
The task is further complicated by the need to translate coded elements across different terminologies, a process that involves more than just swapping codes. It requires ensuring that every piece of data retains its original meaning and purpose post-conversion. For instance, converting an element coded in ICD9 in one system to SNOMED in another is fraught with challenges due to differences between coding systems. Data analysts typically employ ConceptMap resources to navigate the appropriate conversions for coded elements across various terminologies.
Moreover, the variability in how different systems implement and utilize the HL7 and FHIR standards adds another layer of complexity. Thus, when converting data between these formats, it’s crucial to not only focus on accurate code translation but also to maintain the intended function and integrity of each data element. A clear understanding of each element’s role within its workflow is vital to devising effective mapping solutions.
HL7 custom segments
The HL7 standard, with its more than 120 distinct segments and the option for vendors to create custom Z-segments, underscores the flexibility of HL7 implementations. These custom segments are employed to fulfill unique data requirements not addressed by the standard HL7 framework, like local-specific information or other specialized clinical or patient information, thereby enhancing the standard’s adaptability. However, this flexibility also implies a degree of uncertainty regarding the presence of specific information in any given message, contributing to the variability observed across different vendors’ messages.
The frequent use of custom, vendor-specific segments complicates message interpretation and affects structure, order, and content, requiring a customized HL7 to FHIR conversion by a data analyst equipped with an Excel file and libraries of StructureDefinition resources.
AI-Powered Solutions: Datuum Answers the Call
When faced with the need to convert data from one format to anothers an organization typically have two options: to build a custom solution or to buy one. The AI-powered, no-code tools as Datuum offer a promising alternative, capable of reducing the technical and programming burdens required to map data and build a pipeline.
Datuum significantly simplifies the data mapping and transformation process from HL7 to FHIR. Its AI engine, pre-trained on vast amounts of healthcare data, understands data meanings and relationships like humans do, enabling users to transfer data to the desired schema while maintaining its original context and meaning. This facilitates a seamless and accurate data onboarding process that respects the nuances of both standards.
By automating the most intricate aspects of the data onboarding process, Datuum reduces the reliance on extensive manual efforts, making interoperability more achievable and cost-effective for healthcare providers.
