FHIR & Healthcare Graph Databases
An HL7 FHIR Graph Database is a graph database built for the advanced modeling and analysis of FHIR data. Graph databases contain nodes (aka ‘vertices‘) and edges (aka ‘relationships‘). Both nodes and edges can contain information. These databases exist for better analyzing relationships that cannot be analyzed with common relational databases easily. Most commonly these are often associated with social media networks like Linkedin and Facebook.
The below example shows a simple set of nodes and edges, for a patient who has a condition that has been treated with 3 procedures during a single encounter.
Visualization of an HL7 FHIR graph database query
Why HL7 FHIR for a Graph DB?
Graph databases are especially useful for HL7 FHIR data as this data format is built on a node-edge architecture. Stated another way, HL7 FHIR data is made up of buckets of objects that point at each other. Note the arrows also shown in the above diagram. For example:
An HL7 FHIR “Encounter” resource is almost always tied to a patient.
An HL7 FHIR “Procedure“ is tied to a condition, an encounter and a patient resource.
This is shown better below:
HL7 CDA and HL7 ADT data did not work on this basis as explicitly. Both of these formats would repeat practitioner data whenever it is relevant, rather than referencing a practitioner object each time. While this meets the same goal, it means that HL7 FHIR data has less data duplication (more compression) and is styled closer to a graph database than a relational/flat database.
Graph databases also permit:
faster queries for relational information, especially if it “multiple hops“ between nodes
better visualization of data if trying to perform reasonably sized queries
Cons of HL7 FHIR Graph Databases
That being said Graph databases are not meant to be used for all purposes. Traditional relational database SQL (and its variants) exist as a foundational database architype because it is so common efficient, and simple for any developer to learn the basics of in a matter of minutes if they do not already.
Additionally, healthcare graph databases:
can be slower at some tasks.
require more overhead / compute
require more storage
don’t scale particularly well
don’t permit large-scale visualization easily
are mostly paid services. Why they offer development tiers, using these at a prod level typically cost money. Vendors include:
Neo4j (freemium)
TigerGraph (freemium)
Amazon Neptune (freemium)
HL7 FHIR Graph Database Use Cases
If you are putting your FHIR data in a graph database, even temporarily, you are trying to analyze relationships. This is most commonly done for:
Seeing how common associations are between specified sets of conditions and treatments
Disease traceability, as it requires multi-hop analysis
Data traceability, as a larger part of interoperability
HL7 FHIR message quality analysis
Fraud, waste, and abuse detection
Tracing patient referral networks
Data gleamed from HL7 FHIR databases can also be fed into other systems. For example, hop count (the shortest number of edges between two nodes) can be a valuable input into an AI model with a larger goal.
Conclusion
Integrating HL7 FHIR data into graph databases offers significant advantages for healthcare organizations seeking to improve data analysis and interoperability. The inherent structure of FHIR, with its interconnected resources, aligns perfectly with the node-edge architecture of graph databases, enabling more efficient and insightful queries, especially when exploring complex relationships.
This capability is particularly valuable for tasks such as disease traceability, fraud detection, and patient referral networks. However, while graph databases provide powerful analytical benefits, they come with trade-offs in terms of overhead, storage, and scalability, making them more suitable for specialized use cases rather than general-purpose tasks. Despite these challenges, the ability to analyze relationships in a way that traditional relational databases cannot match makes graph databases an essential tool for advancing healthcare data analysis and improving patient outcomes. As the technology evolves, its integration with AI and other systems will further enhance its utility in the healthcare ecosystem.
Contact our team for more information.