JMIR Publications

ABSTRACT

Background:

An essential step in any medical research project after having identified the research question is to find out if there are enough patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and access local data in a secure manner. Working across hospitals usually also means working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard has been developed by HL7 to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative (MII) in Germany has committed to this standard and created Data Integration Canters, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem, however a distributed feasibility query platform for the FHIR standard is still missing.

Objective:

In this study we describe the design and implementation of the components involved in creating a privacy preserving cross-hospital feasibility query platform for researchers based on FHIR resources. This effort is part of a larger COVID-19 data exchange platform (CODEX) and is designed to be scalable for a broad range of patient data.

Methods:

We analysed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, a backend with an ontology and terminology service, a middleware for query distribution and a feasibility FHIR execution service.

Results:

We implemented the components identified in the methods. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test dataset based on the German Corona Consensus Dataset (GECCO). A performance test using specifically created synthetic data revealed the applicability of our solution to datasets containing millions of FHIR resources. We found that our solution could be easily deployed across the hospitals and that it supported complex feasibility queries, combining multiple inclusion criteria and exclusion criteria using standard Health Level Seven (HL7) query languages such as the Clinical Quality Language (CQL) and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple HL7 query languages as well as middleware components necessary to allow integration with future directions of the Medical Informatics Initiative.

Conclusions:

We designed and implemented a feasibility platform for privacy preserving distributed feasibility queries, which works directly on FHIR formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.