Abstract
Background The growing interest in the secondary use of electronic health record (EHR) data
has increased the number of new data integration and data sharing infrastructures.
The present work has been developed in the context of the German Medical Informatics
Initiative, where 29 university hospitals agreed to the usage of the Health Level
Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established
data integration centers. This standard is optimized to describe and exchange medical
data but less suitable for standard statistical analysis which mostly requires tabular
data formats.
Objectives The objective of this work is to establish a tool that makes FHIR data accessible
for standard statistical analysis by providing means to retrieve and transform data
from a FHIR server. The tool should be implemented in a programming environment known
to most data analysts and offer functions with variable degrees of flexibility and
automation catering to users with different levels of FHIR expertise.
Methods We propose the fhircrackr framework, which allows downloading and flattening FHIR
resources for data analysis. The framework supports different download and authentication
protocols and gives the user full control over the data that is extracted from the
FHIR resources and transformed into tables. We implemented it using the programming
language R [1] and published it under the GPL-3 open source license.
Results The framework was successfully applied to both publicly available test data and real-world
data from several ongoing studies. While the processing of larger real-world data
sets puts a considerable burden on computation time and memory consumption, those
challenges can be attenuated with a number of suitable measures like parallelization
and temporary storage mechanisms.
Conclusion The fhircrackr R package provides an open source solution within an environment that
is familiar to most data scientists and helps overcome the practical challenges that
still hamper the usage of EHR data for research.
Keywords
Fast Healthcare Interoperability Resources - electronic health records - health information
interoperability - data analysis
