CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(01): 054-064
DOI: 10.1055/s-0042-1760436
Original Article

“fhircrackr”: An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis

Julia Palm
1   Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
Frank A. Meineke
2   Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
Jens Przybilla
2   Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
3   Clinical Trial Centre Leipzig, University of Leipzig, Leipzig, Germany
Thomas Peschel
2   Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
› Author Affiliations
Funding This work was funded in part by the German Federal Ministry of Education and Research (BMBF) within the Medical Informatics Initiative in the SMITH Consortium and the POLAR project under the funding numbers 01ZZ1910[A-Z] (POLAR) and 01ZZ1803[A-Z] (SMITH).


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.

Protection of Human and Animal Subjects

Artificial EHR data were used for developing and testing this software. No formal intervention was performed and no additional (patient-) data were collected.

Publication History

Received: 31 August 2022

Accepted: 09 November 2022

Article published online:
25 January 2023

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