[MA 2023 03] Firely Simplifier Data Analysis
Firely, Amsterdam
Proposed by: Marten Smits [marten@fire.ly]
Introduction
The Fast Healthcare Interoperability Resources (FHIR) standard is an international, open standard for defining health data models and knowledge artifacts. It is being adopted by hospitals and national interoperability regulation worldwide, as well as large health tech companies like Apple, Google and Microsoft.
Join the Health Informatics Revolution! Firely is a healthcare software development and consulting company based in Amsterdam, the Netherlands. We are leaders in the FHIR standard, co-creating it and developing FHIR tooling and open-source libraries. Our customers include care providers, vendors, and health agencies in Europe and the US.
As an intern at Firely, you'll work with a passionate international team, building FHIR software and providing consultancy. If you're curious, passionate, and love collaboration, join us and be a part of the FHIR revolution!
Description of the SRP Project/Problem
This project focuses on utilizing Simplifier.net, a cloud platform for FHIR data models, to analyze FHIR resources and evaluate data quality. The goal is to answer critical questions about the popularity of FHIR resources over time, the interdependencies between data models, the effectiveness of national data modelling approaches, and the efficacy of FHIR data model documentation.
As the backbone for the official HL7 FHIR Registry, in the Simplifier project we have access to all publicly published FHIR models in the world. Based on this wealth of information several interesting questions can be answered.
Research questions
• Which FHIR resources exhibit consistent popularity, and which elements are frequently constrained?
• How can we visualize the network of dependencies between different FHIR data models and resources?
• What are the categorizations for various national data modelling approaches, and which models demonstrate superior performance?
• What are the best practices for FHIR data model documentation, including essential pages and sections?
Expected results
This research aims to uncover trends in FHIR resource popularity, create visualizations for data model dependencies, establish effective national data modelling categories, and outline optimal documentation strategies. The ultimate goal is to develop an algorithm and visualization for integration within the platform.
Time period, please tick at least 1 time period
o November – June
o May - November
References
Mark A Kramer, Chris Moesel, Interoperability with multiple Fast Healthcare Interoperability Resources (FHIR®) profiles and versions, JAMIA Open, Volume 6, Issue 1, April 2023, ooad001, DOI
Jason Walonoski and others, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, DOI
Kramer MA. Reducing FHIR "Profiliferation": A Data-Driven Approach. AMIA Annu Symp Proc. 2023 Apr 29;2022:634-643. PMID
Smits M, Kramer E, Harthoorn M, Cornet R. A comparison of two Detailed Clinical Model representations: FHIR and CDA. European Journal for Biomedical Informatics. 2015;11(2):en7–en17, PDF