Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data

Area of Science:

• Health Informatics
• Clinical Data Analytics
• Healthcare Information Systems

Background:

• Clinical data analysis is crucial for healthcare insights.
• Existing systems often lack interoperability and scalability for advanced analytics.
• Standardized data models like FHIR offer potential for improved data utilization.

Purpose of the Study:

• To develop a scalable data analytic framework for clinical statistics.
• To leverage the Fast Healthcare Interoperability Resource (FHIR) standard for data analysis.
• To facilitate patient-centered and cohort-based interactive analyses using FHIR APIs.

Main Methods:

• Developed an intelligent algorithm for clinical data analytics on FHIR data.
• Designed workflows for patient registration and laboratory information systems using FHIR APIs.
• Implemented an FHIR database with operations for descriptive data analytics (DDA) and cohort selection.
• Created a prototype user interface for DDA with data visualization capabilities.

Main Results:

• Demonstrated the framework's capability to generate various analytics from FHIR resources.
• Successfully facilitated patient-centered and cohort-based analyses.
• The FHIR database implementation supports efficient DDA and cohort selection.

Conclusions:

• The proposed FHIR-based framework effectively supports clinical statistics and data analysis.
• The framework enhances the utilization of clinical data for research and healthcare settings.
• This approach offers a scalable solution for interactive healthcare data analytics.