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Related Experiment Video

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Feasibility Queries in Distributed Architectures - Concept and Implementation in HiGHmed.

Reto Wettstein1, Hauke Hund2, Insa Kobylinski2

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|May 27, 2021
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Summary

This study introduces a novel process for automated, distributed feasibility queries to estimate patient cohort sizes across institutions. This enables researchers to effectively merge medical data beyond single organizations for enhanced research capabilities.

Keywords:
BPMNFHIRFeasibility queriesdistributed processesmedical informaticssecondary use

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Area of Science:

  • Health Informatics
  • Medical Research Data Management

Background:

  • Medical routine data holds significant research potential but faces challenges in data integration.
  • Existing Medical Data Integration Centers aggregate data, but cross-institutional data merging is crucial for comprehensive research.
  • Researchers require methods to query cohort sizes across institutions to address complex research questions.

Purpose of the Study:

  • To present a solution for fully automated and distributed feasibility queries for cohort size estimations.
  • To facilitate the merging of medical data beyond institutional boundaries for research purposes.

Main Methods:

  • A process for automated and distributed feasibility queries was developed.
  • The process adheres to the Business Process Model and Notation (BPMN) 2.0 standard.
  • The underlying data model utilizes Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) R4 resources.

Main Results:

  • A novel process for automated, distributed feasibility queries (cohort size estimations) was successfully designed.
  • The process integrates data across multiple institutions, overcoming previous limitations.
  • The solution is currently being implemented across nine institutions in Germany.

Conclusions:

  • The proposed automated and distributed query process enhances the utility of medical routine data for research.
  • This approach facilitates cross-institutional data analysis by enabling cohort size estimations.
  • The ongoing deployment signifies the practical applicability and potential impact of the solution.