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|July 29, 2023
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Summary

This study presents a method for personalized home care using a common semantic standardized data model (CSSDM) to create healthcare knowledge graphs (KGs). This approach enhances data interoperability and secure sharing between health and social care systems.

Keywords:
EHRFHIRdata integrationhealthcare systeminteroperabilityknowledge graphontology

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Area of Science:

  • Digital Health
  • Health Informatics
  • Interoperability Standards

Background:

  • The digital health revolution and global health crises necessitate personalized home care solutions.
  • Current healthcare systems face challenges in seamless data exchange between health and social care services.
  • Interoperability is critical for efficient and secure data workflows in modern healthcare.

Purpose of the Study:

  • To introduce a methodology for semi-automated data extraction, transformation, and loading (ETL).
  • To generate personalized healthcare knowledge graphs (KGs) using a Common Semantic Standardized Data Model (CSSDM).
  • To facilitate secure and trustworthy data sharing between health information systems and cloud-enabled health services.

Main Methods:

  • Development of a Common Semantic Standardized Data Model (CSSDM).
  • Utilizing ISO 13940:2015 ContSys ontology for conceptual grounding.
  • Employing FHIR-based specifications for structural attributes to build the KG.
  • Implementing a semi-automated ETL process for data integration.

Main Results:

  • Successful generation of personalized healthcare knowledge graphs (KGs).
  • Demonstration of a standardized approach to healthcare data interoperability.
  • Establishment of a secure data communication pathway for multiple stakeholders.

Conclusions:

  • The CSSDM methodology offers a viable solution for healthcare data interoperability.
  • Personalized KGs can improve data accessibility and sharing in home care settings.
  • This approach supports collaboration between health information system providers and cloud health services.