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FHIR Standard-Based Oncology Data Model for Cancer Screening: Design and Implementation Study.

Manisha Mantri1, Sayali Satokar1, Pritam Tambe1

  • 1Centre for Development of Advanced Computing (C-DAC), India, Pune, India.

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Summary
This summary is machine-generated.

This study introduces a Fast Healthcare Interoperability Resources (FHIR)-based oncology data model (ODM) for improved cancer screening and risk assessment data exchange. The model enhances interoperability across the cancer care continuum, supporting early detection and management.

Keywords:
FHIRFast Healthcare Interoperability ResourcesODMcancerdata modelinteroperabilityoncology data modelrisk assessmentscreening

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Area of Science:

  • Health Informatics
  • Oncology
  • Digital Health

Background:

  • Cancer remains a leading global cause of mortality, necessitating early detection and risk assessment for improved patient outcomes.
  • Challenges in oncology data interoperability hinder effective cancer care delivery and research.
  • Existing data systems struggle to integrate diverse oncology data, including clinical, genetic, and imaging information.

Purpose of the Study:

  • To develop and validate a Fast Healthcare Interoperability Resources (FHIR)-based oncology data model (ODM) for structured cancer data capture and exchange.
  • To focus on screening and risk assessment for breast, cervical, esophageal, lung, and oral cancers within an Indian pilot project.
  • To enhance data interoperability across all stages of cancer care.

Main Methods:

  • Developed an ODM incorporating 5 patient journey phases: encounter, risk assessment, clinical investigation, treatment, and outcome.
  • Utilized FHIR Revision 4 standard, creating custom FHIR profiles and mapping terminology to SNOMED CT, LOINC, and ICD-10.
  • Employed FHIR Shorthand (SUSHI) and IG Publisher for implementation guide creation, followed by technical, clinical, and usability validation.

Main Results:

  • The FHIR-based ODM enhances interoperability across the cancer care continuum, from screening to treatment.
  • The implementation guide includes 25 oncology-specific FHIR profiles and 50 standardized value sets, ensuring semantic and syntactic interoperability.
  • Validated FHIR conformance, terminology binding, and usability of screening questionnaires, demonstrating practical application in clinical and community settings.

Conclusions:

  • The FHIR-based ODM provides a unified framework for structured, interoperable cancer data exchange, crucial for screening and risk assessment.
  • This initiative represents a significant step in applying FHIR standards to oncology data in India.
  • Integration with national digital health systems is recommended for consistent data sharing and informing cancer policy and precision oncology.