JMIR Publications

ABSTRACT

Background:

Cancer is one of the leading causes of death worldwide. Cancer mortality can be reduced by early detection via screening, diagnosis, and effective management. Risk assessment is a vital part of the cancer screening process, especially for breast, cervical, and esophageal cancers, where early detection improves outcomes. Identifying high-risk individuals based on family history, genetics, lifestyle, and environment makes targeted and personalized screening possible, enhancing accuracy and resource efficiency. The inherent complexity of oncology data, which includes a wide array of clinical observations, laboratory results, radiology images, treatment regimens, and genetic information, poses significant challenges to data interoperability and exchange.

Objective:

We propose a Fast Healthcare Interoperability Resource (FHIR) standard-based Oncology Data Model (ODM) that enables the capturing, sharing, and processing of oncology data at various phases in cancer care across the health systems. We particularly focus on screening for five types of cancers, i.e., Breast, Cervical, Esophageal, Lung, and Oral, for risk assessment using the FHIR Questionnaire Resource for use in the Meghalaya FIRST Cancer Care (FCC) pilot project in India.

Methods:

ODM was developed based on the data collected during the cancer patient journey across five key phases: encounter, risk assessment, clinical investigation, treatment, and outcome. Essential oncology data elements were identified and modeled using HL7 FHIR R4 standards. Custom FHIR profiles were created for cancer-specific use cases, along with terminology mapping to standard coding systems such as SNOMED CT, LOINC, and ICD-10. The implementation guide was generated using FHIR Shorthand (FSH), SUSHI, and the HL7 IG Publisher. A demonstration application was also developed to support stakeholder training and facilitate adoption.

Results:

The data model was developed using HL7 FHIR to enhance interoperability across the cancer care continuum, from screening to treatment. The implementation resulted in the creation of a FHIR Implementation Guide featuring 25 oncology-specific resources and 50 standardized terminology value sets to support consistent and semantically accurate data exchange. Central to the model were the FHIR Questionnaire and QuestionnaireResponse resources, which were customized to enable interoperable, structured data collection in both clinical and community-based digital health settings. These profiles were designed to support critical cancer screening and assessment workflows. The demonstration tool enabled hands-on exploration of the FHIR profiles and supported engagement with stakeholders. The comprehensive approach supports more integrated, data-driven oncology care within digital health systems.

Conclusions:

The development of standardized profiles for cancer screening and assessment is a transformative approach to achieving syntactic and semantic interoperability right from screening, diagnosis, to treatment and improving overall cancer care and health service delivery. This work explores the implementation of Questionnaire and Questionnaire Response Resources using digital health standards. When integrated with all the cancer patient journey stages, the approach can accelerate cancer care and support a more responsive and effective healthcare system