CASIDE: A data model for interoperable cancer survivorship information based on FHIR.

Cancer survivorship has traditionally received little research attention although it is associated with a variety of long-term consequences and also many other comorbidities. There is an urgent need to increase research on this area, and the secondary use of healthcare data has the potential to provide valuable insights on survivors' health trajectories. However, cancer survivors' data is often stored in silos and collected inconsistently. In this study we present CASIDE, an interoperable data model for cancer survivorship information that aims to accelerate the secondary use of healthcare data and data sharing across institutions. It is designed to provide a holistic view of the cancer survivor, taking into account not just the clinical data but also the patient's own perspective, and is built upon the emerging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. Advantages of adopting FHIR and challenges in information modelling using this standard are discussed. CASIDE is a generalizable approach that is already being used as a support tool for the development of downstream applications to support clinical decision making and can contribute to translational collaborative research on cancer survivorship.

Interoperable Medical Application for CTC Counting.

In the present poster we will explain how the development of an interoperable AI-powered application for Circulating Tumor Cells (CTCs) counting is addressed. We will explain the selection of the most appropriate information for early detection of distant metastasis, local recurrence and the data structure definition to be compliant with international standards and ontologies.