This report provides an overview of the data modelling approaches adopted in iHELP project. The main goal is to capture the necessary data for personalized healthcare scenarios and ‘transform’ them into ready-to-use parameters into the Holistic Health Records (HHRs). Based on the outcomes of the CrowdHEALTH project1, HHRs are structured health records that include several types of information relevant to patients’ health status, such as medical and clinical data, laboratory measurements as well as data related to patients’ social life and physical activity. The basis for the mapping procedure is the utilization of the Fast Healthcare Interoperability Resources (FHIR) specification, which is a standard for electronically exchanging healthcare information and data, taking advantage of existing logical and theoretical models. FHIR is a new specification based on industry tactics, capitalizing the successes and challenges gained through defining and implementing standards such as the HL7 v1&v2 and CDA. The description and expansion of the HHR model will be based on an ontological level in order to accomplish a high degree of semantic interoperability. Moreover, taking into account that iHELP is focused on different factors associated with Pancreatic Cancer and relevant care pathways, appropriate standards will be used. For this reason, the mapping procedure is based on mCode standard, which focuses on concepts and fields from the oncological domain. The ultimate goal is to achieve sharing of specific aspects of HHR between different health platforms taking advantage of key characteristics of HHR that will allow this inter-exchange, such as their scalability and extensibility.