Communications in Computer and Information Science (CCIS), (in press), Springer

Healthcare platforms are included in multiple domain-related systems which however produce and provide individual and unlinked data to other systems, with high heterogeneity among them. The concept of mapping data from healthcare platforms to other citizens’ daily data could create advantages in identifying and finding better decisions, strategies or guidelines against multiple diseases. In detail, in the current environment where there exist multiple data sources producing hundreds of megabytes of data, the creation of a baseline that aggregates and correlates clinical information, avoiding uncertainties, is mandatory. The current paper presents a new Electronic Health Record (EHR) paradigm, the Holistic Health Records (HHRs), as a form of health records that aggregate data from multiple sources and can provide a complete overview of a citizen, containing several health determinants. This information may be produced by several platforms and devices, at different times of the patient’s life, including data related to the daily activities, the social behavior, the vital signs, the personal examination, or the treatment of a citizen. Several standardization organisms define healthcare standards towards an interoperable healthcare eco-system, with HL7 Fast Healthcare Interoperability Resources (FHIR) being the standard that best suits the purpose of the HHRs. Consequently, the HHRs and the models that finally construct this new EHR paradigm, are based on HL7 FHIR, including data related with the citizens’ roles, the healthcare organizations, results deriving from diagnosis and clinical findings, as well as daily habits. The main goal of the HHR model is to facilitate and guarantee interoperability, being constructed based on existing FHIR libraries, having an additional goal to be also used as an independent component that can be tailored and adjusted for not only exchanging health data, but also categorizing it and classifying it into similar groups.