Authors:
Juha Puustjärvi
1
and
Leena Puustjärvi
2
Affiliations:
1
University of Helsinki, Finland
;
2
The Pharmacy of Kaivopuisto, Finland
Keyword(s):
Personal Health Record, Personal Data, Data Integration, Smart Home, Semantic Web, RDF, SPARQL.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Cloud Computing
;
Collaboration and e-Services
;
Complex Systems Modeling and Simulation
;
Data Engineering
;
e-Business
;
e-Health
;
Electronic Health Records and Standards
;
Enterprise Information Systems
;
Health Information Systems
;
Integration/Interoperability
;
Interoperability
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Platforms and Applications
;
Semantic Interoperability
;
Sensor Networks
;
Simulation and Modeling
;
Software Agents and Internet Computing
;
Software and Architectures
;
Symbolic Systems
Abstract:
A personal health record (PHR) is a record of a consumer that includes data gathered from different sources such as from health care providers, pharmacies, insures, the consumer, and third parties. Gathering data is technically complicated and error-prone due to the heterogeneities of the data sources. Further, due to failed or missed transmissions patients’ PHRs are often incomplete. However, a consumer should have easy access to their own health information as well as to any relevant information they need in order to make decisions about their own heath care. Nevertheless, no holistic approach for managing personal data beyond PHRs has been developed. Satisfying this challenge requires a means to capture and interconnect information from a variety of personal data sources and from public data sources. In order to achieve this goal, we have designed a Personal Record (PR). It is virtually a single record in the sense that it gives an illusion of a traditional standalone tool, suc
h as a traditional PHR, although its content may locate in a variety of sources, e.g., in systems storing data of health, gyms, smart homes, or personal notes. By means of PR we can also achieve synergy, e.g., in using health data together with welfare and smart home data we can produce outcomes that could not be achieved by functioning independently with single data sources. Moreover, using personal data together with public data sources we can also achieve more informal outcomes. The only requirement is that the data sources are in RDF-format, i.e., in the form of subject–predicate–object expressions. Then the SPARQL processor has the ability to process the data as well as to find the connections between triples from separate sources.
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