Authors:
Juha Puustjärvi
1
and
Leena Puustjärvi
2
Affiliations:
1
Helsinki University of Technology, Finland
;
2
The Pharmacy of Kaivopuisto, Finland
Keyword(s):
Medication data, e-Prescription, Personal health record, Business process management, XML, RDF.
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
;
Enterprise Information Systems
;
Health Information Systems
;
Healthcare Management Systems
;
Information Systems Analysis and Specification
;
Integration/Interoperability
;
Interoperability
;
Knowledge Management
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Platforms and Applications
;
Semantic Interoperability
;
Sensor Networks
;
Simulation and Modeling
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Software and Architectures
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
A personal health record (PHR) provides a summary of the health and medical history of a consumer. It includes data gathered from different sources such as from health care providers, pharmacies, insures, the consumer, and third parties such as gyms. Importing data into PHRs is problematic as different data sources use different representation formats. In addition, automating the importation is problematic as many of the sources are built based on proprietary solutions, and thereby are not able to interoperate with PHR systems. In this paper, we described how the importation of e-prescriptions into PHRs can be automated. In our solution e-prescriptions are produced by an electronic prescription writer (EPW) which functionality is specified by BPMN notation and then translated into executable WS-BPEL code. The EPW sends CCR-formatted data of e-prescriptions into PHR system, which first transforms (if needed) the data into the format of the used PHR system, and then stores them into PH
Rs. In particular, we consider how a PHR system can transform a CCR-formatted data into RDF/XML format. The gain of such transformation is that we can implement the PHR system as an application of a knowledge base system, and thereby we can capture the wide expression power of knowledge base system’s query interface into the PHR system.
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