Home Care Decision Support Using an Arden Engine - Merging Smart Home and Vital Signs Data

Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig - Institute of Technology and Hannover Medical School, Germany.
Studies in health technology and informatics 02/2009; 146:483-7. DOI: 10.3233/978-1-60750-024-7-483
Source: PubMed


The demographic change with a rising proportion of very old people and diminishing resources leads to an intensification of the use of telemedicine and home care concepts. To provide individualized decision support, data from different sources, e.g. vital signs sensors and home environmental sensors, need to be combined and analyzed together. Furthermore, a standardized decision support approach is necessary.
The aim of our research work is to present a laboratory prototype home care architecture that integrates data from different sources and uses a decision support system based on the HL7 standard Arden Syntax for Medical Logical Modules.
Data from environmental sensors connected to a home bus system are stored in a data base along with data from wireless medical sensors. All data are analyzed using an Arden engine with the medical knowledge represented in Medical Logic Modules.
Multi-modal data from four different sensors in the home environment are stored in a single data base and are analyzed using an HL7 standard conformant decision support system.
Individualized home care decision support must be based on all data available, including context data from smart home systems and medical data from electronic health records. Our prototype implementation shows the feasibility of using an Arden engine for decision support in a home setting. Our future work will include the utilization of medical background knowledge for individualized decision support, as there is no one-size-fits-all knowledge base in medicine.

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    • "This person-centered, ubiquitous care scenario demands new forms of living and care [3,4], and along with this a new kind of information system architecture with regard to health information. This architecture must include not only the personal or home environment as a source of relevant health data, but also the caregivers and other health professionals as opposed to current institution-centric architectures [5,6]. Such systems are called 'sensor-enhanced health information systems (seHIS)' [7]. "
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    • "Within this context, we have developed a decision support module based on a dynamic Bayesian network that controls exercise training autonomously based on vital signs data [30], so that it may be used in an unsupervised training situation at home. A primary focus of our research is the integration of the DSS in existing or new health information system infrastructures, thus enabling to consider clinical context information such as diagnoses, laboratory findings or past test results as well as caregiver and patient information for the purpose of decision making [24]. "
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