A Semantic and Collaborative Approach to Community Health-care in Underserved Areas

Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable computing devices called Remedy which assists CHWs in decision making and facilitates collaboration among them. Remedy can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Remedy system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.


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collaboration in health-care, community health-care, medical diagnosis, mobile health, reasoning, semantic web

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UMBC

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