Research Article
Rafiki: A Semantic and Collaborative Approach to Community Health-care in Underserved Areas
@INPROCEEDINGS{10.4108/icst.collaboratecom.2014.257299, author={Primal Pappachan and Roberto Yus and Anupam Joshi and Tim Finin}, title={Rafiki: A Semantic and Collaborative Approach to Community Health-care in Underserved Areas}, proceedings={10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing}, publisher={IEEE}, proceedings_a={COLLABORATECOM}, year={2014}, month={11}, keywords={collaboration in health-care mobile health medical diagnosis semantic web reasoning community health-care}, doi={10.4108/icst.collaboratecom.2014.257299} }- Primal Pappachan
Roberto Yus
Anupam Joshi
Tim Finin
Year: 2014
Rafiki: A Semantic and Collaborative Approach to Community Health-care in Underserved Areas
COLLABORATECOM
IEEE
DOI: 10.4108/icst.collaboratecom.2014.257299
Abstract
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 Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki 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 Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.
