Holistic Recommendation System Framework for Health Care Programs

Abstract

AbstractGovernment health care programs for disease surveillance, control, and prevention adopt a strategized technique in collecting information from various sources, monitor the trend of disease outbreak, initiate actions, and detect the response for further course of action. The main components for implementing such programs are integration and decentralization of surveillance activities at the zonal, and subzonal levels, developing and deploying human resources involving field workers to experts, strengthening public health lab facilities, and most importantly embracing digital information communications technology (ICT), the key component in binding all other components in bringing the effective implementation of the program. The proposed work explains the adoption of ICT through a holistic framework depicting the strategy and techniques in implementing the program. It discusses the digitalized strategy in data collection as well as data compilation, data analysis through integrated decision support modules, and proper presentation and dissemination of information data. The key element of the framework involves adopting various recommendation systems techniques in the analysis of data for information filtering, decision support, and data dissemination at various stages of handling the data. It suggests the selection of different recommendation techniques to address various needs such as predicting the disease dynamics and provide decision support in predicting likely hood of the next outbreak of the disease, rate of speeding, demographic details of spread, etc. The framework proposes a strategy to use smartphone-based mobile intervention to support the program and also proposes an API-based data exchange architectural style for data gathering and dissemination connected to various sources and endpoints.KeywordsPublic healthHealth surveillanceHealth care analyticsHealth recommendation systemsm-Health