DataDriven xe2x80x9cMarket Basketxe2x80x9dPricing and Personalized Health Information Services Using Salesforces ModelDriven Systems Service Design Risk management and customer loyalty are increasingly critical. Being able to reduce risks through datadriven packaging and pricing and sustaining customer loyalty are therefore important. This study presents a case study on datadriven packaging (simulating marketbasket analysis) and pricing to enable patients, and caregivers to obtain personalized health information services and for hospitals, knowledge sharing on marketing opportunities (who and what to target), which services should be paired with which, corresponding pricing and to predict based on past trends, resource allocation. Our personalized health information system, Ehealthzone, builds on Salesforceu0027s modeldriven systems service design. It is currently a management information system but we hope to include intelligence eventually. Technology acceptance by sample users indicate positive acceptance. Ehealthzone is perceived as useful especially if hospital fees are expensive, and quality and reasonablypriced services are preferred. Most users intend to use the system when it is released. Expert evaluations view the pricing and booking system and gaining insights through the analytics and dashboards as Ehealthzonexe2x80x98s greatest strengths.

D&D: A Distributed and Disposable Approach to Privacy Preserving Data Analytics in UserCentric Healthcare Benefiting from the modern information and communication technologies, user centricity has become a clear evolution trend in healthcare. Unfortunately, given the high sensitivity of health data and the uncertainty in user environments, usercentric healthcare systems inevitably suffer from more frequent privacy threats, not to mention that technologies and business of data exploitation have generally outpaced the current privacy regulations and laws. Although there exist welldefined privacy preserving mechanisms, such as Data Encryption, Data Perturbation, and Deidentification, they have been considered inadequate to address the diverse privacy challenges in big healthcare data analytics. Our argument is that, before considering any sophisticated mechanism, practitioners should first try to imitate human memoryu0027s forgetting process as an intrinsic privacy preserving strategy in usercentric healthcare. Technically, we implement this strategy by changing traditional data analytics routines into a distributed and disposable manner, so as to naturally exclude the data ownersu0027 sensitive information. The technical implementation essentially acts as a concrete HowTo solution to satisfying a fundamental principle of privacy law, i.e. data minimization. We have initially applied our work to a smart bed project for sleep quality analytics, and received positive feedback on the effectiveness of privacy preservation in suitable homecare scenarios.