Offering personalized services through dynamically formed ecosystems is essential to personal well ness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In addition, the platform also provides essential building blocks of personalization services: smarter analytics for active personalization and dynamic provisioning. While the former common service takes charge of inferring user well ness risks from multiple data sources on the fly and making risk-driven recommendations, the latter common service determines optimal platform pricing and resource allocation given the constraint of acceptable quality of service.

In this study we consider the problem of how to derive insight from medical records to define and improve healthcare services. As noted in many guidelines, risk factors are important to determining the care plan of chronic disease patients, e.g., pre-diabetic or diabetic patients who have started on hemoglobin A1c (HbA1c) control medications. Whereas the traditional management of chronic disease relies on a predetermined set of risk factors, without regard to patient-specific status, literature and recently released guidelines have suggested a less-prescriptive approach that allows flexibility in disease management plans to account for patient-centric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcare services. In this study, we evaluated automatic methods that can identify risk factors important for classifying Diabetic patients at risk of worsen disease progression. In particular, we used the prescription of cardiovascular disease (CVD) medication as the indicator of CVD co-morbidity development in Diabetic patients. We evaluated the summaries obtained with different sources of health information on the risk stratification task and examined the quality of the generated summaries using various proposed intrinsic metrics. In addition, we evaluated to what extent we can reduce the whole medical records into a small set of risk factors. The evaluation illustrates the potential of risk factor summarization and hints on how it can be used to enable practitioners in care planning and to support complex followup services at both the point of care and the extended care settings.

Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant upsurge in and enhanced complexity of diagnostic and treatment information has made the clinical decision-making process more difficult. Medical transaction charges are greater than ever. Population-specific financial requirements are increasing the economic burden on the entire system. Medical insurance and identity theft frauds are on the rise. The current lack of comparative cost analytics hampers systematic efficiency. Redundant and unnecessary interventions add to medical expenditures that add no value. Contemporary payment models are antithetic to outcome-driven medicine. The rate of medical errors and mistakes is high. Slow inefficient processes and the lack of best practice support for care delivery do not create productive settings. Information technology has an important role to play in approaching these problems. This paper describes IBM Research's approach to helping address these issues, i.e., the evidence-based healthcare platform.

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