Sreeram Ramakrishnan

An Ecosystem Approach for Healthcare Services Cloud

Patient-centric healthcare and evidence-based medicine with the emphasis on prevention and wellness promise to deliver better and more affordable healthcare. At minimal, they require health related information to be shared among a community including patients, providers, payers, and regulators. It is important for IT systems to facilitate information sharing within such communities. Furthermore, we argue that it is highly valuable to develop IT technologies that can foster sustainable healthcare ecosystems for collaborative, coordinated healthcare delivery. The emerging cloud computing appears well-suited to meet the demand of a broad set of health service scenarios. In particular, the concept of shared infrastructure and services provides the foundation for supporting healthcare service ecosystems. This paper proposes an ecosystem approach to identify high-level requirements for cloud computing technologies to provide hosting environments for sustainable healthcare ecosystems. We draw the lessons and principles from the sustainable ecological ecosystems, review some of the existing IT-enabled healthcare ecosystems, and provide our view on the imperatives for cloud computing research to support future healthcare IT needs.

Information technology for healthcare transformation

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 Researchs approach to helping address these issues, i.e., the evidence-based healthcare platform.

Next Generation Wellness: A Technology Model for Personalizing Healthcare

Personalization or individualization of care is essential to the behavioral modifications and lifestyle changes that result in patient wellness (for good health or chronic disease management). The implementation of effective personalized care is hampered by the lack of reliable means to collect and process real-time data on individual contexts (preferences, constraints) and on adherence to care protocols and mechanisms to provide timely, customized cognitive coaching that is structured, consistent and informative to users.

DYNAMIC AND ACCRETIVE COMPOSITION OF PATIENT ENGAGEMENT INSTRUMENTS FOR PERSONALIZED PLAN GENERATION

Patient engagement is important to help patients become more informed and active in managing their health. Effective patient engagement demands short, yet valid instruments for measuring self-efficacy in various care dimensions. However, the static instruments are often too lengthy to be effective for assessment purposes. Furthermore, these tests could neither account for the dynamicity of measurements over time, nor differentiate care dimensions that are more critical to certain sub-populations. To remedy these disadvantages, we devise a dynamic instrument composition approach that can model the measurement of patient self-efficacy over time and iteratively select critical care dimensions and appropriate assessment questions based on dynamic user categorization. The dynamically composed instruments are expected to guide patients through self-management reinforcement cycles within or across care dimensions, while tightly integrated into clinical workflow and standard care processes.

Achieving ‘handoff’ traceability of complex system improvement

This paper presents an ontology for performance that is also the basis for an enterprise modeling and measurement framework for managing complex systems through improved traceability. The treaceability is achieved in the context of hand-offs that are typical of discovery in service-oriented environments. Examples in the health care industry illustrate the value of traceability in highly dynamic information technology services.

Automatic summarization of risk factors preceding disease progression an insight-driven healthcare service case study on using medical records of diabetic patients

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 follow-up services at both the point of care and the extended care settings.

Risk Mediation Cloud Service: Constructing Statistical Models of Patient Adherence for Sustainable Case Management

Regimen adherence is a common problem among chronic disease patients and has posed tremendous challenges to sustainable case management. Intervening on every single non-adherence case often creates unnecessary burdens for providers and considerable annoyance for patients, leading to wastes of resources and increasing patient churn rates. In current practice, mitigating the risk of non-adherence cases is a labor-intensive task that requires additional efforts from healthcare professionals to handle on a case-by-case basis. Previous work has investigated into the possibility of modeling patient adherence behavior, but left questions about the accountability of such models in services. With the prevalence of mobile devices and maturing cloud-based service models, more patient data are fed to cloud services from a variety of sources (e.g., health records, surveys, sensors, embedded GPS modules). In this paper, we propose a risk mitigation service that can utilize heterogeneous patient behavioral data sources to construct statistical models of adherence and estimate intervention need. We design evaluations to examine a number of dimensions in statistical models of patient adherence and their impacts on the task of determining critical cases and patient propensity to churn. Finally, we demonstrate how the new service is designed to assist adherence case management with models that can classify cases of different intervention needs and discuss its applications, limitations, and sustainability issues.