Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

In recent years, healthcare needs have shifted from treating acute conditions to meeting an unprecedented chronic disease burden. The healthcare delivery system has structurally evolved to address two primary features of acute care: the relatively short time period, on the order of a patient encounter, and the siloed focus on organs or organ systems, thereby operationally fragmenting and providing care by organ specialty. Much more so than acute conditions, chronic disease involves multiple health factors with complex interactions between them over a prolonged period of time necessitating a healthcare delivery model that is personalized to achieve individual health outcomes. Using the current acute-based healthcare delivery system to address and provide care to patients with chronic disease has led to significant complexity in the healthcare delivery system. This presents a formidable systems’ challenge where the state of the healthcare delivery system must be coordinated over many years or decades with the health state of each individual that seeks care for their chronic conditions. This paper architects a system model for personalized healthcare delivery and managed individual health outcomes. To ground the discussion, the work builds upon recent structural analysis of mass-customized production systems as an analogous system and then highlights the stochastic evolution of an individual’s health state as a key distinguishing feature

The Social fMRI: Measuring, Understanding, and Designing Social Mechanisms in the Real World

A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to design and execution of structured experimental interventions in-situ, in a way comparable to the reliability and intentionality of ex-situ laboratory experiments. In this paper we introduce the Friends and Family study, in which a young-family residential community is transformed into a living laboratory. We employ a ubiquitous computing approach that combines extremely rich data collection in terms of signals, dimensionality, and throughput, together with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, which has been deployed for over a year now. Finally, we describe a novel tailored intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for motivation and adherence, and allow us to quantify the contribution of different incentive mechanisms.U.S. Army Research Laboratory (Cooperative Agreement Number W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award FA9550-10-1-0122

Identifying Close Friendships in a Sensed Social Network

Studies have suggested that propinquity; social, cultural, physical and psychological similarities are major factors in close friendship ties. These studies were subject to human recall of interactions with no details of length or time of interactions. Recently, advancements in mobile technology have enabled the measurement of complex systems of interactions. This study uses social network analysis of data comprising of time-resolved sensed interactions to predict and explain close friendship ties via interactions at different periods, residence (floor) similarity and gender similarity. Results indicate residence (floor) proximity and duration of weekend night interactions have the potential of explaining close friendship ties.MIT Masdar Progra

Change in BMI Accurately Predicted by Social Exposure to Acquaintances

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R[superscript 2]. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.MIT Masdar ProgramMIT Media Lab Consortiu

A Systems Thinking Approach to Designing Clinical Models and Healthcare Services

Chronic diseases are on the rise, increasing in number and treatment regimen complexity. Consequently, the needs of patients with chronic diseases are increasing and becoming more complex and multi-faceted. Such chronic conditions require addressing not only the physical body, but also psychosocial and spiritual health. The healthcare delivery system, however, organically organized into departments based on physical organ systems. Such a configuration makes it ill-suited to provide comprehensive multi-faceted healthcare services that span multiple departments and specialties (e.g., podiatry and endocrinology for diabetes; primary care and psychiatry for behavioral health; and palliative care physicians, chaplains, and social workers for end-of-life care). To deliver new services, the medical field typically designs new clinical models to base its new services on. Several challenges arise from typical approaches to designing healthcare services and clinical models, including addressing only single conditions, describing models only at a high-level of abstraction, and using primarily narrative documents called text-based toolkits for implementation. This paper presents and uses systems thinking as an alternative strategy to designing clinical system models and healthcare services to alleviate many of the current design challenges in designing integrated services for chronic conditions. An illustrative example taking a clinical model and describing it as a system model is presented

Characterization of Diffusion Weighted Magnetic Resonance Imaging for Patients with Brain Tumors

Diffusion weighted Magnetic Resonance Imaging has been shown to be very useful for the clinical evaluation of patients with stroke. Although the acquisition of such data is a common feature on most MR scanners, the implication of the parameters that it uses are often not appreciated by the oncology community, especially for the application to patients with gliomas. The potential for providing quantitative information that describes the biological properties of tumor and surrounding brain parenchyma has therefore not been fully explored. The goal of this thesis was to characterize the variations in diffusion imaging parameters and determine how they may be used for the management of patients with brain tumors throughout the treatment of their disease. This thesis utilized MR spectroscopic imaging (MRSI) techniques to provide adjunct metabolic information. The relationship between the apparent diffusion coefficient (ADC) from DTI and levels of choline containing compounds from MRSI, both of which have been suggested to correlate to cell density, was assessed by grade and subtype within the anatomically heterogeneous tumor regions. There was evidence of a relationship between ADC and choline in some regions of grade IV gliomas but within no regions of grade II gliomas. This suggested independent information to be acquired from both DTI and MRSI. A method to assess deviation from mono-exponential decay in a clinically relevant scan time was developed as another possible method of assessing abnormality in grade IV glioma. Previously observed variations in grade II glioma subtypes was further assessed. A difference in ADC was evident and based on these findings, a method of visualizing this variation was developed using RGB color maps. This technique was applied on a new cohort of grade II gliomas and histopathology was assessed by image-guided biopsies to examine the histopathological variations. Diffusion parameters during treatment were evaluated for early non-invasive biomarkers. The ADC changes from mid- to post-treatment suggest such a possible early non-invasive biomarker. The results of this dissertation suggest that diffusion parameters play an important role in assessing gliomas. These are very important steps towards increasing the utilization of imaging in the management of patients with brain tumors

Architecting a System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

In recent years, healthcare needs have shifted from treating acute conditions to meeting an unprecedented chronic disease burden. The healthcare delivery system has structurally evolved to address two primary features of acute care: the relatively short time period, on the order of a patient encounter, and the siloed focus on organs or organ systems, thereby operationally fragmenting and providing care by organ specialty. Much more so than acute conditions, chronic disease involves multiple health factors with complex interactions between them over a prolonged period of time necessitating a healthcare delivery model that is personalized to achieve individual health outcomes. Using the current acute-based healthcare delivery system to address and provide care to patients with chronic disease has led to significant complexity in the healthcare delivery system. This presents a formidable systems’ challenge where the state of the healthcare delivery system must be coordinated over many years or decades with the health state of each individual that seeks care for their chronic conditions. This paper architects a system model for personalized healthcare delivery and managed individual health outcomes. To ground the discussion, the work builds upon recent structural analysis of mass-customized production systems as an analogous system and then highlights the stochastic evolution of an individual’s health state as a key distinguishing feature

From measures to action: can integrating quality measures provide system-wide insights for quality improvement decision making?

Background Quality improvement decision makers are left to develop an understanding of quality within their healthcare system from a deluge of narrowly focused measures that reflect existing fragmentation in care and lack a clear method for triggering improvement. A one-to-one metric-to-improvement strategy is intractable and leads to unintended consequences. Although composite measures have been used and their limitations noted in the literature, what remains unknown is ‘Can integrating multiple quality measures provide a systemic understanding of care quality across a healthcare system?’Methods We devised a four-part data-driven analytic strategy to determine if consistent insights exist about the differential utilisation of end-of-life care using up to eight publicly available end-of-life cancer care quality measures across National Cancer Institute and National Comprehensive Cancer Network-designated cancer hospitals/centres. We performed 92 experiments that included 28 correlation analyses, 4 principal component analyses, 6 parallel coordinate analyses with agglomerative hierarchical clustering across hospitals and 54 parallel coordinate analyses with agglomerative hierarchical clustering within each hospital.Results Across 54 centres, integrating quality measures provided no consistent insights across different integration analyses. In other words, we could not integrate quality measures to describe how the underlying quality constructs of interest—intensive care unit (ICU) visits, emergency department (ED) visits, palliative care use, lack of hospice, recent hospice, use of life-sustaining therapy, chemotherapy and advance care planning—are used relative to each other across patients. Quality measure calculations lack interconnection information to construct a story that provides insights about where, when or what care is provided to which patients. And yet, we posit and discuss why administrative claims data—used to calculate quality measures—do contain such interconnection information.Conclusion While integrating quality measures does not provide systemic information, new systemic mathematical constructs designed to convey interconnection information can be developed from the same administrative claims data to support quality improvement decision making