Seminar: New Approaches to Delivering Health Coaching Interventions to the Home: Computational Models and Inference of Patient State

Speaker

Holly B Jimison, PhD, FACMI

Professor, College of Computer & Information Science / College of Health Sciences

Director, Consortium on Technology for Proactive Care

Northeastern University, Boston, Massachusetts, USA

Abstract

There is an increasing focus on changing healthcare from being reactive and clinic- or hospital-based to being proactive and continuous, with an emphasis on interventions that make use of home monitoring and information/communications technology to facilitate scalable approaches for delivering care to the home. New developments in sensors, mobile apps and wireless devices have provided us with opportunities to track health behaviors. The new types of data streams that arise from continuous monitoring in the home environment include sleep quality metrics, activities of daily living, socialization measures, physical activity, gait measures and walking speed. Additionally, we also collect physiological home monitoring data used in disease management (blood glucose, peak flows, blood pressure, etc.). These new types of data streams present many challenges to “Big Data” modelers. The issues go beyond just thinking about volume of data and how to summarize or store it. For the behavioral monitoring in the home and environment there are now additional issues of 1) how to model context, bias and noise from signals derived from opportunistic low-cost sensors; 2) how to infer activities and behaviors from multiple sources, often with differing sampling rates and accuracies; and 3) how to manage privacy and security of sensitive data. Yet the opportunities for the discovery of new behavioral markers that will be useful in the management of health interventions are immense. With the continuous monitoring data we will be able to detect trends, using patients as their own controls, thus offering more sensitive and diagnostic measures by understanding what is normal for that individual. In addition, continuous or frequent data provides measures of variability in a signal, which is often diagnostic in itself. Finally, these new types of measures allow us to provide tailored health interventions with just-in-time feedback and support. These new monitoring techniques offer great promise for both reducing the cost of care and improving quality. 

Bio

Holly B. Jimison is a professor in both the College of Computer & Information Science and the College of Health Sciences at Northeastern University. Prior to joining Northeastern, she was Technology Advisor for the Office of Behavioral & Social Science Research at NIH. Her earlier work as medical informatics faculty at Oregon Health & Sciences University focused on technology for successful aging and scalable remote care. She served on the Executive Board of the Oregon Center for Aging & Technology and was past president of Oregon’s Health Information Management Systems Society chapter. As a fellow of the American College of Medical Informatics, Professor Jimison has made significant and sustained contributions to the field of biomedical informatics in the areas of pattern recognition, decision support, and consumer health informatics. She continues to deepen her influence in the field through her research on technology for successful aging and scalable remote care for older adults and patients with chronic conditions. As the director of the Consortium on Technology for Proactive Care at Northeastern University, she leads a multidisciplinary, multi-institutional effort to facilitate research in the area of home monitoring of health behaviors, including helping researchers address the challenges of big data related to large amounts of complex and noisy streaming data from multiple sources used to infer clinically relevant health behaviors.

Meeting details will be emailed to the faculty and students.

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