Shaw Submits NIH R01 Multi-PI Application

Kudos to Ryan Shaw and Lori Orlandi and their entire team for the submission of their NIH R01 Multi-PI application entitled "Advancing Precision Health: Actionable Genomics & Mobile Health Technologies." This proposal requests funding for a four-year period with a start date of July 1, 2019. 

Health care is organized primarily around episodic interactions with patients. The challenge with episodic care in many chronic illnesses is that patients do not receive interventions when and where they need them most – at the point when a problem is about to occur or is occurring. This is a problem for patients with complex illnesses that require self-management and large quantities of disparate data types for clinicians to optimize treatment decisions. What patients need is to have treatments tailored around their daily health needs, be empowered to adapt to challenges at home, and have clinicians intervene early before they decompensate.

Studies demonstrate the short-term (<6 month) feasibility of collecting multiple real time data, but missing are 1) longitudinal data sets that combine genomic, clinical, and daily biological and behavioral data, 2) inclusion of diverse participants, and 3) understanding of the data visualization and clinical decision support features needed for clinical care that will allow for better insight into individual medical and care delivery needs in real time. Conditions most likely to benefit from this approach are those with comorbidities, genomic markers that inform diagnosis and management, defined data driven outcome metrics, and self-management and home-based behavioral interventions (e.g. heart failure and diabetes). Given the high morbidity and mortality costs of these disease types it is critical we extend the completed feasibility work to evaluate the potential of such data to inform earlier interventions and improve long-term outcomes.

We propose an observational mixed-methods control-comparison study of of racial, ethnic, and socioeconomically diverse patients (N=360) with diagnoses of heart failure (n=180) and diabetes mellitus (n=180). Data generated from digital health devices will be plotted as trajectories and merged with electronic health record (EHR), family history and genomics data. We will then conduct interviews with patients and providers to explore ways to integrate these data into care delivery models. Via EHR data, we will compare these participants to a standard of care control comparison group (n=360,180/disease) to assess disease status and changes over time.

Involved Faculty: 
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