SCC-PG: Just in Time Intervention for Patients with Chronic Heart Diseases in Arizona tribes

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Cardiovascular diseases are the leading cause of death in the United States. Cardiovascular diseases are often chronic conditions that involve several costly emergency visits or long hospitalizations for the patients. Limited access to medical facilities in rural communities can result in worse health outcomes for these patients, in particular, the American Indian (AI) patients living in remote and rural areas. A considerable number of Arizonan AIs with cardiovascular conditions may be at risk of missing the window of opportunity for effective treatment and experiencing a lower chance of survival because of living far away from medical service providers. Arizona has the third largest population of Indian Americans who live in rural, tribal and often extremely isolated areas. The cardiac patients living in these areas do not have the required timely access to care, in particular to specialty services like cardiologists. Therefore, an important challenge related to these conditions for rural patients is the ‘early detection of deterioration in symptoms’, which is critical for ‘just in time’ interventions. This planning project is a collaborative effort among the Northern Arizona University (NAU) and University of Michigan (UM) as well as community leaders in rural and tribal health to discuss the best strategies to utilize an integrated remote heart monitoring system to benefit the patients with chronic heart conditions who live in rural, remote and isolated tribal areas.This planning project offers several innovative approaches by working with the tribal AI community to begin(i) Developing a new remote heart monitoring technology to predict the deterioration of the symptoms and occurrence of critical heart conditions in patients with some common chronic cardiac conditions such as atrial fibrillation and congested heart failure. Several remote heart monitoring systems have focused on reliable detection of such events, however by the time that the device alerts the patients or their family, it is already too late to seek medical help for the patients who live in rural and remote areas. Hence, our early prediction framework can give the patients enough time to seek medical assistance. This system can also help caregivers control severe symptoms, reduce readmissions, and reduce the cost associated with care; (ii) Developing deep learning-based and Markov-based prediction methods; and (iii) Developing on-device prediction methods that can work independently of the cloud in order to service the patients with no access to broadband internet. This study can be replicated for a wide range of other diseases and medical conditions and in different geographic regions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.