SBIR Phase II: Development of a Smart Health Management System for Respiratory Patients

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be the redefinition of the care of asthma patients. Asthma is highly prevalent in the US, with 25 million patients currently suffering symptoms, and was the cause of 1.7 million emergency room visits in 2015, resulting in a huge societal cost. A predictive platform that can identify when a patient is at risk of an asthma attack would serve to reduce such costs by allowing intervention in a timely manner, thereby reducing the number of emergency room visits by asthma patients. Through the combination of remote monitoring of patient lung function, local environmental sensing, and data analytics, asthma can be understood at a personalized scale, a dramatic shift in the standard of care for this chronic and complex condition. Additionally, the platform developed in this project can be expanded to other conditions, such as chronic obstructive pulmonary disorder or cystic fibrosis, broadening its impact. Ultimately, it is envisioned that this project will result in a world without asthma attacks. This Small Business Innovation Research (SBIR) Phase II project will result in the development of a machine learning-based data analytics platform to identify in advance when asthma patients are at risk of an asthma attack, thereby enabling physicians to alter a patient's care plan and avoid hospitalization. To date, there is no integrated system to gather and analyze high-quality lung function and environmental data for asthma patients on a daily basis in order to truly analyze and predict a patient's health. As a result, asthma care is often reactive, requiring emergency treatment and resulting in poor clinical outcomes. The objectives of this Phase II research include the development and validation of a machine-learning based analytics model to generate new insights into the health of asthma patients; the development and implementation of a physician portal to translate such insights into clinical outcomes; and the development of new datasets to create a more robust and accurate platform. The result will be a live prediction platform to assist physicians in keeping asthma patients out of the emergency room. 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.