An effective care transition from hospital to home is crucial to ensure optimal care for high-risk patients and to reduce the cost of avoidable readmissions that burdens the healthcare system. A common practice in technology-assisted care transition is to train and guide patients and family caregivers through the web and mobile phone-based systems for symptom reporting, vital signs monitoring, and providing feedback. Although effective, these systems have three major limitations: (a) the patient or caregiver has to actively measure and enter data into the system-which is error-prone, subjective, and often forgotten; (b) web or mobile-based interactions can be cumbersome and demanding-tasks like entering data, knowing health status, or getting and responding to an alert require typing and clicking through a series of forms/web pages; and (c) feedback from the system through messages and notifications is often ineffective and unnoticed. To overcome these limitations, this project employs a data-driven approach to develop a voice-enabled, context-aware, post-treatment self-care system in home settings. The proposed system will (a) monitor specific activities of a patient at home using advanced WiFi radio signal-based human activity recognition algorithms; (b) tailor natural language responses of voice assistants like Amazon Echo/Alexa based on the patient's location, activity, and health history; and (c) automate data-entry and reporting to reduce patient or caregiver burden. The system will be deployed in 40 colorectal and bladder cancer patients' homes for assessing usability, feasibility, and preliminary magnitude of benefits. The overarching goals of this project are aligned with the NLM's 10-year strategic plan for 2017-2027. Through automated collection, linking, curation, and modeling of voice and WiFi radio sensor data, this project will create an interconnected ecosystem of digital biomarkers that explain, influence, and predict health outcomes during care transition. By using low-cost voice assistants (less than $50) and ubiquitous home WiFi, it maximizes the dissemination and engagement of data-powered health to mass population. To foster the development of a data-ready workforce for the future, three Ph.D. students will work in this multidisciplinary project, a new mHealth course will be developed, and open science practices will be applied during the development, deployment, and dissemination phases of the project.
The proposed system can potentially ease the unbalanced demands between the workforce shortage of oncology professionals and the growing patient population. It brings effective personalized healthcare to the public at an affordable cost, which in effect reduces the overall cost of healthcare in the US by minimizing avoidable readmissions or preventable adverse events during care transition. A direct social impact is increased public awareness of alternative means for self-management during care transition.