Access to Healthcare Resources Via a Virtual Voice Assistant

Joan John, Dr. Daehan Kwak, School of Computer Science and Technology, Kean University, 1000 Morris Ave, Union, NJ 07083

In this new era of rapid technology advancement, we are experienced tremendous growth of interaction between humans and machines. Within that growth, the Internet of Things (IoTs), such as the smart speakers like the Amazon Alexa and the Google Home that are revolutionizing the ways of our lives. For instance, turning light switches on/off with just an ease of a verbal command. In this research, a virtual voice assistant is implemented into the Google Home to provide patients or individuals the power to search for community resources taken into account the low literacy and limited access to technology and information they experience. This project specifically focuses on supporting individuals living with severe mental illness (SMI) and/or substance use disorder (SUD) who are vulnerable to and addicted by social and environmental factors. These individuals are faced with limitations to navigate through community resources and exploring opportunities to bridge them to the needed services. Our virtual voice assistant will guide and provide information regarding community resources and needed services regarding the individual’s needs so that individuals can progress towards recovery, health, and well-being. A decision tree constructed by real care managers is integrated as a dialogue tree into the virtual voice assistant where the users use voice commands to easily navigate through health-related resources.  We anticipate a broader impact especially after the needs of these individuals are met. Thus, they will more likely participate, engage in, and benefit from mental health and substance use treatment thereby increasing the rate of recovery, overall health, and wellbeing as a whole. For future work, we will build upon the data that is collected for visual and statistical analysis.

Additional Abstract Information

Presenter: Joan John

Institution: Kean University

Type: Poster

Subject: Computer Science

Status: Approved

Time and Location

Session: Poster 5
Date/Time: Tue 12:30pm-1:30pm
Session Number: 4018