Securing Underserved Communities from Drug Abuse with Drone-Based Smart Medication Delivery
Lead PI:
Zhenbo Wang

Despite considerable efforts in combating substance use and exploring novel treatment and recovery strategies, current practices have not been able to connect many patients from underserved low-income communities in rural areas with available healthcare resources due to infrastructure challenges, inaccessibility to pharmacies, patient’s inability to drive, and lack of transportation. Responding to such issues involves interactions among diverse community stakeholders, including healthcare providers, research institutes, government agencies, nonprofit organizations, and community residents. However, these stakeholders are typically isolated or inefficiently organized, which leads to unbalanced community coordination and inefficient decision-making in substance use responses. To address these challenges and gaps, this Smart and Connected Communities Panning Grant (SCC-PG) will go beyond the current practices to promote substance use disorder treatment by connecting multiple community stakeholders via innovative community-based coordination mechanisms and connecting the residents from rural areas with urban medical resources via novel mobility technologies. Situated at the heart of Appalachia, Knox County and the surrounding communities are selected as an ideal natural testbed to demonstrate how the proposed delivery mechanism and framework can address infrastructure challenges, hurdle interaction and communication barriers, and help improve access to necessary medications among individuals with substance use disorder.

The goal of this SCC-PG project is to create connected systems and intelligent technologies to advance the understanding of interactions and perceptions among people who use drugs, healthcare providers, and government agencies; and build upon the in-depth understanding to engage communities to enable novel practices to treat substance use disorder and reduce illicit drug use. Specifically, the project aims to 1) develop community perception models that reveal how patients’ choices and concerns and public’s acceptance on truck- and drone-enabled delivery mechanisms influence the service patterns and operations, 2) create new truck- and drone-assisted healthcare delivery frameworks and operations through integration of these quantified perception models and operation constraints from healthcare providers and regulatory agencies, and 3) establish novel community engagement models that channel information in a connected way, through which patients, healthcare providers, scientists, engineers, government officials, and volunteers are all involved in a timely fashion to foster informed and all-inclusive decisions and practices to achieve connected interventions and treatment. This research is expected to lay a foundation for more comprehensive design and control of innovative mobility systems and connected and collaborative frameworks that span social and technical dimensions with community engagement to improve medication access of underserved people with substance use disorder.

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.

Zhenbo Wang
Dr. Zhenbo Wang is currently a tenure-track assistant professor in the Department of Mechanical, Aerospace and Biomedical Engineering (MABE) at the University of Tennessee, Knoxville (UTK). He received his Ph.D. in Aerospace Engineering from the School of Aeronautics and Astronautics at Purdue University, West Lafayette, IN. His research aims to improve the level of autonomy and autonomous operations of highly complex dynamical systems by solving real-world problems and creating and testing theoretically solid solutions that can enable novel mission capabilities and transform daily life. He has dedicated his research efforts mainly to the development of advanced control, optimization, and machine learning techniques for space, air, and ground vehicle applications. Recently, he has been focused on the combination of data-driven and model-based methods for real-time decision-making, control, and optimization of vehicular systems for emerging applications such as advanced air mobility, urban air transportation, connected and automated vehicles, drone delivery for health and wellness, and smart agriculture. Dr. Wang has established and maintained a strong research group. His total share of external funding is over $2.2 million from NSF, DOD, NASA, USDA, and ORNL. The cumulative sum of all awards he has been involved in is over $9.5 million. He received the NSF CAREER award in 2023.
Performance Period: 04/01/2023 - 03/31/2024
Institution: University of Tennessee Knoxville
Award Number: 2231710