Increasing Healthcare Access to At-Risk Populations: Research-based Policies for Mobile Health Clinics

Abstract: IIS-1637347 Increasing Healthcare Access to At-Risk Populations:Research-based Policies for Mobile Health Clinics Project Description Mobile clinics play an important role in providing healthcare to at-risk populations in both urban and rural areas. Currently, over 1500 mobile clinics operate in the US and handle over 5 million visits per year providing essential healthcare services. These programs, however, have grown organically over time and it is important to establish evidence-based approaches to encourage a systematic expansion of this essential healthcare delivery model. This proposal addresses this issue through the use of epidemiological and economic GIS data, combined with proprietary routing software, to implement a program for optimal delivery of mobile clinic healthcare services in the Houston, Texas, region. The project involves eight different providers and includes designing systematic strategies for meeting future healthcare needs of low-income communities. The aim is also to develop models and techniques that can be implemented by mobile clinic programs throughout the country. Early estimates indicate that this project can result in 20% increase in mobile health clinic capacity, which could translate into significant savings in healthcare costs, and considerable improvements in quality of life for the poor. These results are in line with the NSF mission goal of promoting the advancement of health. The overall goal of this project is to optimize and implement a data-based program for coordinated deployment of mobile clinic programs. We will initially identify optimal expansion strategies for the eight current mobile clinics programs to meet the fast-growing demand for healthcare services in underserved communities. The project will then measure the potential of the developed models and techniques and apply them to mobile clinic programs in other regions of Texas and other states in the nation. To achieve the above goals, the team will first apply data mining and forecasting techniques to estimate present and future demand of healthcare services in selected communities. We will combine these approaches with advance GIS mapping tools and stochastic measures to identify target population clusters. We will also conduct survey and economic analysis to measure the operational cost and identify operational constraints in the present mobile clinic programs, and develop new optimization models for the deployment of the mobile clinic service. Then, we will develop new effective techniques to solve the developed optimization models to estimate the maximum capacity of the current mobile health clinic programs in the Houston region, and identify expansion strategies to meet the future service demand while minimizing cost. Lastly, we will estimate overall healthcare cost savings and quality of life impact at the community level at baseline and post-intervention.