Performance assessment of the US states on Telehealth utilization after COVID-19 outbreak by using DEA optimization technique

Project Description Problem Definition
Telehealth is unprecedently implemented to deal with the public health emergency leading to lockdowns and staffing shortages in the COVID-19 outbreak. In contrast to the limited utilization due to various barriers prior to the pandemic, the realizations in the pandemic have shown the potential benefit to shrink health inequality, especially for patients dwelling in remote areas, and compensate for the healthcare resource misallocation resulting from the public health crisis.
The existing literature mostly pays attention to the clinical setting and attempts to understand the effectiveness of telehealth implementation by specialties, for example, the long-living application in mental health, such as telepsychiatry, has developed a consolidation model over the dimension of practices, modalities, and settings to show its accessibility and affordability (McCord et al., 2020). However, the absence of research focusing on the influence on macro-level telehealth implementation, especially the large-scale utilization after COVID-19 outbreak, has led to a knowledge chasm among policymakers and other stakeholders in the telehealth ecosystem.
This research, to close this research gap, attempts to ideate an overarching conceptual framework and create a corresponding quantitative model. The objectives of the project are threefold: (1) To assess the relative efficiency of the US states on telehealth utilization and determine their efficiency scores (2) To make suggestions on how inefficient states can improve their efficiency (3) To decide on the most significant factors that impact the efficiency of the states. To achieve the aims of (1) and (2), Data Envelopment Analysis (DEA) will be used which is a linear programming-based optimization technique that considers multiple inputs and outputs. The conceptual framework takes into account policy legislation, technology infrastructure, and social determinant of health (SDOH) including economic stability, education access and quality, healthcare access and quality, social and community context of the states. Then, Tobit regression will be conducted to achieve the aim of (3) that considers the output of the DEA model as a dependent variable and all other variables as independent variables.

Significance of the project
To the best of my knowledge, there is no study that assesses the performance of the states on telehealth utilization which is one of the best technology alternatives to deal with public health crisis during the pandemic. So, this study will promote a better understanding of how states are performing about telehealth utilization after COVID-19 outbreak and how inefficient states can improve their efficiency to be pure (100%) efficient with respect to the other states. This study will also provide what factors are so significant in the efficiency of the states so that it will suggest a direction for policymakers to rethink and optimize resource allocation in the posterior public health emergency.

Skills that the students should have to join this project:
This project is a quantitative project which includes statistical and optimization techniques to solve the problem. So, the students that will able to join this project should have:
- Statistical and optimization background
- The ability to do research and know how to write a research paper
- They should know R/Python programming languages
- If they have journal papers/conference papers, it is most preferable.

Skills that the mentors should have:
- They should be interested in statistics and optimization
- They should be interested in healthcare and telehealth
- They should have published journal papers about DEA
- They should have supervised projects