Date of Graduation
8-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Eksioglu, Sandra
Committee Member
Eksioglu, Burak
Second Committee Member
Zhang, Shengfan
Third Committee Member
Eswaran, Hari
Keywords
Data analytics; Healthcare; Machine learning; Statistical analysis; Telehealth
Abstract
This dissertation investigates the utilization of telehealth services, initially focusing on the Arkansas healthcare system and then extending the analysis nationwide. It aims to understand the factors influencing telehealth adoption and its impact on healthcare delivery. After examining telehealth utilization in Arkansas from 2018 to 2022, the research utilizes a comprehensive dataset from Epic Cosmos, which includes a wide range of patient and visit data from multiple healthcare facilities across the United States from 2018 to 2023. This timeframe allows for a detailed analysis of telehealth trends before, during, and after the COVID-19 pandemic. In Chapter 2, we analyze key demographic and socioeconomic factors affecting telehealth use in Arkansas. We identify population density, broadband subscription, and computer use as significant determinants, with education level and disability also playing crucial roles. These insights are critical for policymakers and healthcare providers to make informed decisions to enhance telehealth accessibility. The findings suggest that improvements in broadband infrastructure, computer literacy, and educational initiatives can significantly enhance telehealth’s effectiveness and reach, particularly in rural and underserved areas. In Chapter 3, we examine the insurance coverage for telehealth services, revealing changes in reimbursement policies and their impact on telehealth and in-person visit patterns. The analysis highlights a decrease in in-person visits covered by Medicare and Medicaid from 2020 to 2022, compared to 2019, underscoring the role of insurance in shaping healthcare delivery trends. We also include a procedure to calculate appointment performance metrics such as waiting time and appointment length. Our analysis reveals Psychiatry, OB/GYN, and Family Medicine had the highest number of telehealth visits. The waiting time for Psychiatry telehealth visits was almost 50% shorter than in-person visits. These findings highlight the potential benefits of telehealth in providing access to healthcare, particularly for patients needing psychiatric care. In Chapter 4, we delve into the resource utilization in telehealth, assessing appointment durations and patient-to-provider ratios across various specialties. We identify specialties with the highest telehealth use and examines geographical variations in telehealth access, particularly between rural and urban regions. Our findings show significant increases in telehealth use, reduced appointment durations, and improved patient-to-provider ratios. The study underscores the potential of telehealth to enhance healthcare accessibility and resource utilization. In conclusion, this dissertation provides valuable insights into the factors driving telehealth adoption and its implications for healthcare delivery. It highlights the need for special intervention in technology and education to enhance telehealth accessibility and effectiveness. The findings contribute to the growing body of knowledge on telehealth, offering practical recommendations for policymakers, healthcare providers, and insurers to improve telehealth services and ensure equitable healthcare access for all populations.
Citation
Cengil, A. (2024). Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, and Machine Learning Techniques. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5514