Optimizing telehealth utilization during COVID-19: enhancing efficiency and equity through data envelopment analysis and machine learning

Ying-Chih Sun (), Ozlem Cosgun () and Raj Sharman ()
Additional contact information
Ying-Chih Sun: East Central University, Department of Business Administration
Ozlem Cosgun: Montclair State University, Department of Information Management and Business Analytics, Feliciano School of Business
Raj Sharman: University at Buffalo

Operational Research, 2025, vol. 25, issue 4, No 21, 29 pages

Abstract: Abstract This study investigates the efficiency of telehealth utilization across U.S. counties during the COVID-19 pandemic, with a focus on identifying the key drivers of efficiency, especially in rural and underserved areas. The primary research goal is to assess the efficiency of telehealth utilization and examine patterns of resource misallocation. A hybrid methodological framework integrates Data Envelopment Analysis to measure county-level efficiency scores and Machine Learning techniques to uncover the most influential predictors of those scores, revealing patterns in resource distribution and infrastructure that contribute to disparities in telehealth performance. Using secondary data from CMS’s 5% Medicare Fee-For-Service claims and various public health databases, the analysis spans all fifty states and the District of Columbia. Findings indicate significant inefficiencies in rural regions, largely tied to limited broadband access, workforce shortages, and adverse social determinants of health. These results highlight the need for targeted policy interventions to improve both the equity and efficiency of telehealth services.

Keywords: Telehealth utilization; Efficiency; COVID-19; DEA; ML (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-025-00978-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:25:y:2025:i:4:d:10.1007_s12351-025-00978-2

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-025-00978-2

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().