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Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility

The use of telehealth has increased significantly over the last decade and has become even more popular and essential during the COVID-19 pandemic due to social distancing requirements. Telehealth has many advantages including potentially improving access to healthcare in rural areas and achieving h...

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Autores principales: Shao, Yaxiong, Luo, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10068221/
https://www.ncbi.nlm.nih.gov/pubmed/37035639
http://dx.doi.org/10.1007/s43762-023-00092-z
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author Shao, Yaxiong
Luo, Wei
author_facet Shao, Yaxiong
Luo, Wei
author_sort Shao, Yaxiong
collection PubMed
description The use of telehealth has increased significantly over the last decade and has become even more popular and essential during the COVID-19 pandemic due to social distancing requirements. Telehealth has many advantages including potentially improving access to healthcare in rural areas and achieving healthcare equality. However, there is still limited research in the literature on how to accurately evaluate telehealth accessibility. Here we present the Enhanced Two-Step Virtual Catchment Area (E2SVCA) model, which replaces the binary broadband strength joint function of the previous Two-Step Virtual Catchment Area (2SVCA) with a step-wise function that more accurately reflects the requirements of telehealth video conferencing. We also examined different metrics for representing broadband speed at the Census Block level and compared the results of 2SVCA and E2VCA. Our study suggests that using the minimum available Internet speed in a Census Block can reveal the worst-case scenario of telehealth care accessibility. On the other hand, using the maximum of the most frequent available speeds reveals optimal accessibility, while the minimum of the most frequent reflects a more common case. All three indicators showed that the 2SVCA model generally overestimates accessibility results. The E2SVCA model addresses this limitation of the 2SVCA model, more accurately reflects reality, and more appropriately reveals low accessibility regions. This new method can help policymakers in making better decisions about healthcare resource allocations aiming to improve healthcare equality and patient outcomes.
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spelling pubmed-100682212023-04-03 Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility Shao, Yaxiong Luo, Wei Comput Urban Sci Methods Paper The use of telehealth has increased significantly over the last decade and has become even more popular and essential during the COVID-19 pandemic due to social distancing requirements. Telehealth has many advantages including potentially improving access to healthcare in rural areas and achieving healthcare equality. However, there is still limited research in the literature on how to accurately evaluate telehealth accessibility. Here we present the Enhanced Two-Step Virtual Catchment Area (E2SVCA) model, which replaces the binary broadband strength joint function of the previous Two-Step Virtual Catchment Area (2SVCA) with a step-wise function that more accurately reflects the requirements of telehealth video conferencing. We also examined different metrics for representing broadband speed at the Census Block level and compared the results of 2SVCA and E2VCA. Our study suggests that using the minimum available Internet speed in a Census Block can reveal the worst-case scenario of telehealth care accessibility. On the other hand, using the maximum of the most frequent available speeds reveals optimal accessibility, while the minimum of the most frequent reflects a more common case. All three indicators showed that the 2SVCA model generally overestimates accessibility results. The E2SVCA model addresses this limitation of the 2SVCA model, more accurately reflects reality, and more appropriately reveals low accessibility regions. This new method can help policymakers in making better decisions about healthcare resource allocations aiming to improve healthcare equality and patient outcomes. Springer Nature Singapore 2023-04-03 2023 /pmc/articles/PMC10068221/ /pubmed/37035639 http://dx.doi.org/10.1007/s43762-023-00092-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Methods Paper
Shao, Yaxiong
Luo, Wei
Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title_full Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title_fullStr Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title_full_unstemmed Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title_short Enhanced Two-Step Virtual Catchment Area (E2SVCA) model to measure telehealth accessibility
title_sort enhanced two-step virtual catchment area (e2svca) model to measure telehealth accessibility
topic Methods Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10068221/
https://www.ncbi.nlm.nih.gov/pubmed/37035639
http://dx.doi.org/10.1007/s43762-023-00092-z
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