JOURNAL ARTICLE
Healthcare costs vis-à-vis economic growth in pandemic crisis with technology adoption.
Published In: International Journal of Technology Management & Sustainable Development, 2023, v. 22, n. 1. P. 7 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Mandal, Purnendu 3 of 3
Abstract
The article examines the interplay between rising healthcare costs, technological advancements, and societal health in the United States during and after the COVID-19 pandemic. It highlights that despite significant adoption of medical and information technologies—such as electronic health records (EHR) mandated under the Affordable Care Act (ACA) and innovations termed Medical 4.0—national healthcare expenditures have continued to increase, partly driven by pandemic-related federal spending. The article also discusses the growing prevalence of metabolic syndrome as an indicator of deteriorating societal health, which may further strain healthcare resources. Using data from Texas as a case study, it notes widespread EHR adoption but finds mixed evidence on whether such technologies have effectively reduced costs or improved patient outcomes. The author emphasizes the need for a systems thinking approach to understand the complex relationships among healthcare costs, technology deployment, and population health, and calls for further research on the long-term impacts of technological interventions and pandemic-related changes in healthcare delivery.
Additional Information
- Source:International Journal of Technology Management & Sustainable Development. 2023/03, Vol. 22, Issue 1, p7
- Document Type:Article
- Subject Area:Economics
- Publication Date:2023
- ISSN:1474-2748
- DOI:10.1386/tmsd_00064_1
- Accession Number:164018610
- Copyright Statement:Copyright of International Journal of Technology Management & Sustainable Development is the property of Intellect Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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