The Impact of a Continuum of Care on Social Determinants of Health and Health Disparities
DOI:
https://doi.org/10.30953/thmt.v10.557Keywords:
AI in healthcare, Digital Health Equity Framework, global health, health equity, health technology, healthcare access, remote monitoring, sustainable development, telemedicineAbstract
In this article, the authors explore the transformative potential of digital health technologies to advance a Continuum of Care (CoC) model aimed at improving health outcomes and reducing disparities. It highlights how standards-based innovations—such as c (AI)-driven telemedicine, Internet of Things (IoT)-enabled remote monitoring, and mobile health—can deliver personalized, timely, and accessible care across diverse populations. Through real-world examples, including mobile clinics and population health platforms, the article illustrates successful implementations that address health inequities. In addition, the authors discuss the technical, economic, and governance challenges that clinicians face in integrating these solutions into routine care. Central to this work is the role of IEEE standards in ensuring equitable access, data interoperability, and built-in accessibility for all users. The paper advocates for a transdisciplinary and inclusive approach, empowering clinicians and technologists to collaborate in creating patient-centered systems that span the entire care journey—from prevention to treatment to recovery—regardless of setting or socioeconomic status.
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References
Abbasi K. A recommitment to continuity of care is central to reviving primary care. BMJ. 2024 Jun 20;q1350.
IEEE SA Transforming the Telehealth Paradigm IC Program, IEEE SA Transdisciplinary Framework for 5G and Future Networks Enabled Applications and Services Industry Connections. A Transdisciplinary Framework for Effective and Reliable Continuum of Connected Health Care [Internet]. Oral presented at: 2024 IEEE Baltimore Technical Colloquium; 2024 Nov 2 [cited 2025 Mar 16]; University of Maryland, Baltimore County (UMBC). Available from: https://site.ieee.org/baltimore/technical-colloquium-landing-page/
World Health Organization. Global strategy on digital health 2020-2025. 2021;
Wake E, Atkins H, Willock A, Hawkes A, Dawber J, Weir KA. Telehealth in trauma: A scoping review. J Telemed Telecare. 2022 Jul;28(6):412–22.
Groh M, Badri O, Daneshjou R, Koochek A, Harris C, Soenksen LR, et al. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nat Med. 2024 Feb;30(2):573–83.
Seah JCY, Tang CHM, Buchlak QD, Holt XG, Wardman JB, Aimoldin A, et al. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet Digital Health. 2021 Aug;3(8):e496–506.
Veluvali A, Dehghani Zahedani A, Hosseinian A, Aghaeepour N, McLaughlin T, Woodward M, et al. Impact of digital health interventions on glycemic control and weight management. npj Digit Med. 2025 Jan 9;8(1):20.
Kong J, Ha D, Lee J, Kim I, Park M, Im SH, et al. Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun. 2022 Jun 28;13(1):3703.
Kurniawan MH, Handiyani H, Nuraini T, Hariyati RTS, Sutrisno S. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Annals of Medicine. 2024 Dec 31;56(1):2302980.
Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019 Nov 14;381(20):1909–17.
Barrett PM, Komatireddy R, Haaser S, Topol S, Sheard J, Encinas J, et al. Comparison of 24-hour Holter Monitoring with 14-day Novel Adhesive Patch Electrocardiographic Monitoring. The American Journal of Medicine. 2014 Jan;127(1):95.e11-95.e17.
Chen B, Shi X, Feng T, Jiang S, Zhai Y, Ren M, et al. Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China. J Med Internet Res. 2024 Oct 24;26:e52404.
Xu Y, Khan TM, Song Y, Meijering E. Edge deep learning in computer vision and medical diagnostics: a comprehensive survey. Artif Intell Rev. 2025 Jan 17;58(3):93.
Mao W, Qiu X, Abbasi A. LLMs and Their Applications in Medical Artificial Intelligence. ACM Trans Manage Inf Syst. 2025 Jan 9;3711837.
Malone NC, Williams MM, Smith Fawzi MC, Bennet J, Hill C, Katz JN, et al. Mobile health clinics in the United States. Int J Equity Health. 2020 Dec;19(1):40.
Yu SWY, Hill C, Ricks ML, Bennet J, Oriol NE. The scope and impact of mobile health clinics in the United States: a literature review. Int J Equity Health. 2017 Dec;16(1):178.
Attipoe-Dorcoo S, Delgado R, Lai D, Gupta A, Linder S. Analysis of Annual Costs of Mobile Clinics in the Southern United States. J Prim Care Community Health. 2020 Jan;11:2150132720980623.
Bennet J, Oriol NE, Vavasis A. Mobile Health Map [Internet]. https://www.mobilehealthmap.org; 2007 [cited 2025 Feb 4]. Available from: https://www.mobilehealthmap.org/our-impact
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Copyright (c) 2025 Elizabeth White Baker, PhD, Katherine Grace (Kit) August, PhD, Narendra Mangra, Dimitrios Kalogeropoulos, PhD, Andres Mellik, MSc/MEng, Paula Muller, PhD , Thomas M. Willis, III, PhD , Raziq Yaqub, PhD , Victor B. Lawrence, Mathini Sellathurai, PhD , Michael Tremblay, PhD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to Telehealth and Medicine Today (THMT).
THMT is published under a Creative Commons Attribution-NonCommercial 4.0 International License.
