Revolutionizing Healthcare: The Role of IoT in Remote Patient Monitoring and Telemedicine
DOI:
https://doi.org/10.31305/trjtm2024.v04.n02.005Keywords:
Internet of Things (IoT), Remote Patient Monitoring (RPM), Telemedicine, Healthcare Innovation, Predictive AnalyticsAbstract
The integration of Internet of Things (IoT) technology in remote patient monitoring (RPM) and telemedicine is transforming healthcare delivery by enabling real-time data collection, predictive analytics, and personalized care. This paper explores how IoT facilitates continuous health monitoring through wearable devices, smart sensors, and connected medical equipment, significantly improving chronic disease management and reducing hospital readmissions. It examines key applications, including virtual consultations and AI-driven diagnostics, while highlighting successful platforms like Teladoc Health and Philips eICU. However, the implementation of IoT in healthcare faces challenges, including interoperability issues, data security risks, regulatory compliance, and high adoption costs. Ethical concerns such as patient privacy and algorithmic bias further complicate widespread deployment. Addressing these barriers requires standardized protocols, robust cybersecurity measures, and collaborative efforts among policymakers, healthcare providers, and technology developers. The study concludes with a call to action for stakeholders to invest in IoT-driven solutions, enhance digital literacy, and develop inclusive frameworks to maximize the technology’s potential. By overcoming these challenges, IoT can revolutionize healthcare, making it more accessible, efficient, and patient-centric.
References
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Baker, S. B., Xiang, W., & Atkinson, I. (2017). Internet of Things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access, 5, 26521–26544. https://doi.org/10.1109/ACCESS.2017.2775180
Bestsennyy, O., Gilbert, G., Harris, A., & Rost, J. (2021). Telehealth: A quarter-trillion-dollar post-COVID-19 reality? McKinsey & Company. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality
Chaudhry, S. I., Mattera, J. A., Curtis, J. P., Spertus, J. A., Herrin, J., Lin, Z., ... & Krumholz, H. M. (2010). Telemonitoring in patients with heart failure. New England Journal of Medicine, 363(24), 2301–2309. https://doi.org/10.1056/NEJMoa1010029
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges. Future Generation Computer Systems, 78, 659–676. https://doi.org/10.1016/j.future.2017.04.036
Frost & Sullivan. (2020). IoT in healthcare: Growth opportunities and market trends. https://ww2.frost.com
Kvedar, J., Coye, M. J., & Everett, W. (2016). Connected health: A review of technologies and strategies to improve patient care with telemedicine and telehealth. Health Affairs, 33(2), 194–199. https://doi.org/10.1377/hlthaff.2013.0992
Bashshur, R., Shannon, G., Krupinski, E., & Grigsby, J. (2011). The taxonomy of telemedicine. Telemedicine journal and e-health : the official journal of the American Telemedicine Association, 17(6), 484–494. https://doi.org/10.1089/tmj.2011.0103
FDA. (2018). FDA allows marketing of first Apple Watch app to detect atrial fibrillation. U.S. Food and Drug Administration. https://www.fda.gov
Guk, K., Han, G., Lim, J., Jeong, K., Kang, T., Lim, E.-K., & Jung, J. (2019). Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare. Nanomaterials, 9(6), 813. https://doi.org/10.3390/nano9060813
Klonoff, D. C. (2020). Continuous glucose monitoring in diabetes. Diabetes Technology & Therapeutics, 22(S1), 1–10. https://doi.org/10.1089/dia.2020.2501
Topol, E. (2019). Deep medicine: How AI can make healthcare human again. Basic Books.
Fu-Chiang Tsui, Michael M. Wagner, Virginia Dato, Chung-Chou Ho Chang, Value of ICD-9-Coded Chief Complaints for Detection of Epidemics. Journal of the American Medical Informatics Association, 9(Supplement_6), S41–S47. https://doi.org/10.1197/jamia.M1224
Klonoff, D. C. (2020). Continuous glucose monitoring in diabetes. Diabetes Technology & Therapeutics, 22(S1), 1–10. https://doi.org/10.1089/dia.2020.2501
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The Rise of Consumer Health Wearables: Promises and Barriers. PLoS medicine, 13(2), e1001953. https://doi.org/10.1371/journal.pmed.1001953
Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., & Caffery, L. J. (2018). Telehealth for global emergencies: Lessons from COVID-19. Journal of Telemedicine and Telecare, 26(5), 309–313. https://doi.org/10.1177/1357633X20916567
Topol, E. (2019). Deep medicine: How AI can make healthcare human again. Basic Books.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164. https://doi.org/10.1016/j.comnet.2014.11.008
Downloads
Published
Issue
Section
Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /home/u495429466/domains/technoreview.co.in/public_html/plugins/generic/citations/CitationsPlugin.php on line 68
