AI-POWERED HEALTH MONITORING: ENHANCING CHRONIC DISEASE MANAGEMENT

Main Article Content

Mr. Pranjal Bora
Abanibhusan Jena
Dr Asutosh Pramanik
Dr Sukanta Bandyopadhyay
Dr. Anil Hazarika

Keywords

Chronic disease management, AI-powered monitoring, predictive analytics, personalized healthcare, wearable devices, cost-effectiveness analysis.

Abstract

Diabetes, hypertension, and cardiovascular diseases are some of the common diseases that are prevalent in the world and exert a lot of pressure on the health systems. The conventional management techniques do not provide immediate and personalized attention. The purpose of this research is to analyze the impact of AI based health monitoring systems for enhancing chronic diseases. Smartwatches and mobile applications in combination with artificial intelligence tools enable users to monitor, evaluate, and provide interventions on a daily basis. The study employs both quantitative and qualitative research to assess the effectiveness of AI tools on patients’ outcomes in a RCT trial. The results indicate the improvement of the patients’ health outcomes including HbA1c, blood pressure, and medication adherence. The AI systems are more efficient and among all the models the CNN model has the highest accuracy and prediction. The cost breakdown demonstrates that while the setup cost of AI-based monitoring systems is relatively higher than the standard care, the annual operating cost is relatively lower and the QALYs per patient is also higher. There are limitations such as data privacy and technological implementation and thus it is suggested that subsequent research take them into consideration. The study finds that AI in health monitoring is capable of revolutionizing chronic disease management through efficiency, cost and patient centeredness.The cost breakdown demonstrates that while the setup cost of AI-based monitoring systems is relatively higher than the standard care, the annual operating cost is relatively lower and the QALYs per patient is also higher. There are limitations such as data privacy and technological implementation and thus it is suggested that subsequent research take them into consideration. The study finds that AI in health monitoring is capable of revolutionizing chronic disease management through efficiency, cost and patient centeredness.

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References

1. Mackey, T. K., & Liang, B. A. (2020). "Health data privacy and AI: How artificial intelligence could threaten health data privacy and what we can do about it." Health Policy and Technology, 9(3), 263-274. https://doi.org/10.1016/j.hlpt.2020.06.002
2. Obermeyer, Z., Powers, B., & Vogeli, C. (2019). "Dissecting racial bias in an algorithm used to manage the health of populations." Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
3. Rajkomar, A., Dean, J., & Kohane, I. (2019). "Machine learning in medicine." New England Journal of Medicine, 380, 1347-1358. https://doi.org/10.1056/NEJMra1814259
4. Saria, S., Subbaswamy, A., & Wang, D. (2020). "A framework for assessing the impact of algorithmic bias in health care." Journal of Biomedical Informatics, 108, 103514. https://doi.org/10.1016/j.jbi.2020.103514
5. Vos, T., Abajobir, A. A., & Abate, K. H. (2020). "Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016." The Lancet, 390(10100), 1211-1259. https://doi.org/10.1016/S0140-6736(17)32154-2
6. Albahri, A. S., Albahri, O. S., & Jaffar, M. A. (2021). "Wearable health devices—A comprehensive review of the state-of-the-art technologies and applications." Healthcare, 9(6), 749. https://doi.org/10.3390/healthcare9060749
7. Cresswell, K., Bates, D. W., & Sheikh, A. (2019). "Health information technology in the NHS: A review of the literature." Journal of Biomedical Informatics, 100, 103287. https://doi.org/10.1016/j.jbi.2019.103287
8. Gulshan, V., Peng, L., & Coram, M. (2016). "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." JAMA, 316(22), 2402-2410. https://doi.org/10.1001/jama.2016.17216
9. Obermeyer, Z., Powers, B., & Vogeli, C. (2019). "Dissecting racial bias in an algorithm used to manage the health of populations." Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
10. Rajkomar, A., Dean, J., & Kohane, I. (2019). "Machine learning in medicine." New England Journal of Medicine, 380, 1347-1358. https://doi.org/10.1056/NEJMra1814259
11. Saria, S., Subbaswamy, A., & Wang, D. (2020). "A framework for assessing the impact of algorithmic bias in health care." Journal of Biomedical Informatics, 108, 103514. https://doi.org/10.1016/j.jbi.2020.103514
12. Sustained Diabetes Care Consortium. (2021). "Innovative AI tools for diabetes management: Clinical outcomes and real-world applications." Diabetes Technology & Therapeutics, 23(7), 482-491. https://doi.org/10.1089/dia.2021.0043
13. Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. ISBN: 978-1541644632
14. World Health Organization. (2021). "Global report on diabetes." World Health Organization. https://www.who.int/publications/i/item/9789240062596
15. Creswell, J. W., & Creswell, J. D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
16. Deakin, T., McShane, C., Cade, J. E., & Williams, R. (2019). "Group-based training for self-management of diabetes: A systematic review and meta-analysis." Diabetes Care, 42(1), 35-43. https://doi.org/10.2337/dc18-1423
17. Fitzgerald, J. T., Davis, W. K., & Hess, G. E. (2017). "The Diabetes Self-Management Scale (DSMS): A reliable and valid measure of diabetes self-management." Diabetes Care, 40(7), 917-923. https://doi.org/10.2337/dc17-0154
18. Liu, S., Liu, X., & Yang, C. (2020). "Patient satisfaction with telemedicine services during the COVID-19 pandemic: A cross-sectional study." Telemedicine and e-Health, 26(6), 469-475. https://doi.org/10.1089/tmj.2020.0072
19. Rajkomar, A., Dean, J., & Kohane, I. (2019). "Machine learning in medicine." New England Journal of Medicine, 380, 1347-1358. https://doi.org/10.1056/NEJMra1814259

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