Digital Technologies in Health Care: A Comprehensive Review of Current Status and Future Perspectives
DOI:
https://doi.org/10.59890/ijasr.v2i3.1558Keywords:
Medical Education, Electronic Resources, Online Learning, Technological AdvancementsAbstract
The review paper focuses on the most important digital technologies that are used in health care, as well as the difficulties, advantages, and prospective prospects for the future that are linked with the inclusion of digital tools into clinical learning. Within the context of this rapidly evolving digital age, it is of the highest significance for students, medical educators, and training organizations to keep their understanding of the breadth, promise, difficulties, and limitations of digital technologies up to date. Digital health studies aim to realize the promise of digital technologies and understand their feasibility and effects. Through a critical examination of the most famous interdisciplinary digital health publications, this study argues that the digital health field has not really engaged with its main topic, technology. The intricacies of healthcare environments, including different technologies, existing procedures, and people, are ignored in research on digital technologies. The study findings show that health literature focuses on digital technology processing and its effects on digital health research approaches that emphasize technology and context. It claims that digital health's full potential requires multidisciplinary research on healthcare systems, informational demands, and digital technology.
References
Archibald, M. M., Ambagtsheer, R. C., Casey, M. G., & Lawless, M. (2019). Using Zoom videoconferencing for qualitative data collection: perceptions and experiences of researchers and participants. International journal of qualitative methods, p. 18, 1609406919874596.
Benchoufi, M., & Ravaud, P. (2017). Blockchain technology for improving clinical research quality. Trials, 18(1), 1-5.
Benis, A., Tamburis, O., Chronaki, C., & Moen, A. (2021). One digital health: a unified framework for future health ecosystems. Journal of Medical Internet Research, 23(2), e22189.
Bhavnani, S. P. (2020). Digital health: Opportunities and challenges to develop the next-generation technology-enabled models of cardiovascular care. Methodist DeBakey Cardiovascular Journal, 16(4), 296.
Cascini, F., Santaroni, F., Failla, G., Gentili, A., & Ricciardi, W. (2021). Developing a data-driven approach in order to improve the safety and quality of patient care. Frontiers in public health, 9, 667819.
Choi, T. M. (2020). Innovative “bring-service-near-your-home” operations under Corona-virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the messiah? Transportation Research Part E: Logistics and Transportation Review, p. 140, 101961.
Chung, E. Y., Palmer, S. C., Natale, P., Krishnan, A., Cooper, T. E., Saglimbene, V. M., ... & Strippoli, G. F. (2021). Incidence and outcomes of COVID-19 in people with CKD: a systematic review and meta-analysis. American Journal of Kidney Diseases, 78(6), 804–815.
Della Mea, V. (2001). What is e-Health (2): The death of telemedicine? Journal of medical Internet research, 3(2), e834.
Frehywot, S., Vovides, Y., Talib, Z., Mikhail, N., Ross, H., Wohltjen, H., ... & Scott, J. (2013). E-learning in medical education in resource-constrained low-and middle-income countries. Human resources for health, 11, 1-15.
Georgeff, M. (2014). Patients and technology: Digital technologies and chronic disease management. Australian Family Physician, 43(12), 842–846.
Gopal, G., Suter-Crazzolara, C., Toldo, L., & Eberhardt, W. (2019). Digital transformation in healthcare–architectures of present and future information technologies. Clinical Chemistry and Laboratory Medicine (CCLM), 57(3), 328-335.
Gupta, A., & Katarya, R. (2020). Social media based surveillance systems for healthcare using machine learning: a systematic review. Journal of biomedical informatics, 108, 103500.
Hund, A., Wagner, H. T., Beimborn, D., & Weitzel, T. (2021). Digital innovation: Review and novel perspective. The Journal of Strategic Information Systems, 30(4), 101695.
Igbonagwam, H. O., Dauda, M. A., Ibrahim, S. O., Umana, I. P., Richard, S. K., Enebeli, U. U., ... & Selowo, T. T. (2022). A review of digital tools for clinical learning. Journal of the Medical Women’s Association of Nigeria, 7(2), 29-35.
Iyawa, G. E., Herselman, M., & Botha, A. (2016). Digital health innovation ecosystems: From a systematic literature review to conceptual framework. Procedia Computer Science, 100, 244-252.
Iyengar, K., Upadhyaya, G. K., Vaishya, R., & Jain, V. (2020). COVID-19 and applications of smartphone technology in the current pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 733-737.
Jang, I. J. (2019). Artificial intelligence in drug development: clinical pharmacologist perspective. Translational and Clinical Pharmacology, 27(3), 87.
Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82.
Krishnan, A., Finkelstein, E. A., Levine, E., Foley, P., Askew, S., Steinberg, D., & Bennett, G. G. (2019). A digital behavioral weight gain prevention intervention in primary care practice: cost and cost-effectiveness analysis. Journal of Medical Internet Research, 21(5), e12201.
Lin, H., Li, R., Liu, Z., Chen, J., Yang, Y., Chen, H., ... & Liu, Y. (2019). Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine, 9, 52-59.
Mak, K. K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug discovery today, 24(3), 773-780.
Maramba, I., Chatterjee, A., & Newman, C. (2019). Methods of usability testing in the development of eHealth applications: a scoping review. International journal of medical informatics, 126, 95-104.
Marion, T. J., & Fixson, S. K. (2021). The transformation of the innovation process: How digital tools are changing work, collaboration, and organizations in new product development. Journal of Product Innovation Management, 38(1), 192-215.
Maslove, D. M., Klein, J., Brohman, K., & Martin, P. (2018). Using blockchain technology to manage clinical trials data: a proof-of-concept study. JMIR medical informatics, 6(4), e11949.
Muhammad, A. B., & Mukhtar, I. B. (2023). Artificial Intelligence in the Healthcare Sector. Global Journal of Research in Engineering & Computer Sciences, 3(5), 10–13.
Niebuhr, V., Niebuhr, B., Trumble, J., & Urbani, M. J. (2014). Online faculty development for creating E-learning materials. Education for health, 27(3), 255-261.
Noonan, D., & Simmons, L. A. (2021). Navigating nonessential research trials during COVID‐19: the push we needed for using digital technology to increase access for rural participants?. The Journal of Rural Health, 37(1), 185.
Osipenko, L. (2019). Blockchain’s potential to improve clinical trials—an essay by Leeza Osipenko. BMJ, 367.
Park, J. C., Kwon, H. J. E., & Chung, C. W. (2021). Innovative digital tools for new trends in teaching and assessment methods in medical and dental education. Journal of educational evaluation for health professions, p. 18.
Roggeveen LF, Fleuren LM, Guo T, Throall P, Jan de Grooth H, Swart EL, Klausch TLT, van der Voort PHJ, Girbes ARJ, Bosman RJ, & Elbers PWG (2019). Right dose right now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock—rationale and design of a multicenter randomized controlled superiority trial. Trials, 20:745. doi: 10.1186/s13063-019-3911-5
Santo, K., & Redfern, J. (2020). Digital health innovations to improve cardiovascular disease care. Current Atherosclerosis Reports, 22, 1-10.
Shaikh, F., Inayat, F., Awan, O., Santos, M. D., Choudhry, A. M., Waheed, A., ... & Tuli, S. (2017). Computer-assisted learning applications in health educational informatics: a review. Cureus, 9(8).
Stiles-Shields, C., Montague, E., Lattie, E. G., Kwasny, M. J., & Mohr, D. C. (2017). What might get in the way: barriers to the use of apps for depression. Digital Health, 3, 2055207617713827.
Taylor, K. I., Staunton, H., Lipsmeier, F., Nobbs, D., & Lindemann, M. (2020). Outcome measures based on digital health technology sensor data: data-and patient-centric approaches. NPJ digital medicine, 3(1), 97.
United States Food and Drug Administration (FDA). (2020). Digital Health Innovation Plan. Silver Spring, MD: U.S. Food and Drug Administration, 8p
University of Texas at Austin, Texas Advanced Computing Center. (April 22, 2020). AI fast-tracks drug discovery to fight COVID-19. EureakAlert! (American Association for the Advancement of Science).
Vyas, S., Gupta, M., & Yadav, R. (2019, February). Converging blockchain and machine learning for healthcare. In 2019 Amity international conference on artificial intelligence (AICAI) (pp. 709-711). IEEE.
Wijnberge, M., Geerts, B. F., Hol, L., Lemmers, N., Mulder, M. P., Berge, P., ... & Veelo, D. P. (2020). Effect of a machine learning–derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial. Jama, 323(11), 1052-1060.
Winstanley, E. L., Lander, L. R., Zheng, W., Law, K. B., Six-Workman, A., & Berry, J. H. (2021). Rapid transition of individual and group-based behavioral outpatient visits to telepsychiatry in response to COVID-19. Journal of Addiction Medicine, 15(3), 263–265.
Zoom Video Communications Inc. (2016). Security guide. Zoom Video Communications Inc. Retrieved from https://d24cgw3uvb9a9h.cloudfront.net/static/81625/doc/Zoom-Security-White-Paper.pdf
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