DIGITAL TOOLS FOR THE RESILIENCE OF THE HEALTHCARE SECTOR DURING THE COVID-19 PANDEMIC: OVERVIEW
DOI:
https://doi.org/10.56177/11icmie2023.53Keywords:
digital health, business resilience, COVID-19, digital tools, healthcare sectorAbstract
In times of crisis, organizations need to quickly adapt to unexpected disruptions and prevent the halt of their ongoing workflows. The COVID-19 pandemic had wracked havoc across economies worldwide, and the associated toll of lost lives, together with the incommensurable financial losses on the global markets will always be remembered as the defining grim consequences of this crisis that humanity faced. As a side effect, the pressure that it exerted over businesses worldwide has given rise to new ways on how digital tools and can be leveraged to combat intrinsic pandemic effects, such as limited mobility of people and goods, or various types of supply chain inconsistencies. As a result, in the context of medicine, the COVID-19 pandemic has accelerated the transition to the concept of “digital health” in health care systems worldwide. In this work we provide an overview of various digital tools and solutions reported in the frame of public and private health care sectors, which have been successfully used during the COVID-19 pandemic as solutions for the resilience of healthcare sectors worldwide.
References
Wu, Y., Xu, X., Chen, Z., Duan, J., Hashimoto, K., Yang, L., Liu, C., and Yang, C.: ‘Nervous system involvement after infection with COVID-19 and other coronaviruses’, Brain, Behavior, and Immunity, 2020
Guo, T., Fan, Y., Chen, M., Wu, X., Zhang, L., He, T., Wang, H., Wan, J., Wang, X., and Lu, Z.: ‘Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19)’, JAMA cardiology, 2020
Xu, L., Liu, J., Lu, M., Yang, D., and Zheng, X.: ‘Liver injury during highly pathogenic human coronavirus infections’, Liver International, 2020
Mantovani, A., Beatrice, G., and Dalbeni, A.: ‘Coronavirus disease 2019 (COVID‐19) and prevalence of chronic liver disease: A meta‐analysis’, Liver International, 2020
Gao, Q.Y., Chen, Y.X., and Fang, J.Y.: ‘2019 Novel coronavirus infection and gastrointestinal tract’, Journal of Digestive Diseases, 2020
Jin, X., Lian, J.-S., Hu, J.-H., Gao, J., Zheng, L., Zhang, Y.-M., Hao, S.-R., Jia, H.-Y., Cai, H., and Zhang, X.-L.: ‘Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms’, Gut, 2020
Frank, S.R.: ‘Digital health care—the convergence of health care and the Internet’, The Journal of ambulatory care management, 2000, 23, (2), pp. 8-17
Mathews, S.C., McShea, M.J., Hanley, C.L., Ravitz, A., Labrique, A.B., and Cohen, A.B.: ‘Digital health: a path to validation’, NPJ digital medicine, 2019, 2, (1), pp. 38
Perrin, P.B., Pierce, B.S., and Elliott, T.R.: ‘COVID‐19 and telemedicine: A revolution in healthcare delivery is at hand’, Health science reports, 2020, 3, (2)
Jnr, B.A.: ‘Use of telemedicine and virtual care for remote treatment in response to COVID-19 pandemic’, Journal of medical systems, 2020, 44, (7), pp. 132
Nittari, G., Savva, D., Tomassoni, D., Tayebati, S.K., and Amenta, F.: ‘Telemedicine in the COVID-19 era: a narrative review based on current evidence’, International Journal of Environmental Research and Public Health, 2022, 19, (9), pp. 5101
Grote, L., McNicholas, W.T., and Hedner, J.: ‘Sleep apnoea management in Europe during the COVID-19 pandemic: data from the European Sleep Apnoea Database (ESADA)’, European Respiratory Journal, 2020, 55, (6)
Bikov, A., Khalil, S., Gibbons, M., Bentley, A., Jones, D., and Bokhari, S.: ‘A fully remote diagnostic and treatment pathway in patients with obstructive sleep apnoea during the COVID-19 pandemic: a single centre experience’, Journal of Clinical Medicine, 2021, 10, (19), pp. 4310
WHO, W.: ‘cardiovascular diseases (CVDs)’, World Health Organization (WHO), 2017
Battineni, G., Sagaro, G.G., Chintalapudi, N., and Amenta, F.: ‘The benefits of telemedicine in personalized prevention of cardiovascular diseases (CVD): A systematic review’, Journal of Personalized Medicine, 2021, 11, (7), pp. 658
Kennel, P.J., Rosenblum, H., Axsom, K.M., Alishetti, S., Brener, M., Horn, E., Kirtane, A.J., Lin, E., Griffin, J.M., and Maurer, M.S.: ‘Remote cardiac monitoring in patients with heart failure: a review’, JAMA cardiology, 2022, 7, (5), pp. 556-564
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E.H., Marvel, F.A., Martin, S.S., Muse, E.D., Turakhia, M.P., and Tarakji, K.G.: ‘Smart wearable devices in cardiovascular care: where we are and how to move forward’, Nature Reviews Cardiology, 2021, 18, (8), pp. 581-599
Liu, H.H., Ezekowitz, M.D., Columbo, M., Khan, O., Martin, J., Spahr, J., Yaron, D., Cushinotto, L., and Kapelusznik, L.: ‘Testing the feasibility of operationalizing a prospective, randomized trial with remote cardiac safety EKG monitoring during a pandemic’, Journal of Interventional Cardiac Electrophysiology, 2022, 63, (2), pp. 345-356
Liu, J., Cao, R., Xu, M., Wang, X., Zhang, H., Hu, H., Li, Y., Hu, Z., Zhong, W., and Wang, M.: ‘Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro’, Cell discovery, 2020, 6, (1), pp. 16
Sicari, S., Rizzardi, A., and Coen-Porisini, A.: ‘Home quarantine patient monitoring in the era of COVID-19 disease’, Smart Health, 2022, 23, pp. 100222
Norgeot, B., Glicksberg, B.S., and Butte, A.J.: ‘A call for deep-learning healthcare’, Nature medicine, 2019, 25, (1), pp. 14-15
Wang, F., Casalino, L.P., and Khullar, D.: ‘Deep learning in medicine—promise, progress, and challenges’, JAMA internal medicine, 2019, 179, (3), pp. 293-294
Attia, Z.I., Kapa, S., Lopez-Jimenez, F., McKie, P.M., Ladewig, D.J., Satam, G., Pellikka, P.A., Enriquez-Sarano, M., Noseworthy, P.A., and Munger, T.M.: ‘Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram’, Nature medicine, 2019, 25, (1), pp. 70-74
Siontis, K.C., Noseworthy, P.A., Attia, Z.I., and Friedman, P.A.: ‘Artificial intelligence-enhanced electrocardiography in cardiovascular disease management’, Nature Reviews Cardiology, 2021, 18, (7), pp. 465-478
Wong, C.K., Ho, D.T.Y., Tam, A.R., Zhou, M., Lau, Y.M., Tang, M.O.Y., Tong, R.C.F., Rajput, K.S., Chen, G., and Chan, S.C.: ‘Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial’, BMJ open, 2020, 10, (7), pp. e038555
Khurana, A., Allawadhi, P., Khurana, I., Allwadhi, S., Weiskirchen, R., Banothu, A.K., Chhabra, D., Joshi, K., and Bharani, K.K.: ‘Role of nanotechnology behind the success of mRNA vaccines for COVID-19’, Nano Today, 2021, 38, pp. 101142
Huang, J., Wen, J., Zhou, M., Ni, S., Le, W., Chen, G., Wei, L., Zeng, Y., Qi, D., and Pan, M.: ‘On-site detection of SARS-CoV-2 antigen by deep learning-based surface-enhanced Raman spectroscopy and its biochemical foundations’, Analytical Chemistry, 2021, 93, (26), pp. 9174-9182
Kang, J., Yoo, Y.J., Park, J.-H., Ko, J.H., Kim, S., Stanciu, S.G., Stenmark, H.A., Lee, J., Mahmud, A.A., and Jeon, H.-G.: ‘Deepgt: Deep Learning-Based Quantification of Nanosized Bioparticles in Bright-Field Micrographs of Gires-Tournois Biosensor’, Nano Today, 2023, 52, 101968
Yoo, Y.J., Ko, J.H., Lee, G.J., Kang, J., Kim, M.S., Stanciu, S.G., Jeong, H.H., Kim, D.H., and Song, Y.M.: ‘Gires–Tournois Immunoassay Platform for Label‐Free Bright‐Field Imaging and Facile Quantification of Bioparticles’, Advanced Materials, 2022, 34, (21), pp. 2110003
Mbunge, E., Dzinamarira, T., Fashoto, S.G., and Batani, J.: ‘Emerging technologies and COVID-19 digital vaccination certificates and passports’, Public health in practice (Oxford, England), 2021, 2, pp. 100136
Sharun, K., Tiwari, R., Dhama, K., Rabaan, A.A., and Alhumaid, S.: ‘COVID-19 vaccination passport: prospects, scientific feasibility, and ethical concerns’, Human vaccines & immunotherapeutics, 2021, 17, (11), pp. 4108-4111
Nyawa, S., Tchuente, D., and Fosso-Wamba, S.: ‘COVID-19 vaccine hesitancy: a social media analysis using deep learning’, Annals of Operations Research, 2022, pp. 1-39
Alam, K.N., Khan, M.S., Dhruba, A.R., Khan, M.M., Al-Amri, J.F., Masud, M., and Rawashdeh, M.: ‘Deep learning-based sentiment analysis of COVID-19 vaccination responses from Twitter data’, Computational and Mathematical Methods in Medicine, 2021, 2021
Aygün, I., Kaya, B., and Kaya, M.: ‘Aspect based twitter sentiment analysis on vaccination and vaccine types in covid-19 pandemic with deep learning’, IEEE Journal of Biomedical and Health Informatics, 2021, 26, (5), pp. 2360-2369
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 International Conference of Management and Industrial Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.