Volume 11 , Issue 2 , PP: 17-26, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Abdelaziz 1 * , Alia N. Mahmoud 2
Doi: https://doi.org/10.54216/JCIM.110202
Effective management of patient data is critical for improving the quality of care and patient outcomes in healthcare systems. However, ensuring the confidentiality, integrity, and availability of patient data while complying with regulatory requirements can be challenging. To address this challenge, this work proposes an artificial intelligence (AI)-enabled framework that integrates information security (IS) and information management (IM) capabilities into a unified solution for improving the overall functionality of healthcare systems. The proposed framework leverages AI algorithms to automate managerial transactions of healthcare systems, while ensuring they are secure against possible threats. By automating these tasks, the framework can reduce the burden on healthcare staff, improve the accuracy and speed of information processing, and reduce the risk of human error. Our framework provides accurate and timely information to healthcare providers, enabling them to make informed decisions and provide better care to patients.
Information Management , Information Security , Healthcare system.
[1] Ifinedo, P., 2012. Understanding information systems security policy compliance: An integration of the theory of planned behavior and the protection motivation theory. Computers & Security, 31(1), pp.83-95.
[2] Brancheau, J.C. and Wetherbe, J.C., 1987. Key issues in information systems management. MIS quarterly, pp.23-45.
[3] Aparna, P. and Kishore, P.V.V., 2018. An efficient medical image watermarking technique in E-healthcare application using hybridization of compression and cryptography algorithm. Journal of Intelligent Systems, 27(1), pp.115-133.
[4] Simonet, D., 2015. Post-NPM reforms or administrative hybridization in the French health care system?. International Journal of Public Administration, 38(9), pp.672-681.
[5] McGivern, G., Currie, G., Ferlie, E., Fitzgerald, L. and Waring, J., 2015. HYBRID MANAGER–PROFESSIONALS'IDENTITY WORK: THE MAINTENANCE AND HYBRIDIZATION OF MEDICAL PROFESSIONALISM IN MANAGERIAL CONTEXTS. Public Administration, 93(2), pp.412-432.
[6] Alphonsa, A.M. and Mohanasundaram, N., 2020. Privacy preservation for the health care sector in a cloudenvironment by advanced hybridization mechanism. Control and Cybernetics, 49.
[7] Boudreau, M.C. and Robey, D., 2005. Enacting integrated information technology: A human agency perspective. Organization science, 16(1), pp.3-18.
[8] Suresh, A., Udendhran, R. and Balamurgan, M., 2020. Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Computing, 24(11), pp.7947-7953.
[9] Al-Shayea, T.K., Mavromoustakis, C.X., Batalla, J.M. and Mastorakis, G., 2019. A hybridized methodology of different wavelet transformations targeting medical images in IoT infrastructure. Measurement, 148, p.106813.
[10] Cottingham, M.D., 2019. The missing and needed male nurse: Discursive hybridization in professional nursing texts. Gender, Work & Organization, 26(2), pp.197-213.
[11] Niederman, F., Brancheau, J.C. and Wetherbe, J.C., 1991. Information systems management issues for the 1990s. MIS quarterly, pp.475-500.
[12] Vivas, F.J., De las Heras, A., Segura, F. and Andújar, J.M., 2017. H2RES2 simulator. A new solution for hydrogen hybridization with renewable energy sources-based systems. international journal of hydrogen energy, 42(19), pp.13510-13531.
[13] Rana, S.S., Rahman, M.T., Salauddin, M., Maharjan, P., Bhatta, T., Cho, H. and Park, J.Y., 2020. A humanmachine interactive hybridized biomechanical nanogenerator as a self-sustainable power source for multifunctional smart electronics applications. Nano Energy, 76, p.105025.
[14] Gruler, A., Armas Adrián, J.D., Juan, A.A. and Goldsman, D., 2019. Modelling human network behaviour using simulation and optimization tools: the need for hybridization. SORT: statistics and operations research transactions, 43(2), pp.0193-222.
[15] King, K.C., Stelkens, R.B., Webster, J.P., Smith, D.F. and Brockhurst, M.A., 2015. Hybridization in parasites: consequences for adaptive evolution, pathogenesis, and public health in a changing world. PLoS pathogens, 11(9), p.e1005098.
[16] Branitskiy, A. and Kotenko, I., 2017. Hybridization of computational intelligence methods for attack detection in computer networks. Journal of Computational Science, 23, pp.145-156.
[17] Kobayashi, Y., Peters, G.M., Ashbolt, N.J., Heimersson, S., Svanström, M. and Khan, S.J., 2015. Global and local health burden trade-off through the hybridisation of quantitative microbial risk assessment and life cycle assessment to aid water management. Water research, 79, pp.26-38.
[18] Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M.L. and Tarricone, L., 2015. An IoT -aware architecture for smart healthcare systems. IEEE internet of things journal, 2(6), pp.515-526.
[19] Grenier, C. and Bernardini-Perinciolo, J., 2016. Strategic and enforced logics hybridization: an agency view within french hospitals and universities. In Towards a comparative institutionalism: forms, dynamics and logics across the organizational fields of health care and higher education (Vol. 45, pp. 109-144). Emerald Group Publishing Limited.
[20] McGill, S., 2016. NGO Hybridisation as an Outcome of HIV Services Delivery in Global Fund-Supported Programmes in Ukraine. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27, pp.1853-1870.
[21] Hadeer Mahmoud , Ahmed Abdelhafeez, Spherical Fuzzy Multi-Criteria Decision-Making Approach for Risk Assessment of Natech, Neutrosophic and Information Fusion, Vol. 2 , No. 1 , (2023) : 59-68 (Doi : https://doi.org/10.54216/NIF.020105)
