AI and Big Data: A New Paradigm for Decision Making in Healthcare

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Published: Dec 29, 2020
DOI: https://doi.org/10.12681/hapscpbs.26490
Keywords:
AI decision making Big data healthcare HRM Human Resources Management Big Data Artificial Intelligence Medical Research healthcare organizations key performance indicators medical diagnostic analysis decisions Clinical Decision Support Systems
Iris - Panagiota Efthymiou
University of Piraeus; Hellenic Association of Political Scientists
https://orcid.org/0000-0001-9656-8378
Athanassios Vozikis
University of Piraeus
https://orcid.org/0000-0002-8342-3839
Symeon Sidiropoulos
Hellenic Association of Political Scientists; University of Piraeus
https://orcid.org/0000-0002-1548-2749
Dimitrios Kritas
Hellenic Association of Political Scientists; University of Crete
https://orcid.org/0000-0001-8678-6996
Abstract

The latest developments in artificial intelligence (AI)—a general-purpose technology impacting many industries — have been based on advancements in machine learning, which is recast as a quality-adjusted decline in forecasting ratio. The influence of Policy on AI and big data has impacted two key magnitudes which are known as diffusion and consequences. And these must be focused primarily on the context of AI and big data. First, in addition to the policies on subsidies and intellectual property (IP) that will affect the propagation of AI in ways close to their effect on other technologies, three policy categories — privacy, exchange, and liability — may have a specific impact on the diffusion of AI. The first step in the prohibition process is to identify the shortcomings of current hospital procedures, why we need acute care AI, and eventually how the direction of patient decision-making will shift with the introduction of AI-based research. The second step is to establish a plan to shift the direction of medical education in order to enable physicians to retain control of AI. Medical research would need to rely less on threshold decision-making and more on the prediction, interpretation, and pathophysiological context of contextual time cycles. This should be an early part of a medical student's education, and this is what their hospital aid (AI) ought to do. Effective contact between human and artificial intelligence includes a shared pattern of focused knowledge base. Human-to-human contact protection in hospitals should lead this professional transformation process.

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Author Biographies
Iris - Panagiota Efthymiou, University of Piraeus; Hellenic Association of Political Scientists

Iris - Panagiota Efthymiou is Board Member and President of the Interdisciplinary Committee, of the Hellenic Association of Political Scientists (HAPSc), Scientific Associate at the Laboratory of Health Economics and Management University of Piraeus, Board Member of Womanitee, HAPSc: Athens, Greece.

Athanassios Vozikis, University of Piraeus
Athanassios Vozikis is Associate Professor at the University of Piraeus and Director of the Laboratory of Health Economics and Management (LabHEM) of the University of Piraeus, Greece.
Symeon Sidiropoulos, Hellenic Association of Political Scientists; University of Piraeus

Symeon Sidiropoulos is Political Scientist, President of the Hellenic Association of Political Scientists (HAPSc), Scientific Associate at Laboratory of Health Economics and Management (LabHEM) of the University of Piraeus.

Dimitrios Kritas, Hellenic Association of Political Scientists; University of Crete

Dimitrios Kritas is PhD candidate, University of Crete. He is also Deputy President of the Hellenic Association of Political Scientists, Field Manager of the Public Policy and Administration Research Laboratory, Univeristy of Crete, Scientific Associate of the Laboratory of Health Economics and Management (LabHEM) of the University of Piraeus and Researcher of the Center for Political Research and Documentation (KEPET) of the University of Crete, Greece.

References
Arora, V., J. Johnson, D. Lovinger, H. J. Humphrey, and D. O. Meltzer. (2005). Communication Failures in Patient Sign-out and Suggestions for Improvement: A Critical Incident Analysis. Quality & Safety in Health Care, 14 (6): 401–407.
Artificial Intelligence Ai in Politics Should Political Ai Be Controlled” International Journal of Innovative Science and Research Technology (n.d). Available at: https://ijisrt.com/artificial-intelligence-ai-in-politics-should-political-ai-be-controlled. (Accessed: 19/09/2020).
Charani, E., Edwards, R., Sevdalis, N., Alexandrou, B., Sibley, E., Mullett, D., Franklin, B. D. and Holmes, A. (2011). Behavior Change Strategies to Influence Antimicrobial Prescribing in Acute Care: A Systematic Review. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 53 (7): 651–62.
Ching, T., Himmelstein, D., Beaulieu-Jones, B. et al. (2018). Opportunities and Obstacles for Deep Learning in Biology and Medicine. Journal of The Royal Society Interface, 15 (141): 1-47.
Efthymiou I. P., Efthymiou - Egleton Th. W., Sidiropoulos S. (2020). Artificial Intelligence (AI) in Politics: Should Political AI be Controlled?. International Journal of Innovative Science and Research Technology, 5(2): 49-51.
Efthymiou, I., Sidiropoulos, S., Kritas, D., Rapti, P., Vozikis, A. and Souliotis, K. (2020). AI transforming Healthcare Management during Covid-19 pandemic. HAPSc Policy Briefs Series, 1(1): 130-138.
El Aboudi, N., and Benhlima, L. (2018). Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation. Advances in Bioinformatics, 2018: 1-10.
Graham, K.L., Marcantonio, E.R., Huang, G.C. et al. (2013). Effect of a Systems Intervention on the Quality and Safety of Patient Handoffs in an Internal Medicine Residency Program. J GEN INTERN MED, 28(8): 986–993.
Grisso, T. and Appelbaum, P. (1998). Assessing Competence to Consent to Treatment: A Guide for Physicians and Other Health Professionals. New York: Oxford University Press.
Janz, N., Wren P., Copeland L. et al. (2004). Patient-Physician Concordance: Preferences, Perceptions, and Factors Influencing the Breast Cancer Surgical Decision. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 22 (15): 3091– 3098.
Kent, D. M, Steyerberg, E., van Klaveren, D. (2018). Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ (Clinical Research ed.), 363:k4245.
Lamanna, C., and Byrne, L. (2018). Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm. AMA Journal of Ethics, 20 (9): 902–910.
Lepping, P., Thushara S., and Turner, J. (2015). Systematic Review on the Prevalence of Lack of Capacity in Medical and Psychiatric Settings. Clinical Medicine, 15 (4): 337–43.
Lynn, L. A. (2019). Artificial Intelligence Systems for Complex Decision-Making in Acute Care Medicine: A Review. Patient Safety in Surgery, 13 (1): 1-8.
Lynn, L. A. and Curry, J. P. (2011). Patterns of Unexpected In-Hospital Deaths: A Root Cause Analysis. Patient Safety in Surgery, 5 (1): 3.
Lysaght, T., Yeefen, L. H., Xafis, V. and Ngiam, K. Y. (2019). AI-Assisted Decision-Making in Healthcare. Asian Bioethics Review, 11 (3): 299–314.
Marks, M. A. Z., and Hal, R. A. (2008). Patient and Surrogate Disagreement in End-of-Life Decisions: Can Surrogates Accurately Predict Patients’ Preferences?, Medical Decision Making: An International Journal of the Society for Medical Decision Making, 28 (4): 524–531.
Mercer, K., Li, M., Giangregorio, L., et. al. (2016). Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis. JMIR Mhealth Uhealth, 4: e40
Rawson, T. M., O’Hare, D., Herrero, P., et al. (2018). Delivering Precision Antimicrobial Therapy through Closed-Loop Control Systems. Journal of Antimicrobial Chemotherapy, 73 (4): 835 –843.
Rawson, M. T., Ahmad, R. Toumazou, C., Georgiou, P and Holmes, A. H. (2019). Artificial Intelligence Can Improve Decision-Making in Infection Management. Nature Human Behaviour, 3 (6): 543–545.
Sousa, M. J., Pesqueira A. M., et. al. (2019). Decision-Making Based on Big Data Analytics for People Management in Healthcare Organizations. Journal of Medical Systems, 43 (9): 290.
Wendler, D. and Rid. A. (2011). Systematic Review: The Effect on Surrogates of Making Treatment Decisions for Others. Annals of Internal Medicine, 154 (5): 336–346.
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