A Conceptual Framework for Leveraging Big Data and AI in Enhancing Healthcare Delivery and Public Health Policy

References

[1] ADDIN EN.REFLIST [1]O. S. Soyege et al., "Concept paper: Strategic healthcare administration and cost excellence for underserved communities (SHACE-UC)."

[2] [2]B. O. Tomoh, A. Y. Mustapha, A. O. Mbata, M. C. Kelvin-Agwu, A. Y. Forkuo, and T. O. Kolawole, "Assessing the impact of telehealth interventions on rural healthcare accessibility: a quantitative study."

[3] [3]C. P.-M. Ewim, H. E. Omokhoa, I. A. Ogundeji, and A. I. Ibeh, "Future of Work in Banking: Adapting Workforce Skills to Digital Transformation Challenges," Future, vol. 2, no. 1, 2021.

[4] [4]K. O. Ogunsola, E. D. Balogun, and A. S. Ogunmokun, "Enhancing Financial Integrity Through an Advanced Internal Audit Risk Assessment and Governance Model," 2021.

[5] [5]S. Gerke, T. Minssen, and G. Cohen, "Ethical and legal challenges of artificial intelligence-driven healthcare," in Artificial intelligence in healthcare: Elsevier, 2020, pp. 295-336.

[6] [6]L. Wang and C. A. Alexander, "Big data analytics in medical engineering and healthcare: methods, advances and challenges," Journal of medical engineering & technology, vol. 44, no. 6, pp. 267-283, 2020.

[7] [7]C. E. Alozie, O. O. Ajayi, J. I. Akerele, E. Kamau, and T. Myllynen, "Standardization in Cloud Services: Ensuring Compliance and Supportability through Site Reliability Engineering Practices."

[8] [8]C. E. Alozie, O. O. Ajayi, J. I. Akerele, E. Kamau, and T. Myllynen, "The Role of Automation in Site Reliability Engineering: Enhancing Efficiency and Reducing Downtime in Cloud Operations."

[9] [9]F. S. E Moura, K. Amin, and C. Ekwobi, "Artificial intelligence in the management and treatment of burns: a systematic review," Burns & trauma, vol. 9, p. tkab022, 2021.

[10] [10]N. J. Sam-Bulya, H. E. Omokhoa, C. P.-M. Ewim, and G. O. Achumie, "Developing a Framework for Artificial Intelligence-Driven Financial Inclusion in Emerging Markets."

[11] [11]O. S. Soyege et al., "Health informatics in developing countries: Challenges and opportunities."

[12] [12]M. C. Kelvin-Agwu, M. O. Adelodun, G. T. Igwama, and E. C. Anyanwu, "Enhancing Biomedical Engineering Education: Incorporating Practical Training in Equipment Installation and Maintenance."

[13] [13]N. L. Majebi, M. O. Adelodun, and E. Chinyere, "Community-Based Interventions to Prevent Child Abuse and Neglect: A Policy Perspective."

[14] [14]K. J. Olowe, N. L. Edoh, S. J. C. Zouo, and J. Olamijuwon, "Theoretical perspectives on biostatistics and its multifaceted applications in global health studies."

[15] [15]D. O. Olutimehin, T. O. Falaiye, C. P.-M. Ewim, and A. I. Ibeh, "Developing a Framework for Digital Transformation in Retail Banking Operations."

[16] [16]M. Subramanian et al., "Precision medicine in the era of artificial intelligence: implications in chronic disease management," Journal of translational medicine, vol. 18, pp. 1-12, 2020.

[17] [17]N. L. Edoh, V. M. Chigboh, S. J. C. Zouo, and J. Olamijuwon, "The role of data analytics in reducing healthcare disparities: A review of predictive models for health equity."

[18] [18]O. Famoti et al., "Agile Software Engineering Framework for Real-Time Personalization in Financial Applications."

[19] [19]Y. Xie et al., "Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare," Current medical science, vol. 41, no. 6, pp. 1123-1133, 2021.

[20] [20]Z. Ahmed, K. Mohamed, S. Zeeshan, and X. Dong, "Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine," Database, vol. 2020, p. baaa010, 2020.

[21] [21]A. Majeed and S. O. Hwang, "Data-driven analytics leveraging artificial intelligence in the era of COVID-19: an insightful review of recent developments," Symmetry, vol. 14, no. 1, p. 16, 2021.

[22] [22]J. S. Bedi, D. Vijay, P. Dhaka, J. P. S. Gill, and S. B. Barbuddhe, "Emergency preparedness for public health threats, surveillance, modelling & forecasting," Indian Journal of Medical Research, vol. 153, no. 3, pp. 287-298, 2021.

[23] [23]M. S. Reddy et al., "Predicting tomorrow’s Ailments: How AI/ML Is Transforming Disease Forecasting," ESP Journal of Engineering & Technology Advancements, vol. 1, no. 2, pp. 188-200, 2021.

[24] [24]J. Qadir, A. Ali, R. ur Rasool, A. Zwitter, A. Sathiaseelan, and J. Crowcroft, "Crisis analytics: big data-driven crisis response," Journal of International Humanitarian Action, vol. 1, pp. 1-21, 2016.

[25] [25]K. Ganasegeran, "Deep learning for disease prediction in public health," in Deep Learning for Biomedical Applications: CRC Press, 2021, pp. 157-180.

[26] [26]C. Garattini, J. Raffle, D. N. Aisyah, F. Sartain, and Z. Kozlakidis, "Big data analytics, infectious diseases and associated ethical impacts," Philosophy & technology, vol. 32, pp. 69-85, 2019.

[27] [27]L. Mählmann, M. Reumann, N. Evangelatos, and A. Brand, "Big data for public health policy-making: policy empowerment," Public health genomics, vol. 20, no. 6, pp. 312-320, 2018.

[28] [28]A. M. Shahat Osman and A. Elragal, "Smart cities and big data analytics: a data-driven decision-making use case," Smart Cities, vol. 4, no. 1, pp. 286-313, 2021.

[29] [29]G. H. Saunders et al., "Application of big data to support evidence-based public health policy decision-making for hearing," Ear and hearing, vol. 41, no. 5, pp. 1057-1063, 2020.

[30] [30]A. Tsoukalas, T. Albertson, and I. Tagkopoulos, "From data to optimal decision making: a data-driven, probabilistic machine learning approach to decision support for patients with sepsis," JMIR medical informatics, vol. 3, no. 1, p. e3445, 2015.

[31] [31]R. Reddy, "Predictive modeling with AI and ML for small business health plans: Improving employee health outcomes and reducing costs," Available at SSRN 5018069, 2020.

[32] [32]N. Aggarwal et al., "Advancing artificial intelligence in health settings outside the hospital and clinic," NAM perspectives, vol. 2020, p. 10.31478/202011f, 2020.

[33] [33]I. N. Cofone, "Algorithmic discrimination is an information problem," Hastings LJ, vol. 70, p. 1389, 2018.

[34] [34]B. Henderson, C. M. Flood, and T. Scassa, "Artificial Intelligence in Canadian Healthcare: Will the Law Protect Us from Algorithmic Bias Resulting in Discrimination?," Bradley Henderson, Colleen M. Flood, and Teresa Scassa,“Artificial Intelligence in Canadian Healthcare: Will the Law Protect Us from Algorithmic Bias Resulting in Discrimination, vol. 19, p. 2, 2021.

[35] [35]W. Guidance, "Ethics and governance of artificial intelligence for health," World Health Organization, 2021.

[36] [36]S. MULUKUNTLA, "Interoperability in Electronic Medical Records: Challenges and Solutions for Seamless Healthcare Delivery," EPH-International Journal of Medical and Health Science, vol. 1, no. 1, pp. 31-38, 2015.

[37] [37]V. Saini, S. G. Reddy, D. Kumar, and T. Ahmad, "Evaluating FHIR’s impact on Health Data Interoperability," IoT and Edge Comp. J, vol. 1, no. 1, pp. 28-63, 2021.

[38] [38]A. Kouroubali and D. G. Katehakis, "The new European interoperability framework as a facilitator of digital transformation for citizen empowerment," Journal of biomedical informatics, vol. 94, p. 103166, 2019.

[39] [39]W. J. Gordon and C. Catalini, "Blockchain technology for healthcare: facilitating the transition to patient-driven interoperability," Computational and structural biotechnology journal, vol. 16, pp. 224-230, 2018.

[40] [40]Y. Juhn and H. Liu, "Artificial intelligence approaches using natural language processing to advance EHR-based clinical research," Journal of Allergy and Clinical Immunology, vol. 145, no. 2, pp. 463-469, 2020.

[41] [41]S. Bayyapu, R. R. Turpu, and R. R. Vangala, "Advancing healthcare decision-making: The fusion of machine learning, predictive analytics, and cloud technology," International Journal of Computer Engineering and Technology (IJCET), vol. 10, no. 5, pp. 157-170, 2019.

[42] [42]M. Joe and E. Oye, "Exploring the Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Personalized Treatment Plans in Modern Healthcare Systems," 2018.

[43] [43]F. Griffin, "Artificial intelligence and liability in health care," Health Matrix, vol. 31, p. 65, 2021.

[44] [44]A. C. B. Monteiro, R. P. França, R. Arthur, and Y. Iano, "An overview of medical Internet of Things, artificial intelligence, and cloud computing employed in health care from a modern panorama," The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care, pp. 3-23, 2021.

[45] [45]J. T. Kelly, K. L. Campbell, E. Gong, and P. Scuffham, "The Internet of Things: Impact and implications for health care delivery," Journal of medical Internet research, vol. 22, no. 11, p. e20135, 2020.

[46] [46]S. Zeadally and O. Bello, "Harnessing the power of Internet of Things based connectivity to improve healthcare," Internet of Things, vol. 14, p. 100074, 2021.

[47] [47]N. N. Thilakarathne, M. K. Kagita, and T. R. Gadekallu, "The role of the internet of things in health care: a systematic and comprehensive study," International Journal of Engineering and Management Research (IJEMR), vol. 10, no. 4, pp. 145-159, 2020.

@article{1703054,
      author = {Ernest Chinonso Chianumba, Nura Ikhalea, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, Damilola Osamika},
      title = {A Conceptual Framework for Leveraging Big Data and AI in Enhancing Healthcare Delivery and Public Health Policy},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {6},
      pages = {303-311},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703054.pdf},
      abstract = {The integration of artificial intelligence and big data analytics has the potential to revolutionize healthcare delivery and public health policy. This paper presents a conceptual framework that explores the application of AI-driven predictive analytics in chronic disease management, epidemic forecasting, and healthcare cost optimization. Additionally, it examines how AI supports data-driven public health policymaking, enhances healthcare accessibility, and addresses ethical, legal, and social considerations. The study also discusses the interoperability challenges associated with integrating AI into healthcare ecosystems, focusing on the role of electronic health records, clinical decision support systems, and IoT-enabled remote patient monitoring. Despite the transformative benefits of these technologies, barriers to adoption?such as technical limitations, financial constraints, and regulatory concerns?pose significant challenges. Future research directions highlight the importance of explainable AI, federated learning, and AI-driven personalized medicine. The paper concludes with policy recommendations for standardizing data-sharing protocols, improving AI literacy among healthcare professionals, and ensuring ethical AI implementation to create a more efficient and equitable healthcare system.},
      keywords = {Artificial intelligence in healthcare, Big data analytics, Predictive healthcare modelling, Public health policy, Healthcare interoperability, AI ethics and regulation},
      month = {December},
  }