Recovering from a Pandemic? Analysis and Implications of Utilising Predictive Analytics for Improving UK Government Healthcare Financing

Jayne Arinze Egemonye

doi:10.18535/sshj.v9i02.1652

Abstract

The COVID-19 pandemic has exacerbated existing challenges in the UK healthcare system, including rising healthcare costs, inefficiencies in resource allocation, and the need for additional financing to support pandemic response efforts. This study explores the potential of predictive analytics, specifically machine learning models, to improve healthcare financing in the UK. Using time series data from 1980 to 2021, we employ three machine learning models—Linear Regression, Prophet, and Theta—to forecast healthcare expenditure as a percentage of GDP from 2023 to 2030. The results indicate a positive relationship between health financing and health outcomes, with healthcare expenditure in the UK expected to continue rising. The study highlights the effectiveness of predictive analytics in forecasting future healthcare financing levels and underscores the need for ongoing investment in healthcare infrastructure to ensure improved health outcomes for all citizens. The findings provide valuable insights for policymakers and stakeholders in the healthcare sector, both in the UK and internationally.

Keywords

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[refR-2] Pospiech, M. and Felden, C. (2013). A Descriptive Big Data Model Using Grounded Theory*. IEEE Xplore.Google Scholar ↗

[refR-3] Kumaranayake, L. and Walker, D. (2002). Cost-effectiveness analysis and priority-setting: global approach without local meaning*. Health policy in a globalising world.Google Scholar ↗

[refR-4] Nicola, M., O'Neill, N., Sohrabi, C., Khan, M., Agha, M., & Agha, R. (2020). Evidence based management guideline for the COVID-19 pandemic - Review article*. International Journal of Surgery.Google Scholar ↗

[refR-5] Wang, Q., Xu, R., & Volkow, N. D. (2021). Increased risk of COVID-19 infection and mortality in people with mental disorders: Analysis from electronic health records in the United States*. World Psychiatry.Google Scholar ↗

[refR-6] Witten, I.H., Frank, E. and Mark, A. (2016). Data Mining: Practical machine learning tools and techniques.Google Scholar ↗

[refR-7] OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition. OECD Publishing.Google Scholar ↗

[refR-8] Stoye, G. and Zaranko, B. (2019). UK health spending. IFS Report.Google Scholar ↗

[refR-9] Buck, D. and Dixon, A. (2013). Improving the allocation of health resources in England. London: Kings Fund.Google Scholar ↗

[refR-10] Anton, S.G. and Onofrei, M. (2012). Health Care Performance and Health Financing Systems in Countries from Central and Eastern Europe. Transylvanian Review of Administrative Sciences.Google Scholar ↗