Greece 2.0, Health Economics and Outcome Research and the Rise of Artificial Intelligence: Another Missed Opportunity or it's Time for Brilliance?


Published: Aug 31, 2022
Keywords:
Greece 2.0 Health Economics and Outcome Research Artificial Intelligence Healthcare Reform
Dimitrios Fylatos
Iris Panagiota Efthymiou
Symeon Sidiropoulos
Alkinoos Emmanouil-Kalos
Athanassios Vozikis
Abstract

The EU National Recovery and Resilience Plan "Greece 2.0" includes, among other priorities, a framework to promote and reform the health system, with a focus on digitalization of health and the use of information technology applications. Greece 2.0 may offer a chance to address the current scarcity of high-quality, reliable data sources, which is limiting the spread and impact of health economics and outcomes research (HEOR). We also suspect that the use of artificial intelligence (AI) in HEOR will play an important role in Greece's health-care reform and that it will be critical for making real-world data-driven decisions, reducing policy uncertainty. Greece has a once-in-a-lifetime chance to start from scratch and potentially build data-centric AI systems that prioritise data quality over quantity and are built on scalable, flexible, and governable data collection. This commentary explains and critically considers the significance of developing and funding an innovative plan for using AI in HEOR as part of the Greece 2.0 framework. It also discusses ethical issues and the larger role of HEOR in health-care reform.

Article Details
  • Section
  • Editorial
Downloads
Download data is not yet available.
References
Chen, Y., Guzauskas, G., Gu, C., Wang, B., Furnback, W., Xie, G., Dong, P.,Garrison, L. (2016). Precision Health Economics and Outcomes Research to support precision medicine: Big Data meets patient heterogeneity on the road to value. Journal of Personalized Medicine, 6(4), 20. https://doi.org/10.3390/jpm6040020
Dash, S., Shakyawar, S. K., Sharma, M., Kaushik, S. (2019). Big Data in Healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0
Economou, C., Kaitelidou, D., Karanikolos, M., & Maresso, A. (2017). Greece: Health System Review. Health systems in transition, 19(5), 1–166.
Efthymiou, I. P., Vozikis, A., Sidiropoulos, S. and Kritas, D. (2020a). AI and Big Data: A New Paradigm for Decision Making in Healthcare. HAPSc Policy Briefs Series, 1(2), 138-145. https://doi.org/10.12681/hapscpbs.26490
Efthymiou, I. P., Sidiropoulos, S., Kritas, D., Rapti, P., Vozikis, A., Souliotis, K. (2020b). AI transforming Healthcare Management during Covid-19 pandemic. HAPSc Policy Briefs Series, 1(1), 130 – 138. https://doi.org/10.12681/hapscpbs.24958
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial intelligence in healthcare (pp. 295-336). Academic Press.
Holtorf, A. P., Brixner, D., Bellows, B., Keskinaslan, A., Dye, J., & Oderda, G. (2012). Current and Future Use of HEOR Data in Healthcare Decision-Making in the United States and in Emerging Markets. American health & drug benefits, 5(7), 428–438.
Jin, H., and Kalavrezou, N. (2021). Health Care Reform in greece: Progress and reform priorities. IMF Working Papers, 2021(189), 1. https://doi.org/10.5089/9781513588834.001
Kentikelenis, A. and Papanicolas, I. (2011). Economic crisis, austerity and the Greek Public Health System. The European Journal of Public Health, 22(1), 4–5. https://doi.org/10.1093/eurpub/ckr190
Khosla, S., Tepie, M., Nagy, M., Kafatos, G., Seewald, M., Marchese, S. and Liwing, J., (2021). The Alignment of Real-World Evidence and Digital Health: Realising the Opportunity. Therapeutic Innovation and Regulatory Science, 55(4), 889–898. https://doi.org/10.1007/s43441-021-00288-7
Liang, W., Xie, J., Fu, H.,; Wu, E. Q. (2014). The role of Health Economics and Outcomes Research in health care reform in China. PharmacoEconomics, 32(3), 231–234. https://doi.org/10.1007/s40273-014-0141-2
Lu, Z., Xiong, X., Lee, T., Wu, J., Yuan, J. and Jiang, B. (2021). Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis. Frontiers in Pharmacology, 12.
Maddox, T. M., Rumsfeld, J. S.,; Payne, P. R. (2019). Questions for artificial intelligence in health care. JAMA, 321(1), 31. https://doi.org/10.1001/jama.2018.18932
Murtha, L. F., Jain, P., & Song, K. (2022, May 31). Ethical issues surrounding research of AI in health care. Online available at: https://www.reuters.com/legal/litigation/ethical-issues-surrounding-research-ai-health-care-2022-05-31/
NICE. (2022). Evidence standards framework (ESF) for digital health technologies. [online] NICE. Available at: <https://www.nice.org.uk/about/what-we-do/our-programmes/evidence-standards-framework-for-digital-health-technologies> [Accessed 10 September 2022].
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Padula, W., Kreif, N., Vanness, D., Adamson, B., Rueda, J., Felizzi, F., Jonsson, P., IJzerman, M., Butte, A. and Crown, W. (2022). Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. Value in Health, 25(7), 1063-1080.
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., Boccia, S. (2019). Benefits and challenges of Big Data in Healthcare: An overview of the European initiatives. European Journal of Public Health, 29(Supplement_3), 23–27. https://doi.org/10.1093/eurpub/ckz168
Patel, H., Guttula, S., Mittal, R. S., Manwani, N., Berti-Equille, L.,Manatkar, A. (2022). Advances in exploratory data analysis, visualisation and quality for data centric AI systems. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3542604
Petmesidou, M. (2019). Challenges to healthcare reform in crisis-hit Greece. e-Cadernos CES, 31. https://doi.org/10.4000/eces.4127
Rigby, J., M. (2019). Ethical Dimensions of Using Artificial Intelligence in Health Care. Online available at: https://journalofethics.ama-assn.org/article/ethical-dimensions-using-artificial-intelligence-health-care/2019-02
Short, E. (2018). It turns out Amazon’s AI hiring tool discriminated against women. Silicon Republic.
Singal, A., Higgins, P. and Waljee, A. (2014). A Primer on Effectiveness and Efficacy Trials. Clinical and Translational Gastroenterology, 5(1), p.e45.
Yu, K.-H., Beam, A. L., Kohane, I. S. (2018). Artificial Intelligence in Healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z
Zou, K. H., Li, J. Z., Imperato, J., Potkar, C. N., Sethi, N., Edwards, J., Ray, A. (2020). harnessing real-world data for regulatory use and applying innovative applications. Journal of Multidisciplinary Healthcare, 13, 671–679. https://doi.org/10.2147/jmdh.s262776