ASSESSING THE IMPACT OF DATA SCIENCE ON DRUG MARKET ACCESS AND HEALTH ECONOMICS: A COMPREHENSIVE REVIEW
Keywords:
Data Science, Healthcare, Drug Market Access, Health Economics, Predictive Analytics, Real-World Evidence, Machine Learning, Personalized Medicine, Cost-Effectiveness, Data Privacy, Algorithmic Bias, Health PolicyAbstract
The application of data science in healthcare is revolutionizing drug market access and health economics by enabling more precise, data-driven decision-making. This paper provides a comprehensive review of the impact of data science on these fields, highlighting key advancements in predictive analytics, real-world evidence, and machine learning. Through these technologies, stakeholders can optimize market access strategies, improve cost-effectiveness analyses, and enhance personalized treatment approaches. Despite these benefits, the adoption of data science in healthcare faces significant challenges, including issues related to data quality, integration with traditional health economic models, and ethical concerns such as data privacy and algorithmic bias. This paper also explores future opportunities, emphasizing the importance of advancing data science techniques, integrating them with personalized medicine, and developing robust policy frameworks. The findings suggest that while obstacles remain, the continued evolution of data science in healthcare holds substantial promise for improving patient outcomes, increasing efficiency, and ensuring equitable healthcare delivery.
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Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
Garrison, L. P., Neumann, P. J., Erickson, P., Marshall, D., & Mullins, C. D. (2017). Using real-world evidence in economic evaluations: The ISPOR real-world data task force report. Value in Health, 20(7), 838-845.
Hughes, D., Teynor, M., O’Hanlon, M., & Kaufman, J. (2020). Real-world evidence in health technology assessment: The role of external evidence in assessment and appraisal. European Journal of Health Economics, 21(8), 1221-1234.
Kwon, Y., Sim, J., Lee, S., & Kim, D. (2019). Predicting drug sales using machine learning algorithms: An application to the South Korean pharmaceutical market. Healthcare, 7(2), 72.
Neumann, P. J., Sanders, G. D., Russell, L. B., Siegel, J. E., & Ganiats, T. G. (2018). Cost-effectiveness in health and medicine (2nd ed.). Oxford University Press.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216-1219.
Phillips, K. A., Trosman, J. R., Kelley, R. K., Pletcher, M. J., & Douglas, M. P. (2018). Genomic sequencing: Assessing the health technology assessment environment in the US and Europe. Journal of Personalized Medicine, 8(2), 33.
Towse, A., Garrison, L. P., & Puig-Peiró, R. (2019). The use of real-world evidence in the pricing and reimbursement of new medicines. Journal of Comparative Effectiveness Research, 8(6), 397-404.
Willke, R. J., Neumann, P. J., & Garrison, L. P. (2020). Review of recent US value frameworks—a health economics approach: An ISPOR Special Task Force report. Value in Health, 23(4), 451-456.
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Copyright (c) 2023 Rajesh Munirathnam (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
