A Review of the Challenges of Using Biomedical Big Data for Economic Evaluations of Precision Medicine

Abstract

There is potential value in incorporating biomedical big data (BBD)—observational real-world patient-level genomic and clinical data in multiple sub-populations—into economic evaluations of precision medicine. However, health economists face practical and methodological challenges when using BBD in this context. We conducted a literature review to identify and summarise these challenges. Relevant articles were identified in MEDLINE, EMBASE, EconLit, University of York Centre for Reviews and Dissemination and Cochrane Library from 2000 to 2018. Articles were included if they studied issues relevant to the interconnectedness of biomedical big data, precision medicine, and health economic evaluation. Nineteen articles were included in the review. Challenges identified related to data management, data quality and data analysis. The availability of large volumes of data from multiple sources, the need to conduct data linkages within an environment of opaque data access and sharing procedures, and other data management challenges are primarily practical and may not be long-term obstacles if procedures for data sharing and access are improved. However, the existence of missing data across linked datasets, the need to accommodate dynamic data, and other data quality and analysis challenges may require an evolution in economic evaluation methods. Health economists face challenges when using BBD in economic evaluations of technologies that facilitate precision medicine. Potential solutions to some of these challenges do, however, exist. Going forward, health economists who present work that uses BBD should document challenges and the solutions they have applied to the challenges to support future researcher endeavours.Electronic supplementary materialThe online version of this article (10.1007/s40258-019-00474-7) contains supplementary material, which is available to authorized users.