Economic evaluations of big data analytics for clinical decision-making
A scoping review
American Medical Informatics Association. Journal , Volume 27 - Issue 9 p. 1466- 1475
Objective: Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown
whether these investments have achieved the desired goals. We performed a scoping review to determine
the health and economic impact of big data analytics for clinical decision-making.
Materials and Methods: We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term.
Results: The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes.
Discussion: The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.
|big data, clinical decision-making, economics, data science, cost-effectiveness|
|American Medical Informatics Association. Journal|
|Organisation||Institute for Medical Technology Assessment (iMTA)|
Bakker, L.J, Aarts, J.E.C.M, Uyl-de Groot, C.A, & Redekop, W.K. (2020). Economic evaluations of big data analytics for clinical decision-making. American Medical Informatics Association. Journal, 27(9), 1466–1475. doi:10.1093/jamia/ocaa102