Robust Postdonation Blood Screening Under Prevalence Rate Uncertainty

Hadi El-Amine (), Ebru K. Bish () and Douglas R. Bish ()
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Hadi El-Amine: Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030
Ebru K. Bish: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061
Douglas R. Bish: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061

Operations Research, 2018, vol. 66, issue 1, 1-17

Abstract: Blood products are essential components of any healthcare system, and their safety, in terms of being free of transfusion-transmittable infections, is crucial. While the Food and Drug Administration (FDA) in the United States requires all blood donations to be tested for certain infection types, it does not dictate which particular tests should be used by blood centers. Multiple FDA-licensed blood screening tests are available for each infection type, and screening tests are imperfectly reliable and have different costs. In addition, infection prevalence rates within the donor population are uncertain for both emerging and established infection types. In this setting, the budget-constrained blood center’s objective is to devise a “robust” postdonation bloodscreening scheme that minimizes the risk of an infectious donation being released into the blood supply. Toward this goal, we study the minimization of the transfusion-transmittable infection risk considering regret- and expectation-based objectives, and we characterize structural properties of their optimal solutions. This allows us to gain insight, derive the price of robustness, and develop efficient algorithms. The proposed robust solution lowers the expected infection risk over various FDA-compliant testing schemes as well as the expectation-based scheme under forecast error. These findings have important public policy implications.

Keywords: healthcare; blood screening and safety; risk minimization; robust testing schemes under uncertainty (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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