Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 114, Issue 43, Pages 11368-11373Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1714097114
Keywords
prediction; blood; supervised learning
Categories
Funding
- National Science Foundation [DMS-9971405]
- National Institutes of Health [N01-HV-28183]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1608987] Funding Source: National Science Foundation
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Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the similar to 2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save similar to 80 million dollars in health care costs.
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