• Call for Social Sciences Papers
  • Sign-up for PNAS eTOC Alerts

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system

  1. Tho D. Phamb,d,f,2
  1. aDepartment of Statistics, Stanford University, Stanford, CA 94305;
  2. bDepartment of Pathology, Stanford University, Stanford, CA 94305;
  3. cStanford Center for Clinical Informatics, Stanford University, Stanford, CA 94305;
  4. dStanford Hospital Transfusion Service, Stanford Medicine, Stanford, CA 94305;
  5. eDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305;
  6. fStanford Blood Center, Stanford Medicine, Stanford, CA 94305
  1. Contributed by Robert J. Tibshirani, August 10, 2017 (sent for review June 25, 2017; reviewed by James Burner, Pearl Toy, and Minh-Ha Tran)


In modern hospital systems where complicated, severely ill patient populations are the norm, there is currently no reliable way to forecast the use of perishable medical resources to enable a smart and economic way to deliver optimal patient care. We here demonstrate a statistical model using hospital patient data to quantitatively forecast, days in advance, the need for platelet transfusions. This approach can be leveraged to significantly decrease platelet wastage, and, if adopted nationwide, would save approximately 80 million dollars per year. We believe our approach can be generalized to all other aspects of patient care involving timely delivery of perishable medical resources.


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 ~2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ~80 million dollars in health care costs.


  • ?1L.G. and X.T. contributed equally to this work.

  • ?2To whom correspondence may be addressed. Email: tibs{at}stanford.edu or thopham{at}stanford.edu.
  • Author contributions: S.G., A.J.Z., and T.D.P. designed research; S.G., G.K., R.S., and B.N. performed research; L.G., X.T., R.J.T., and T.D.P. analyzed data; and L.G., X.T., R.J.T., and T.D.P. wrote the paper.

  • Reviewers: J.B., University of Texas Southwestern; P.T., University of California, San Francisco; and M.-H.T., University of California, Irvine.

  • The authors declare no conflict of interest.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1714097114/-/DCSupplemental.

This is an open access article distributed under the PNAS license.

Online Impact

    <var id="UPyyYwe"><span id="UPyyYwe"></span></var>
    <var id="UPyyYwe"><video id="UPyyYwe"></video></var>
    <ins id="UPyyYwe"><span id="UPyyYwe"><cite id="UPyyYwe"></cite></span></ins>
    <menuitem id="UPyyYwe"><video id="UPyyYwe"><thead id="UPyyYwe"></thead></video></menuitem><cite id="UPyyYwe"><video id="UPyyYwe"></video></cite><cite id="UPyyYwe"><video id="UPyyYwe"><var id="UPyyYwe"></var></video></cite>
    <ins id="UPyyYwe"><span id="UPyyYwe"><cite id="UPyyYwe"></cite></span></ins>
    <ins id="UPyyYwe"></ins> <ins id="UPyyYwe"><span id="UPyyYwe"><cite id="UPyyYwe"></cite></span></ins>
    <var id="UPyyYwe"></var>
    <var id="UPyyYwe"><span id="UPyyYwe"></span></var><cite id="UPyyYwe"></cite>
    <ins id="UPyyYwe"></ins>
    <cite id="UPyyYwe"><video id="UPyyYwe"></video></cite>
    <ins id="UPyyYwe"><span id="UPyyYwe"><var id="UPyyYwe"></var></span></ins>
    <cite id="UPyyYwe"></cite>
    <ins id="UPyyYwe"></ins>
    <ins id="UPyyYwe"></ins>
    <ins id="UPyyYwe"></ins><ins id="UPyyYwe"></ins><var id="UPyyYwe"><video id="UPyyYwe"></video></var>
    <cite id="UPyyYwe"><video id="UPyyYwe"><var id="UPyyYwe"></var></video></cite>
    <var id="UPyyYwe"><span id="UPyyYwe"><menuitem id="UPyyYwe"></menuitem></span></var><ins id="UPyyYwe"></ins>
    <var id="UPyyYwe"><video id="UPyyYwe"><menuitem id="UPyyYwe"></menuitem></video></var>
  • 1634281249 2018-02-17
  • 2115681248 2018-02-17
  • 8627591247 2018-02-17
  • 1184961246 2018-02-17
  • 9203941245 2018-02-17
  • 4504061244 2018-02-16
  • 5597191243 2018-02-16
  • 5234981242 2018-02-16
  • 6285841241 2018-02-16
  • 3913011240 2018-02-16
  • 5129741239 2018-02-16
  • 3595841238 2018-02-16
  • 3166311237 2018-02-16
  • 633831236 2018-02-16
  • 4424691235 2018-02-16
  • 4865101234 2018-02-16
  • 159241233 2018-02-16
  • 8626671232 2018-02-16
  • 315591231 2018-02-16
  • 5822951230 2018-02-16