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Predicting drug shortages using pharmacy data and machine learning

Author

Listed:
  • Raman Pall

    (National Research Council of Canada)

  • Yvan Gauthier

    (National Research Council of Canada)

  • Sofia Auer

    (National Research Council of Canada)

  • Walid Mowaswes

    (PharmaGuide Inc)

Abstract

Drug shortages are a global and complex issue having negative impacts on patients, pharmacists, and the broader health care system. Using sales data from 22 Canadian pharmacies and historical drug shortage data, we built machine learning models predicting shortages for the majority of the drugs in the most-dispensed interchangeable groups in Canada. When breaking drug shortages into four classes (none, low, medium, high), we were able to correctly predict the shortage class with 69% accuracy and a kappa value of 0.44, one month in advance, without access to any inventory data from drug manufacturers and suppliers. We also predicted 59% of the shortages deemed to be most impactful (given the demand for the drugs and the potential lack of interchangeable options). The models consider many variables, including the average days of a drug supply per patient, the total days of a drug supply, previous shortages, and the hierarchy of drugs within different drug groups and therapeutic classes. Once in production, the models will allow pharmacists to optimize their orders and inventories, and ultimately reduce the impact of drug shortages on their patients and operations.

Suggested Citation

  • Raman Pall & Yvan Gauthier & Sofia Auer & Walid Mowaswes, 2023. "Predicting drug shortages using pharmacy data and machine learning," Health Care Management Science, Springer, vol. 26(3), pages 395-411, September.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:3:d:10.1007_s10729-022-09627-y
    DOI: 10.1007/s10729-022-09627-y
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    References listed on IDEAS

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    1. Mohsen Sadegh Amalnick & Naser Habibifar & Mahdi Hamid & Mahdi Bastan, 2020. "An intelligent algorithm for final product demand forecasting in pharmaceutical units," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 481-493, April.
    2. Sheldon Jacobson & Edward Sewell & Ruben Proano, 2006. "An analysis of the pediatric vaccine supply shortage problem," Health Care Management Science, Springer, vol. 9(4), pages 371-389, November.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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