Digital Technologies Research Centre, National Research Council of Canada, 1200 Montreal Rd, Ottawa, K1A 0R6, ON, Canada.
PharmaGuide Inc, 55 West Beaver Creek Rd Unit 20, Richmond Hill, L4B 1K5, ON, Canada.
Health Care Manag Sci. 2023 Sep;26(3):395-411. doi: 10.1007/s10729-022-09627-y. Epub 2023 Mar 13.
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.
药品短缺是一个全球性且复杂的问题，对患者、药剂师和更广泛的医疗保健系统都产生了负面影响。我们使用了来自 22 家加拿大药店的销售数据和历史药品短缺数据，为加拿大大多数最常用可互换群组中的药物建立了预测短缺的机器学习模型。当将药品短缺分为四类（无、低、中、高）时，我们能够在一个月前准确预测短缺类别，准确率为 69%，kappa 值为 0.44，而无需访问药品制造商和供应商的任何库存数据。我们还预测了 59%的被认为最具影响力的短缺（考虑到对这些药物的需求以及潜在的缺乏可替代选择）。这些模型考虑了许多变量，包括每个患者的药物供应天数、药物供应总天数、以往的短缺情况以及不同药物组和治疗类别中药物的等级。一旦投入生产，这些模型将使药剂师能够优化他们的订单和库存，最终减少药品短缺对患者和运营的影响。
