Implementation of supply management strategies by the pharmacy service in a general hospital during the COVID-19 pandemic

Objective: Given the global uncertainty faced due to the Covid-19 pandemic, health services were forced to adjust inventory management and purchase projections. This publication aims to describe the strategies taken and their impact on the supply chain indicators by the pharmacy service in the management of drug purchases during the pandemic to expose the importance of pharmacist in charge of the supply chain.Methods: This observational study describes the drug purchasing system in a general hospital and the strategies used to manage drug supply. The actions proposed by the pharmacy department are listed chronologically related to inventory issues and purchasing decisions. The accuracy of the purchase forecast was evaluated by calculating indicators such as the mean absolute standard deviation (MAD) and the variance of the forecast error (MSE). Inventory days and inventory turnover indicators were also compared in pre-pandemic and pre-pandemic periods.Findings: In general, the forecast error given by MAD and MSE tended to decrease. Specifically, from the 82 drug categories, during the pandemic period, this indicator decreased in 72 (88%), increased in 9 (11%), and remained the same in only 1 (1%) of the categories. In financial terms, comparing the 2018-2019 and 2020-2021 periods, a favorable result was obtained when evaluating the inventory turnover indicators, which decreased by 0.01 points and the days of inventory increased on average by two days. Conclusions: The implementation and use of these indicators prevented drug shortages, reducing inventory forecast errors. A pharmacist with knowledge in inventory management allows managing a process of continuous improvement and tactics for efficient inventory management without neglecting the benefit to patients or the economic profitability of the service. There were limitations since digital operating systems do not generate centralized or organized data for this type of analysis.