ASSESSING THE FORECASTING BY MSMS FOR MEDICAL STORES MANAGEMENT OF A TERTIARY CARE TEACHING HOSPITAL

Abstract

Medical Stores management is an important aspect in healthcare delivery to patients. Inventory in a medical store is one of the vital components of the current assets. Excess holdings of inventory may not only increase the cost but may also lead to wastage. Effective and efcient management of inventory is an integral part of supply chain. Improper use of pharmaceutical products or shortage of medicine not only cause nancial loss but may also affect the patients adversely. Contrary to the traditional techniques of managing inventory, Articial Intelligence (AI) is gaining credibility in making the process more effective and efcient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study aims to assess the effective implementation of the medical stores management system (MSMS) by correlating the forecasted Monthly Maintenance Figure (MMF) and the actual consumption pattern of the consumables used in the hospital in the 3 consecutive years using Root mean Square error (RMSE). RMSE was used as a measures for evaluating the quality of predictions. As RMSE is used to measure the deviation from the expected and actual values, the values closer to 0 are considered better. The RMSE was found to be 3.8 in 2020, 3.33 in 2021 and reduced to 1.33 in 2022 forecast with a correlation coefcient of 0.75(p-value= 0.001 x 10-20).