The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features—the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets—that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.
An electronic bidding system promises to save resources in procurement procedures for many industries. While the results of previous studies in several countries sounded promising, it is concerning that more evidence would be needed to support changes in practice for the pharmaceutical sector. The objective of this study was to determine the prevalence of price saving in bidding-based electronic procurement setting and to clarify the main factors contributing to drug price changes. A comprehensive literature search was retrieved from five databases (Scopus, PubMed, ProQuest, Web of Science, Medline) to identify articles that studied the prices of medicines as a case study before and after the implementation of the electronic bidding system. Articles that were published in English from January 2012 to December 2021 were eligible for inclusion. The result showed that a total of 3214 records articles were identified in the electronic databases after the exclusion of duplicate articles. After the initial review, we found 13 studies that fulfilled our inclusion criteria. The review presented the important information suggesting that the use of the electronic bidding system likely results in a reduction in procurement prices of medicines. The prevalence of price saving for pharmaceutical procurement ranged from 7.24 % to 40 %. Additionally, the following factors were indirectly associated with drug price changes; bid volume, procurement location, contract characteristics, level of competitiveness and procurement organization. Further research may need to examine the functioning of e-bidding policies to address problems like supply disruptions to preserve the integrity of bidding-based pharmaceutical systems.
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