Volume 24, Issue 3 (12-2022)                   yafte 2022, 24(3): 24-33 | Back to browse issues page


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Hayati H, Khalilifard J. Analysis of drug prescriptions and insurance deductions of the governmental pharmacy of Lorestan University of Medical Sciences. yafte 2022; 24 (3) :24-33
URL: http://yafte.lums.ac.ir/article-1-3530-en.html
Razi Herbal Medicines Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
Abstract:   (853 Views)
Background: Standards-based prescribing can increase the effectiveness of treatment and the optimal management of financial resources, including reducing the costs of insurance deductions. Therefore, reducing insurance deductions is necessary for pharmacy management. So, this study aimed to analysis the prescriptions and insurance deductions of the governmental pharmacy of Lorestan University of Medical Sciences.
Materials and Methods: This study is a retrospective cross-sectional study that includes a review of all prescriptions of the Social Security and the Armed Forces insurances. The information was collected through the checklist. Then, they were statistically analyzed by SPSS22 and excel software’s.
Results: The results showed that the highest number of prescriptions is related to general practitioners and the highest monetary amounts of the monthly deductions related to the Armed Forces Insurance is “price adjustment and low demand” (1700606 Rials) and the highest monetary amounts of the deductions in connection with the Social Security Insurance is “wrong code of the medical system (4617125 Rials).
Conclusion: Based on the results, it can be concluded that the most important causes of the deductions are related to human errors. Hence to reduce the deductions, first be more coordination’s between the insurances and pharmacies for covered items and the regulations second monitoring and up-to-date training of individuals on the correct entry of information based on the standards and regulations.

Full-Text [PDF 274 kb]   (464 Downloads)    
Type of Study: Research |
Received: 2022/06/27 | Accepted: 2022/12/6 | Published: 2022/12/31

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