International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Sat, Jul 27, 2024
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Volume 31, Issue 1 (IJIEPR 2020)
IJIEPR 2020, 31(1): 115-130 |
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kargari M, Sahranavard S. Managing Hospital medicine Costs in Healthcare Reform: Case Shari'ati Hospital. IJIEPR 2020; 31 (1) :115-130
URL: http://ijiepr.iust.ac.ir/article-1-894-en.html
URL: http://ijiepr.iust.ac.ir/article-1-894-en.html
1- Tarbiat Modares , m_kargari@modares.ac.ir
2- Tarbiat Modares
2- Tarbiat Modares
Abstract: (3062 Views)
Background: The continuous growth of healthcare and medicine costs as a strategic commodity requires tools to identify high cost populations and cost control. After the implementation of the healthcare Reform plan in Iran, a huge share of hospital funding has been spent on undesirable costs due to changes in the use of medicines and instruments.
Objective: The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.
Method: Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions information. Then using normalized risk function to identify abnormal and high cost cases based on the distance between the input data and the mean of the data. The data used are 15078 records, including information from patients' prescriptions from Shari'ati HIS in Tehran-Iran from 2012 to 2016.
Results: According to the obtained results, the proposed model has a positive Likehood ratio (LR+) of 6.35.
Objective: The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.
Method: Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions information. Then using normalized risk function to identify abnormal and high cost cases based on the distance between the input data and the mean of the data. The data used are 15078 records, including information from patients' prescriptions from Shari'ati HIS in Tehran-Iran from 2012 to 2016.
Results: According to the obtained results, the proposed model has a positive Likehood ratio (LR+) of 6.35.
Type of Study: Research |
Subject:
Decision Analysis and Methods
Received: 2019/04/27 | Accepted: 2019/12/30 | Published: 2020/03/15
Received: 2019/04/27 | Accepted: 2019/12/30 | Published: 2020/03/15
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