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Optimization of the clinical drug list of DRG based on data mining technology
- ZHONGGUO YAOFANG Vol. 35, Issue 13, Pages: 1558-1563(2024)
- 作者机构：
1.常州市第一人民医院药学部，江苏 常州 213003
2.常州市第一人民医院医保办公室，江苏 常州 213003
3.常州市第一人民医院神经内科，江苏 常州 213003
- 作者简介：
- 基金信息：
DOI：10.6039/j.issn.1001-0408.2024.13.03
CLC： R95Published：15 July 2024，
Received：24 October 2023，
Revised：19 June 2024，
- 稿件说明：
扫 描 看 全 文
恽琴素,周伟贤,徐卉等.基于数据挖掘技术优化DRG临床用药目录 Δ[J].中国药房,2024,35(13):1558-1563.
YUN Qinsu,ZHOU Weixian,XU Hui,et al.Optimization of the clinical drug list of DRG based on data mining technology[J].ZHONGGUO YAOFANG,2024,35(13):1558-1563.
恽琴素,周伟贤,徐卉等.基于数据挖掘技术优化DRG临床用药目录 Δ[J].中国药房,2024,35(13):1558-1563. DOI： 10.6039/j.issn.1001-0408.2024.13.03.
YUN Qinsu,ZHOU Weixian,XU Hui,et al.Optimization of the clinical drug list of DRG based on data mining technology[J].ZHONGGUO YAOFANG,2024,35(13):1558-1563. DOI： 10.6039/j.issn.1001-0408.2024.13.03.
摘要
目的
2
优化疾病诊断相关分组（DRG）的临床用药目录，减少患者药品费用，提高DRG结付率。
方法
2
选取某院神经内科BR23疾病组作为研究对象，应用数据挖掘技术，探索疾病组的用药规律，并利用药品综合评价方法对重点监测药品进行评分，进而优化疾病组的临床用药目录。选取2022年12月入组该疾病组患者的住院信息作为优化前数据，2023年9月入组该疾病组患者的住院信息作为优化后数据，通过比较两组患者的医疗质量及药品费用数据来评价优化目录的实施效果。
结果
2
优化临床用药目录后，该疾病组的结付率由优化前的84.36%上升至104.70%，住院药品费用及住院总费用均显著降低（
P
＜0.05），重点监测药品使用量明显下降。
结论
2
数据挖掘技术有助于探索疾病组临床用药规律；药师可以此为依据，通过有效药学干预手段提高DRG结付率。
Abstract
OBJECTIVE
2
To optimize the clinical drug list of diagnosis-related group （DRG）， reduce the drug cost of patients， and increase the DRG settlement rate.
METHODS
2
By selecting BR23 disease group in the department of neurology of a hospital as the research object， data mining technology was used to explore the medication rule of the disease group， and the key monitored drugs were scored by comprehensive evaluation of drugs， thus optimizing the clinical drug list of disease groups. The hospitalization information of patients enrolled in the disease group in December 2022 was selected as the pre-optimization data， and the hospitalization information of patients enrolled in the disease group in September 2023 was selected as the post-optimization data. The implementation effect of the optimized list was evaluated by comparing the medical quality and drug cost data between the two groups.
RESULTS
2
After optimizing the clinical drug list， the settlement rate of this disease group increased from 84.36% before optimization to 104.70%； there was significant reduction in hospitalization drug cost and total hospitalization cost （
P
＜0.05）； the consumption of key monitored drugs significantly decreased.
CONCLUSIONS
2
Data mining technology helps explore the clinical medication rules of disease groups， which can be used by pharmacists to improve the settlement rate of DRG through effective pharmaceutical intervention.
关键词
疾病诊断相关分组数据挖掘技术临床用药目录重点监测药品药品综合评价
Keywords
data mining technologyclinical drug listkey monitored drugsdrug comprehensive evaluation
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