浏览全部资源
扫码关注微信
- Journal Information
- Archive
- Submission
- For Reviewers
- Statement and Policies
Research on dynamic monitoring of drug consumption based on statistical process control
- ZHONGGUO YAOFANG Vol. 35, Issue 19, Pages: 2328-2334(2024)
- 作者机构：
重庆市急救医疗中心药剂科，重庆 400014
- 作者简介：
- 基金信息：
DOI：10.6039/j.issn.1001-0408.2024.19.02
CLC： R952Published：15 October 2024，
Received：22 May 2024，
Revised：20 August 2024，
- 稿件说明：
移动端阅览
陈杨,淡重辉,徐美玲等.基于统计过程控制的药品用量动态监测研究 Δ[J].中国药房,2024,35(19):2328-2334.
CHEN Yang,DAN Chonghui,XU Meiling,et al.Research on dynamic monitoring of drug consumption based on statistical process control[J].ZHONGGUO YAOFANG,2024,35(19):2328-2334.
陈杨,淡重辉,徐美玲等.基于统计过程控制的药品用量动态监测研究 Δ[J].中国药房,2024,35(19):2328-2334. DOI： 10.6039/j.issn.1001-0408.2024.19.02.
CHEN Yang,DAN Chonghui,XU Meiling,et al.Research on dynamic monitoring of drug consumption based on statistical process control[J].ZHONGGUO YAOFANG,2024,35(19):2328-2334. DOI： 10.6039/j.issn.1001-0408.2024.19.02.
摘要
目的
2
探索基于统计过程控制（SPC）的药品用量动态监测方法，以提升药品使用过程的宏观监管水平。
方法
2
根据药品费用和国家相关文件建立我院重点监控药品品种目录。以全院、门诊药房和住院药房的重点监控药品品种月度用量数据为监测对象，利用SPC的X控制图、移动极差控制图和指数加权移动平均值控制图建立药品用量动态监测（DMDC）模型，分别从单月用量、极差变化以及用量趋势3个维度进行监测。以瑞舒伐他汀、美托洛尔和美罗培南为例，展示DMDC模型的监测效果。
结果
2
针对全院、门诊药房和住院药房分别建立了包含203、167和200个品种的重点监控药品目录。在排除无法建模及建模失败的品种后，成功为这3个药品消耗区域分别建立了179、116和172个DMDC模型。这3组模型在2024年前4个月的实践中，警示的药品品种数分别为54、32、62个。所建DMDC模型成功监测了全院瑞舒伐他汀、门诊药房美托洛尔以及住院药房美罗培南等药品的月度用量。相较于我院原用的浮动率排序法，DMDC模型的应用显著提升了药品监测的范围和深度，监测品种由原先的约50种大幅扩展至179种，监测维度也从1个维度增加到了3个维度。
结论
2
基于SPC的DMDC模型有效、可行，适用于对月度用量较稳定的药品品种进行监测。
Abstract
OBJECTIVE
2
To investigate a method for dynamic monitoring of drug consumption （DMDC） based on statistical process control （SPC）， aiming to improve the macro-supervisory capacity in the process of drug utilization.
METHODS
2
The lists of key monitoring drug varieties in our hospital were established based on drug cost and relevant national documents. Monthly consumption data of key monitoring drug varieties in the entire hospital， outpatient pharmacy and inpatient pharmacy were taken as monitoring objects，and the DMDC model was established using SPC’s X control chart， moving range control chart， and exponentially weighted moving-average control chart， monitoring from three dimensions： single-month consumption， range variation， and consumption trend. Rosuvastatin， metoprolol and meropenem were taken as examples to demonstrate the monitoring capabilities of the DMDC model.
RESULTS
2
Lists of key monitoring drug varieties were established for entire hospital， outpatient pharmacy and inpatient pharmacy， containing 203， 167 and 200 varieties， respectively. After excluding drug varieties that could not be modeled and for which modeling failed， 179， 116 and 172 DMDC models were successfully established for these three drug consumption areas， respectively. During the first four months of 2024， these three groups of model separately warned 54， 32 and 62 drug varieties. The DMDC model successfully monitored the monthly consumption of drugs，such as rosuvastatin throughout the hospital， metoprolol in outpatient pharmacy， and meropenem in inpatient pharmacy. Compared with the previously used floating rate ranking method in our hospital， the application of the DMDC model significantly improved the scope and depth of drug monitoring， with the monitored drug varieties greatly expanded from about 50 to 179， and the monitoring dimensions increased from a single dimension to three.
CONCLUSIONS
2
The DMDC model based on SPC is effective and feasible，suitable for monitoring drug varieties with stable monthly consumption.
关键词
药品用量药品动态监测统计过程控制
Keywords
drug dynamic monitoringstatistical process control
references
孔玲娟，赵颉，任晓辉. 我院临床用药动态监测和超常预警干预模式建设初探[J]. 中国卫生标准管理，2017，8（19）：21-22.
KONG L J，ZHAO J，REN X H. Construction of dynamic monitoring and supernormal early warning intervention mode for clinical drugs in our hospital[J]. China Health Stand Manag，2017，8（19）：21-22.
梁锦仪.浅析我院药品使用动态监测和超常预警干预体系的构建[J/OL].临床医药文献电子杂志，2018，5（93）：187，190[2024-04-12].https：//kns.cnki.net/kcms2/article/abstract?v=2arE9-pF_S0GfHIQ20YgoHIkENPVifMpUhHVfsGbDFAConlgd5omNQL1y0rM6K9J3I7Y8SQTJqxCenCflO_YGzU6qBTOq-PBkmQJZ5OL0NoIezZbK-3qr-ccnjJbX4iu28GkE4UGqFQbwt6jel1GtK0_Vs9WwvI5vCpia1INVnD_3CxyURtEQIQXO5DE8NRbq&uniplatform=NZKPT&language=CHS.DOI：10.16281/j.cnki.jocml.2018.93.159https://kns.cnki.net/kcms2/article/abstract?v=2arE9-pF_S0GfHIQ20YgoHIkENPVifMpUhHVfsGbDFAConlgd5omNQL1y0rM6K9J3I7Y8SQTJqxCenCflO_YGzU6qBTOq-PBkmQJZ5OL0NoIezZbK-3qr-ccnjJbX4iu28GkE4UGqFQbwt6jel1GtK0_Vs9WwvI5vCpia1INVnD_3CxyURtEQIQXO5DE8NRbq&uniplatform=NZKPT&language=CHS.DOI:10.16281/j.cnki.jocml.2018.93.159.
LIANG J Y. Analysis on the construction of dynamic monitoring and super early warning intervention system for drug use in our hospital[J/OL]. J Clin Med，2018，5（93）：187，190[2024-04-12]. https：//kns.cnki.net/kcms2/article/abstract?v=2arE9-pF_S0GfHIQ20YgoHIkENPVifMpUhHVfsGbDFAConlgd5omNQL1y0rM6K9J3I7Y8SQTJqx-https://kns.cnki.net/kcms2/article/abstract?v=2arE9-pF_S0GfHIQ20YgoHIkENPVifMpUhHVfsGbDFAConlgd5omNQL1y0rM6K9J3I7Y8SQTJqx-
CenCflO_YGzU6qBTOq-PBkmQJZ5OL0NoIezZbK-3qr-ccnjJbX4iu28GkE4UGqFQbwt6jel1GtK0_Vs9WwvI5vCpia1INVnD_3CxyURtEQIQXO5DE8NRbq&uniplatform=NZKPT&language=CHS.DOI：10.16281/j.cnki.jocml.2018.93.159http://dx.doi.org/10.16281/j.cnki.jocml.2018.93.159.
冯霞，叶丽卡，王若伦，等. 医院药品使用动态监测及超常预警模式研究[J]. 今日药学，2010，20（10）：40-42.
FENG X，YE L K，WANG R L，et al. The study on dynamic monitoring and extraordinary prewarning model for hospital drug utilization[J]. Pharm Today，2010，20（10）：40-42.
张洁璟，张延伟. 我院2010年临床使用药品动态监测结果分析[J]. 安徽医药，2012（3）：394-396.
ZHANG J J，ZHANG Y W. Dynamic monitoring for hospital drug use in a hospital of China in 2010[J]. Anhui Med Pharm J，2012（3）：394-396.
李肖玲，陈玮，沈金芳，等. 精细化管理在住院药房药品数量化管理中的应用[J]. 中国药房，2014，25（1）：36-39.
LI X L，CHEN W，SHEN J F，et al. Application of lean management in drug quantitative management in the inpatient pharmacy[J]. China Pharm，2014，25（1）：36-39.
ANHØJ J，WENTZEL-LARSEN T. Sense and sensibility：on the diagnostic value of control chart rules for detection of shifts in time series data[J]. BMC Med Res Methodol，2018，18（1）：100.
MONTGOMERY D C. Introduction to statistical quality control[M]. 7th edition. Hoboken：John Wiley & Sons，Inc.，2019：13-16，267-269，437-438.
NG J，ANDREW P，MUIR P，et al. Feasibility and re- liability of clinical coding surveillance for the routine monitoring of adverse drug events in New Zealand hospitals[J]. N Z Med J，2018，131（1484）：46-60.
IMAM N，SPELMAN T，JOHNSON S A，et al. Statistical process control charts for monitoring Staphylococcus aureus bloodstream infections in Australian health care facilities[J]. Qual Manag Health Care，2019，28（1）：39-44.
邓亚娟. 质量控制图在医院管理中的应用[J].中国卫生统计，2001，18（4）：254.
DENG Y J. Application of quality control chart in hospital management[J]. Chin J Health Stat，2001，18（4）：254.
FALTIN F W，KENETT R S，RUGGERI F. Statistical methods in healthcare[M]. Hoboken：John Wiley & Sons，Inc.，2012：253-265.
NELSON L S. Control charts for individual measurements[J]. J Qual Technol，1982，14（3）：172-173.
AMIN R W，ETHRIDGE R A. A note on individual and moving range control charts[J]. J Qual Technol，1998，30（1）：70-74.
ALBIN S L，KANG L，SHEA G. An X and EWMA chart for individual observations[J]. J Qual Technol，1997，29（1）：41-48.
0
Views
0
下载量
0
CSCD
- L-PDFDownload
- WORDDownload
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
- Address：8th Floor, Sihuan Building, No. 129 Daping Main Street, Yuzhong District, Chongqing, China Postal code：400042
- Technical support is provided by Beijing Founder Electronics co., LTD 渝ICP备15012710号
公网安备50010302001817 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.