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利用配药数据评估社区药房的依从性实施率。

Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy.

作者信息

Torres-Robles Andrea, Wiecek Elyssa, Cutler Rachelle, Drake Barry, Benrimoj Shalom I, Fernandez-Llimos Fernando, Garcia-Cardenas Victoria

机构信息

Graduate School of Health, University of Technology Sydney, Sydney, NSW, Australia.

Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia.

出版信息

Front Pharmacol. 2019 Feb 26;10:130. doi: 10.3389/fphar.2019.00130. eCollection 2019.

Abstract

Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study was to analyze the changes on adherence implementation rates before and after a community pharmacist intervention integrated in usual real life practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Retrospective observational study. A de-identified database of dispensing data from 20,335 patients ( = 11,257 on rosuvastatin, = 6,797 on irbesartan, and = 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenlafaxine. In terms of the proportion of adherent patients (PDC >= 80.0%) the trend was similar, increasing after the pharmacist intervention from overall 17.4 to 41.2% and decreasing after one year of analysis to 35.3%. Big database analysis techniques provided results on adherence implementation over 2 years of analysis. An increase in adherence rates was observed after the pharmacist intervention, followed by a gradual decrease over time. Enhancing the current intervention using an evidence-based approach and integrating big database analysis techniques to a real-time measurement of adherence could help community pharmacies improve and sustain medication adherence.

摘要

药物治疗依从性仍然是医疗保健系统面临的一个重大问题,具有临床、人文和经济影响。由于电子健康数据易于获取,配药数据是一种有价值且常用的衡量指标。本研究的目的是分析在日常实际生活实践中纳入社区药剂师干预前后依从性实施率的变化,采用大数据分析技术从药房配药数据评估覆盖天数比例(PDC)。回顾性观察研究。分析了一个来自20335名患者(瑞舒伐他汀组11257名,厄贝沙坦组6797名,度洛西汀组2281名)的匿名配药数据库。纳入的患者接受了药剂师主导的药物治疗依从性干预,且在干预前后都有配药记录。作为依从性实施的衡量指标,采用了PDC。使用SQL和Python对数据库进行分析。药剂师干预三个月后,瑞舒伐他汀的平均PDC从50.2%(标准差:30.1)提高到66.9%(标准差:29.9),厄贝沙坦从50.8%(标准差:30.3)提高到68%(标准差:29.3),度洛西汀从47.3%(标准差:28.4)提高到66.3%(标准差:27.3)。这些比率在12个月内下降,瑞舒伐他汀降至62.1%(标准差:32.0),厄贝沙坦降至62.4%(标准差:32.5),度洛西汀降至58.1%(标准差:31.1)。就依从性患者比例(PDC≥80.0%)而言,趋势相似,药剂师干预后从总体17.4%增加到41.2%,分析一年后降至35.3%。大数据分析技术提供了两年分析期内依从性实施的结果。药剂师干预后观察到依从率上升,随后随时间逐渐下降。采用循证方法加强当前干预,并将大数据分析技术整合到依从性的实时测量中,有助于社区药房提高并维持药物治疗依从性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ba4/6399119/cc8b7390c7b7/fphar-10-00130-g001.jpg

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