Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation
Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation
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DOI：
10.1016/j.ijmedinf.2020.104092
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发表时间：
2020-04-01
影响因子：
4.9
通讯作者：
Lingman, Markus
中科院分区：
文献类型：
--
作者：
Galozy, Alexander;Nowaczyk, Slawomir;Lingman, Markus
背景和目的：患者的药物依从性是一个复杂的、多方面的现象。配药数据和电子健康记录用于通过续药依从性近似服药。很少深入讨论数据质量和计算差异的不利影响。这篇文章的目的是评估的影响时，使用电子健康records.Procedures计算药物依从性的常见陷阱：我们指出常见的陷阱与数据和操作的依从性措施。我们提供了可操作的定义，补充坚持和进行实验，以确定的效果的陷阱坚持估计。我们进行了统计显著性检验的影响，常见的陷阱使用基线方案作为reference.Findings：轻微的变化，定义可以显着倾斜重填坚持估计。收集模式造成计量与常用的覆盖天数比例之间的重大分歧。常见的数据相关的问题有一个小的，但统计学上显着（p < 0.05）的影响，人口水平和显着的效果对个别cases.Conclusion：在现实世界的管理数据库中遇到的数据相关的问题，影响各种业务定义的补充坚持不同，可以显着扭曲补充坚持值，导致错误的结论，坚持，特别是当估计坚持个人。
Background and purpose: Patients' adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals.