University of Iowa College of Pharmacy, Iowa City, IA 52242, USA.
Clin Ther. 2013 Mar;35(3):344-50. doi: 10.1016/j.clinthera.2013.02.010. Epub 2013 Mar 1.
Using patient-reported data to supplement claims-based indicators may be helpful in identifying Medicare beneficiaries likely to benefit from medication therapy management (MTM) services.
Our objective was to develop and initially assess a patient medication user self-evaluation (MUSE) tool to identify Medicare Part D beneficiaries who would benefit from a comprehensive medication review.
A random sample of 225 patient medication profiles was created from a survey of Medicare beneficiaries; the survey also included demographic characteristics, responses to adherence questions, and reported symptoms. Three clinical pharmacists used the patient profiles to make judgments regarding the likelihood (low, moderate, or high) that each patient would benefit from an MTM visit in the next 3 months. A total of 150 cases were used for model calibration, and 75 were used for validation. Ordinal logistic regression models were fit to predict the likelihood of benefit from an MTM visit by using different combinations of potential MUSE items. Final model selection was based on the Akaike information criterion and the percent agreement between model prediction and expert judgments in the validation data. Measures considered for inclusion in the MUSE tool were related to medication use, medical conditions, and health care utilization.
The final MUSE items incorporated number of medications, number of physicians, number of pharmacies, number of hospitalizations in the past 6 months, having forgotten to take medications, cost-related problems, and number of medical conditions.
The 7-item MUSE tool could be used in targeting MTM services, such as comprehensive medication reviews, among Medicare beneficiaries.
利用患者报告数据来补充基于索赔的指标,可能有助于识别出可能从药物治疗管理(MTM)服务中受益的医疗保险受益人。
我们的目的是开发并初步评估一种患者用药自我评估(MUSE)工具,以识别可能从全面药物审查中受益的医疗保险 D 部分受益人。
从对医疗保险受益人的调查中创建了一个由 225 个患者用药档案组成的随机样本;该调查还包括人口统计学特征、对依从性问题的回答以及报告的症状。三位临床药师使用患者档案来判断每位患者在未来 3 个月内是否有可能从 MTM 访问中受益(低、中或高)。共有 150 个病例用于模型校准,75 个用于验证。使用不同的潜在 MUSE 项目组合,对有序逻辑回归模型进行拟合,以预测从 MTM 访问中受益的可能性。最终模型选择基于 Akaike 信息准则和验证数据中模型预测与专家判断之间的百分比一致性。考虑纳入 MUSE 工具的措施与药物使用、医疗状况和医疗保健利用有关。
最终的 MUSE 项目纳入了用药数量、医生数量、药房数量、过去 6 个月的住院次数、忘记服药、与费用相关的问题以及医疗状况数量。
7 项 MUSE 工具可用于针对 Medicare 受益人进行 MTM 服务(如全面药物审查)。