Automating the medication regimen complexity index
Automating the medication regimen complexity index
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DOI：
10.1136/amiajnl-2012-001272
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发表时间：
2013-05-01
影响因子：
6.4
通讯作者：
Feldman, Penny H.
中科院分区：
文献类型：
--
作者：
McDonald, Margaret V.;Peng, Timothy R.;Feldman, Penny H.
目的 在急性后期家庭护理环境下的商业药物数据库结构中调整药物治疗方案复杂性指数（MRCI）并使其自动化。
材料与方法 在第1阶段，从89645份电子健康记录中提取药物数据，使其与MRCI的组成部分（剂型、给药频率和其他用药说明）相一致。一个委员会审查输出结果以分配指数权重并确定必要的调整。在第2阶段，我们通过对自动列表和描述性统计的分析来检验改良后的MRCI的表面效度。
结果 每份患者病历的平均用药数量为7.6种（标准差3.8）；平均MRCI评分为16.1分（标准差9.0）。用药数量与MRCI高度相关，但每种用药数量对应的MRCI评分范围很广。大多数患者（55%）仅服用片剂/胶囊形式的口服药物，尽管16%的患者用药方案中有3种或更多不同途径/剂型的药物。对MRCI评分贡献最大的是给药频率（平均值为11.9）。超过36%的患者需要记住两条或更多特殊说明（例如，隔日服用、溶解）。
讨论 通过一个自动化流程并针对当地组织系统进行一些调整，可以对药物复杂性进行列表统计。与简单的药物计数相比，MRCI提供了一种更细致的测量和评估复杂性的方法。
结论 自动化的MRCI可能有助于识别发生不良事件风险较高的患者，并有可能用于研究和临床决策支持，以改善药物管理和患者预后。
Objective To adapt and automate the medication regimen complexity index (MRCI) within the structure of a commercial medication database in the post-acute home care setting.Materials and Methods In phase 1, medication data from 89 645 electronic health records were abstracted to line up with the components of the MRCI: dosage form, dosing frequency, and additional administrative directions. A committee reviewed output to assign index weights and determine necessary adaptations. In phase 2 we examined the face validity of the modified MRCI through analysis of automatic tabulations and descriptive statistics.Results The mean number of medications per patient record was 7.6 (SD 3.8); mean MRCI score was 16.1 (SD 9.0). The number of medications and MRCI were highly associated, but there was a wide range of MRCI scores for each number of medications. Most patients (55%) were taking only oral medications in tablet/capsule form, although 16% had regimens with three or more medications with different routes/forms. The biggest contributor to the MRCI score was dosing frequency (mean 11.9). Over 36% of patients needed to remember two or more special instructions (eg, take on alternate days, dissolve).Discussion Medication complexity can be tabulated through an automated process with some adaptation for local organizational systems. The MRCI provides a more nuanced way of measuring and assessing complexity than a simple medication count.Conclusions An automated MRCI may help to identify patients who are at higher risk of adverse events, and could potentially be used in research and clinical decision support to improve medication management and patient outcomes.