Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Carey Business School, Johns Hopkins University, Baltimore, Maryland.
J Manag Care Spec Pharm. 2020 Jul;26(7):860-871. doi: 10.18553/jmcp.2020.26.7.860.
Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs.
To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims.
We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman's coefficient (SC) after adjusting for age and sex.
The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], < 0.001; 30-day PFR = 75.7 [23.6%], < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, < 0.001) or with 30-day PFR (SC = -0.17, < 0.001) at significant levels.
Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts.
No outside funding supported this study. The authors have nothing to disclose. The abstract of this work was presented at INFORMS Healthcare Conference, held on July 27-29, 2019, in Cambridge, MA.
不遵守药物治疗方案可能导致不良的医疗保健结果和增加成本。
（a）使用人群水平的电子健康记录（EHR）数据评估门诊环境下的药物复杂性水平，（b）评估其与从药物配药索赔中得出的药物依从性措施的相关性。
我们将 EHR 数据与 2012 年至 2013 年间在美国中西部医疗系统就诊的 70054 名患者的保险索赔进行了链接。我们构建了 3 个药物衍生指数：使用 EHR 数据的药物治疗方案复杂性指数（MRCI）；使用保险药房索赔的药物占有比（MPR）；以及使用两种数据源的处方填写率（PFR；7 天和 30 天）。我们在调整年龄和性别后，使用 Spearman 系数（SC）估计指数之间的部分相关性。
70054 名患者的平均年龄（SD）为 37.9（18.0）岁，平均 Charlson 合并症指数为 0.308（0.778）。2012 年的数据显示，MRCI、MPR 和 30 天 PFR 的平均值（SD）分别为 14.6（17.8）、0.624（0.310）和 81.0（27.0）。有住院史的患者可能具有较高的 MRCI 评分（36.3 [37.9]，< 0.001），并且对门诊处方的依从性较低（MPR = 50.3 [27.6%]，< 0.001；30 天 PFR = 75.7 [23.6%]，< 0.001）。然而，MRCI 与 MPR（SC = -0.31，< 0.001）或与 30 天 PFR（SC = -0.17，< 0.001）之间的相关性并不显著。
可以使用链接的 EHR 和索赔数据在人群水平上计算药物复杂性和依从性指数。方案的复杂性影响患者对门诊药物的依从性，人群之间的相关性强度略有不同。未来的研究应评估 MRCI、MPR 和 PFR 对人群健康管理工作的附加值。
本研究没有外部资金支持。作者没有什么可披露的。本工作的摘要在 2019 年 7 月 27 日至 29 日在马萨诸塞州剑桥举行的 INFORMS 医疗保健会议上进行了介绍。
