Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Am Heart J. 2018 Mar;197:153-162. doi: 10.1016/j.ahj.2017.09.019. Epub 2017 Dec 2.
Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims.
In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence.
Claims were highly predictive of patients in the worst adherence trajectory (C=0.78), but EHR data also provided good predictions (C=0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables.
EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available.
医疗保健提供者越来越被鼓励提高患者对慢性病药物的依从性。预测依从性可以识别需要干预的患者，但大多数预测工作都集中在索赔数据上，而提供者可能无法获得这些数据。电子健康记录 (EHR) 易于获取，并且可以提供比目前通过索赔获得的更丰富的信息来预测依从性。
在一个由医疗保险优势计划的完整索赔和多专业门诊实践的综合 EHR 链接的数据库中，我们确定了在 2011 年至 2012 年期间服用他汀类药物、抗高血压药或口服降糖药处方的患者。我们跟踪患者以确定随后的药物使用模式，并使用基于群组的轨迹模型将患者分配到依从性轨迹。然后，我们从索赔和 EHR 数据中识别潜在的预测因素，并拟合一系列模型来评估每个数据源预测药物依从性的准确性。
索赔数据高度预测了依从性最差的患者轨迹（C=0.78），但 EHR 数据也提供了良好的预测（C=0.72）。在索赔预测因素中，之前至少有 6 天的用药空白是迄今为止最具影响力的预测因素。相比之下，EHR 数据的良好预测需要复杂的模型和许多变量。
EHR 数据可以提供良好的依从性轨迹预测，因此对于寻求部署资源密集型干预措施的提供者可能是有用的。然而，来自索赔的数据中的先前依从性信息是最具预测性的，并且在可用时可以补充 EHR 数据。
