Kaiser Permanente Colorado, Institute for Health Research, Denver, Colorado 80237-8066, USA.
J Am Med Inform Assoc. 2011 Sep-Oct;18(5):717-20. doi: 10.1136/amiajnl-2011-000151. Epub 2011 May 25.
We developed an accurate and valid medication order algorithm to identify from electronic health records the definitive medication order intended for dispensing and applied this process to identify a cohort of patients and to stratify them into one of three medication adherence groups: early non-persistence, primary non-adherence, or ongoing adherence. We identified medication order data from electronic health record tables, obtained the orders, and linked the orders to dispensings. These steps were then used to identify patients newly prescribed antihypertensive, antidiabetic, or antihyperlipidemic medications and to determine the adherence group of each patient. Record review validated each process step, thus increasing the accuracy of group assignment as well as the criteria used to select patients. This work is an important first step to accurately identify study-specific patient adherence cohorts and allow more comprehensive estimates of population medication adherence.
我们开发了一种准确有效的药物医嘱算法,从电子健康记录中识别出用于配药的明确药物医嘱,并应用该过程来识别患者队列,并将他们分为三种药物依从性组之一:早期不持续、原发性不依从或持续依从。我们从电子健康记录表中识别药物医嘱数据,获取医嘱,并将医嘱与配药相关联。然后,这些步骤用于识别新处方抗高血压、抗糖尿病或抗高血脂药物的患者,并确定每个患者的依从性组。记录审查验证了每个过程步骤,从而提高了分组的准确性以及用于选择患者的标准。这项工作是准确识别研究特定患者依从性队列并允许更全面估计人群药物依从性的重要第一步。