Medication Adherence Monitoring at the Point of Care: Pediatric Asthma as a Test Case

项目摘要/摘要 最近的技术进步将药房配药数据近乎实时地带到了床边，以便 大量的执业提供者。随时访问患者的药房配药数据可能会 改变我们监测用药依从性的方式以及我们对这些信息的反应方式。因为这件事 技术相对较新，供应商还没有设计出系统的方法来整合这些数据 进入他们的临床实践。哮喘儿童是最有可能从积极主动中受益的人之一。 由于有效的控制者药物的可用性，对处方分配数据的监测， 从历史上看，遵守情况很差，而且代价高昂但可以预防的恶化。另外，哮喘药物 比率(AMR)(控制器药物填充的数量/(控制器填充的数量+救援填充的数量))是基于药房索赔的 哮喘风险预报器，可识别高危儿童的后续病情恶化。已使用AMR 作为护理质量指标，但尚未转化为床边风险预测工具，部分原因是 以前缺乏可用的、及时的药房配药数据。药房配药报告可通过 提交文件增加了AMR成功转化为床边风险的可能性 预测工具。这种积极主动的方法最终可以防止昂贵的急诊室就诊和住院治疗 哮喘。为了将风险预测器用作人口管理或床边风险预测工具，它 必须准确(基于准确的数据并具有优化的灵敏度和特异度)、方便(容易 在护理点向临床医生提供，可在大量儿童人口中衡量)，并及时 (使用尽可能最接近和最少的数据点来确定)。Surescript改进了 这一潜在有效的风险预测工具的便利性和及时性，但在它可以在 在床边，我们必须确定它的精度。首先，我们必须确定和比较AMR的精度 对照以前使用的风险预测工具。此外，我们还必须检查药房的准确性 通过将Surescript中可用的数据与付款人提供的索赔数据的黄金标准进行比较来分发数据。通过 尝试独立验证床边配药数据的准确性和完整性并确定 如果AMR是可用的最准确的哮喘风险预测指标，我们将迈出重要的一步 开发一种系统的方法，使用药房配药数据来改善患者护理。

Project Summary/Abstract Recent technological advances have brought pharmacy dispensing data to the bedside in near real time for a large number of practicing providers. Ready access to patients' pharmacy dispensing data has the potential to transform the way we monitor medication adherence and how we react to this information. Because this technology is relatively new, providers have yet to design systematic approaches to incorporating these data into their clinical practice. Children with asthma are among those most likely to benefit from proactive monitoring of prescription dispensing data because of the availability of effective controller medications, historically poor adherence, and costly yet preventable exacerbations. Additionally, the asthma medication ratio (AMR) (# of controller medication fills/(# of controller fills +# of rescue fills)) is a pharmacy claims based asthma risk predictor that identifies children at high risk for subsequent exacerbation. The AMR has been used as a quality of care metric but has not been translated into a bedside risk prediction tool, owing in part to the previous lack of available, timely pharmacy dispensing data. Pharmacy dispensing reports available through Surescripts have increased the likelihood that the AMR could be successfully translated into a bedside risk prediction tool. This proactive approach could ultimately prevent costly ED visits and hospitalizations for asthma. In order for a risk predictor to be utilized as a population management or bedside risk prediction tool, it must be precise (based on accurate data and with optimized sensitivity and specificity), convenient (readily available to clinicians at the point of care, measureable on large populations of children), and timely (determined using the most proximal and fewest number of data points possible). Surescripts has improved the convenience and timeliness of this potentially effective risk prediction tool but before it can be used at the bedside we must determine its precision. First we must determine and compare the precision of the AMR against previously utilized risk prediction tools. Additionally, we must examine the accuracy of the pharmacy dispensing data available in Surescripts by comparing it to the gold standard of payer supplied claims data. By attempting to independently verify the accuracy and completeness of bedside dispensing data and determining if the AMR is the most accurate asthma risk predictor available we will take an important step towards developing a systematic approach for using pharmacy dispensing data to improve patient care.