ASPIRE: Advancing Safety Process Innovation in Rheumatology

 描述（由申请人提供）：尽管美国医疗保健系统普遍存在患者安全问题，但开发可行的、可扩展的解决方案的研究已经滞后，特别是在门诊环境中。在使用高风险免疫抑制药物的非卧床患者的护理中，对提高安全性的新方法的需求尤其迫切，因为这类药物中可预防的不良事件的报告有所增加。到目前为止，还没有研究仔细定义这些安全性风险的流行病学。拟议的使用登记研究推进安全流程创新（ASPIRE）项目将使用先进的电子健康记录（EHR）数据分析来定义接受高风险药物的患者的动态患者安全错误和不良事件的程度（目标1）。我们还将描述这些风险在种族/民族，社会经济地位和医疗复杂性方面的差异。将检查三类患者安全性过程：1）减少药物不良事件的预防措施，2）使用高风险免疫抑制药物的患者的安全性监测，3）药物毒性监测异常结果的管理。这些发现将用于为eMeasures的开发提供信息，这是一种新的质量测量方法，使用自动提取的EHR信息为临床医生和卫生系统提供有关特定患者安全问题的信息（目标2）。我们将使用电子测量标准，包括质量数据模型（QDM），卫生质量测量格式（HQMF）和其他结构化编码术语，以开发，指定和测试系统范围的原型电子测量。HIT解决方案（如eMeasures）可以应用于EHR数据存储库，作为持续学习医疗保健系统的基础，正如医学研究所所设想的那样。本文提出的工作为进一步测试和在全国范围内传播eMeasures奠定了基础，以监测和改善在门诊环境中使用高风险药物的人的安全性。

 DESCRIPTION (provided by applicant): Despite widespread recognition of pervasive patient safety problems in the U.S. health care system, research to develop feasible, scalable solutions has lagged, particularly in the ambulatory setting. The need for new approaches to improve safety is especially pressing in the care of ambulatory patients using high-risk immunosuppressive medications, as reports of preventable adverse events among this category of drugs have increased. To date, no studies have carefully defined the epidemiology of these safety risks. The proposed Advancing Safety Process Innovation Using Registries (ASPIRE) project will use advanced electronic health record (EHR) data analytics to define the magnitude of ambulatory patient safety errors and adverse events for patients receiving high risk medications (Aim 1). We will also characterize disparities in these risks across race/ethnicity, socioeconomic status, and medical complexity. Three categories of patient safety processes will be examined: 1) preventive practices that reduce adverse medication events, 2) safety monitoring for patients using high-risk immunosuppressive medications, and 3) management of abnormal results from medication toxicity monitoring. These findings will be used to inform the development of eMeasures, a new approach to quality measurement that uses automatically extracted EHR information to give clinicians and health systems information regarding specific patient safety problems (Aim 2). We will use eMeasurement standards, including the Quality Data Model (QDM), Health Quality Measures Format (HQMF) and other structured coded terminologies to develop, specify and test system-wide prototype eMeasures. HIT-solutions such as eMeasures that can be applied across EHR data repositories serve as a foundation for a continuous learning health care system, as envisioned by the Institute of Medicine. The work proposed here builds the groundwork for further testing and nationwide dissemination of eMeasures to monitor and improve safety for those using high-risk medications in the ambulatory setting.