Instrumenting i2b2 for Improved Medication Research: Adding the Patient Voice

描述（由申请人提供）：哈佛合作伙伴国家生物计算中心的信号成功包括上市后监测研究，证明了卫生系统数据在上市后阶段检测与药物相关的不良事件的效用。NCBC现在在虚拟队列研究中扩展了这些方法，阐明了炎症途径并测量了比较有效性。这些研究的关键数据是（1）患者正在服用的药物的准确列表和（2）药物对生物学和健康的积极和消极影响的评估。我们建议构建和测试基础设施，以在桌面和移动的平台上捕获新的药物来源和患者输入，以改进药物列表和上市后监督和比较有效性研究的药物效应核算。公众可能认为，他们的医生和药剂师所使用的电子信息系统确保了完整的用药历史的一致性。在实践中，这种全面的用药历史要么不存在，要么不一致地访问和维护。尽管各医疗机构的医疗服务相当分散，但任何一家医疗机构的医疗清单都很可能不完整。在临床领域，将多个药物数据源合并为患者应接受的药物的单一全面明确列表的做法被称为“药物核对”。“我们实施软件和流程，以捕获药物协调的研究级版本。同时也使患者能够自我报告与药物相关的数据，包括开始和停止药物治疗的原因、疗效终点和不良反应。为此，我们创建了一个通用机制，让患者参与以患者为中心的研究，并为i2 b2贡献表型数据。我们在软件中实例化我们的解决方案，以提供一个通用的解决方案，使患者能够为i2 b2数据源做出贡献，并在高优先级的儿科慢性病人群中对其进行测试。我们使用NCBC计算工具作为基石扩展了一个全国范围的生物医学计算环境。该提案的目标有两个方面：1）开发新方法来捕获药物变量，以进行有效性比较和上市后研究;和2）定义在最低密集度之间的权衡（使用现有的EHR数据）（获取药房数据，让患者协调药物）以便研究人员使用我们的研究结果可以选择最适合特定研究目标和资源的方法。在两个真实的世界环境中， 患者队列，以评估临床有效性研究和上市后监测中关键变量的药房来源和患者核对的药物相关数据的附加值。

DESCRIPTION (provided by applicant): Signal successes of the Harvard Partners National Center for Biocomputing include post-market surveillance studies demonstrating the utility of health system data for detecting adverse events associated with medications in the post marketing phase. The NCBC now extends these approaches in virtual cohort studies elucidating inflammatory pathways and measuring comparative effectiveness. A critical class of data for these studies is (1) an accurate lists of medications a patient is taking and (2) an assessment of the impact-positive and negative-of the medications on biology and health. We propose to build and test infrastructure for capturing new medication sources and patient input across desktop and mobile platforms, to improve medication lists and accounting of medication effects for post-market surveillance and comparative effectiveness studies. The public may believe that the electronic information systems employed by their physicians and pharmacists ensure consistent access to complete medication histories. In practice such comprehensive medication histories either are not present or are not consistently accessed and maintained. This despite substantial fragmentation in care across sites-making the med list at any single institution very likely to be incomplete. In the clinical realm, the practice of merging several sources of medication data into a single comprehensive definitive list of medications the patient should be receiving is called "medication reconciliation." We implement the software and processes to capture a research grade version of medication reconciliation. And also capacitate patients to self-report medication-related data on reasons for starting and stopping medications, efficacy endpoints, and adverse effects. To do so, we create a general mechanism for patients to participate in patient-centered research and to contribute phenotype data to i2b2. We instantiate our solutions in software to provide a general solution enabling patient contribution to i2b2 data sources, and test them in high priority pediatric chronic disease populations. We extend a national-scale biomedical-computing environment using NCBC computational tools as foundation stones. The goal of this proposal is two-fold: 1) develop novel approaches to capture medication variables for comparative effectiveness and postmarket study; and 2) define the tradeoffs across the least intensive (using the existing EHR data) to the most (acquiring pharmacy data engaging patients to reconcile the medications) so that researchers using our findings may select he method most suited to the objectives and resources of a particular study. In two real world settings, we enroll patient cohorts to assess the value added by pharmacy- sourced, and patient reconciled medication-related data for key variables in clinical effectiveness research and postmarket surveillance.