Instrumenting i2b2 for Improved Medication Research: Adding the Patient Voice

描述（由申请人提供）：哈佛伙伴国家生物计算中心的成功标志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。现在，NCBC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相对有效性。这些研究的关键数据类别是：(1)患者正在服用的药物的准确清单；(2)评估药物对生物学和健康的影响（积极和消极）。我们建议建立和测试用于跨桌面和移动平台捕获新药物来源和患者输入的基础设施，以改进药物清单和药物效果核算，用于上市后监测和比较有效性研究。公众可能认为，他们的医生和药剂师使用的电子信息系统可以确保始终如一地获得完整的用药史。在实践中，这样的综合用药史要么不存在，要么不一致地访问和维护。尽管各个地点的护理存在很大的碎片化，使得任何一个机构的医疗清单都很可能是不完整的。在临床领域，将几个来源的药物数据合并成一个单一的、全面的、确定的患者应该接受的药物清单的做法被称为“药物调和”。我们实现了软件和流程来捕获一个研究级的药物调节版本。也使患者能够自我报告药物相关数据，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i2b2提供表型数据。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，使患者能够对i2b2数据源做出贡献，并在高优先级儿科慢性病人群中进行测试。我们扩展了一个国家规模的生物医学计算环境，使用NCBC计算工具作为基石。本提案的目标是双重的：1)开发新的方法来捕获药物变量，用于比较有效性和上市后研究；2)定义最不密集（使用现有电子病历数据）和最密集（获取药房数据，吸引患者调和药物）之间的权衡，以便使用我们的研究结果的研究人员可以选择最适合特定研究目标和资源的方法。在两个现实世界中，我们进行了注册

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.