Instrumenting i2b2 for Improved Medication Research: Adding the Patient Voice

检测 i2b2 以改进药物研究：添加患者的声音

基本信息

批准号：
8637091
负责人：
KENNETH D MANDL
金额：
$ 43.47万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2017-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8637091
关键词：
Academic Medical Centers Accounting Adopted Adverse effects Adverse event Antiepileptic Agents Biology Biomedical Computing Boston Calculi Caring Cells Childhood Chronic Disease Clinical Clinical Research Clinical effectiveness Cohort Studies Collaborations Computer software Consent Data Data Reporting Data Sources Discipline Disease Electronic Health Record Electronics Enrollment Ensure Environment Epilepsy Evaluation Foundations Funding Future Goals Health Health system Healthcare Systems Housing Immune Inflammatory Informatics Information Systems Institution Logistics Measures Methods Modeling Ontology Output Pathway interactions Patient Self-Report Patients Pediatric Hospitals Pharmaceutical Preparations Pharmacists Pharmacy facility Phase Phenotype Physicians Population Problem Solving Process Production Public Health Recording of previous events Reporting Research Research Infrastructure Research Personnel Resources Running Safety Secure Signal Transduction Site Solutions Source South Carolina Supporting Cell System Testing Translational Research United States National Institutes of Health Urticaria Vendor Voice Work base biocomputing cohort comparative effectiveness computerized tools data modeling effectiveness research health information technology health record improved instrument microbial alkaline proteinase inhibitor novel strategies open source patient oriented post-market programs public health relevance success surveillance study tool usability virtual willingness

项目摘要

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.

描述（由申请人提供）：哈佛大学国家生物计算中心的信号成功包括市场后的监视研究，证明了卫生系统数据的实用性，用于检测在后营销阶段与药物相关的不良事件。 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比较有效性。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，以及（2）评估撞击阳性和阴性的生物学和健康药物。我们建议建立和测试基础设施，以捕获跨桌面和移动平台的新药物来源和患者投入，以改善药物清单和对药物效应的核算，以进行后市场监测和比较有效性研究。公众可能会认为，其医生和药剂师使用的电子信息系统可确保持续使用完整的药物历史。在实践中，这种全面的药物历史要么不存在，要么不始终如一地访问和维护。尽管在任何单个机构的医疗列表中都非常可能是不完整的，但在整个网站上的医疗服务中存在很大的分裂。在临床领域，将多种药物数据合并到一个全面的确定性药物清单中，患者应接受的方法称为“药物和解”。我们实施软件和流程，以捕获药物对帐的研究级版本。还可以将患者引导到自我报告药物相关的数据，以了解开始和停止药物，疗效终点和不良反应的原因。为此，我们为患者创建了一种以患者为中心的研究并为I2B2贡献表型数据的一般机制。我们在软件中实例化解决方案，以提供一种通用解决方案，从而为患者提供对I2B2数据源的贡献，并以高优先级儿科慢性病种群进行测试。我们使用NCBC计算工具作为基础石头扩展了国家规模的生物医学计算环境。该提案的目的是两个方面：1）开发新的方法来捕获用于比较有效性和后市场研究的药物变量； 2）将最不密集的（使用现有EHR数据）定义的权衡为最多（获取药房数据吸引患者以调和药物），以便使用我们的发现的研究人员可以选择最适合特定研究的目标和资源的方法。在两个现实世界中，我们注册患者队列评估由药房添加的增值，以及患者和解的与药物相关的数据，用于临床有效性研究和后市场监视的关键变量。