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）：哈佛合作伙伴国家生物计算中心的成功信号包括上市后监测研究，证明卫生系统数据可用于检测上市后阶段与药物相关的不良事件。 NCBC 现在将这些方法扩展到虚拟队列研究中，以阐明炎症途径并测量比较有效性。这些研究的一类关键数据是（1）患者正在服用的药物的准确列表，以及（2）药物对生物学和健康的积极和消极影响的评估。我们建议建立和测试基础设施，用于跨桌面和移动平台捕获新的药物来源和患者输入，以改进药物清单和药物效果核算，以进行上市后监测和比较有效性研究。公众可能认为，医生和药剂师使用的电子信息系统可确保持续获取完整的用药史。在实践中，这种全面的用药史要么不存在，要么没有被一致地访问和维护。尽管各个站点的护理存在很大的分散性，这使得任何单个机构的药物清单都很可能不完整。在临床领域，将多个药物数据源合并到患者应接受的单一全面的明确药物列表中的做法称为“药物协调”。我们实施软件和流程来捕获药物协调的研究级版本。患者还能够自我报告与药物相关的数据，包括开始和停止药物的原因、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向 i2b2 贡献表型数据。我们在软件中实例化我们的解决方案，以提供一个通用解决方案，使患者能够贡献 i2b2 数据源，并在高优先级儿科慢性病人群中测试它们。我们使用 NCBC 计算工具作为基石扩展了全国范围的生物医学计算环境。该提案的目标有两个：1）开发新方法来捕获药物变量，以进行比较有效性和上市后研究； 2）定义从最不密集（使用现有的 EHR 数据）到最密集（获取药房数据，让患者协调药物）之间的权衡，以便研究人员使用我们的研究结果可以选择最适合目标和资源的方法。特别研究。在两个现实世界的设置中，我们注册患者队列，以评估药房来源的附加值，以及患者对临床有效性研究和上市后监测中关键变量的药物相关数据进行核对。