A framework to stratify patient cohorts for clinical management

项目摘要 现有的电子健康记录（EHR）系统在查找可以受益的患者方面功能有限 特别的干预。因此，临床医生无法主动接触这些患者，导致 一个不断扩大的证据-护理差距。该提案的目标是改善医疗保健服务。客观 是开发一个数据框架，用于准确发现可以从大规模/人群中受益的患者 干预措施。我们将研究两个方面来改善患者搜索-提高分辨率 目标1）和提高搜索所针对的数据的质量（目标2）。在目标1中， 将检验一个假设，即相关临床指南的基于规则的模型，将发现具有更高 比传统的查询更准确。理由是，基于规则的方法将允许复杂的 分组的资格标准，从而将提供更高的分辨率的搜索比传统的 查询.在目标2中，我们将研究机器学习（ML）是否可以提高患者数据的准确性 并且有助于通过常规查询和指南获得的搜索结果的准确性 规则基础除了上述目标，在目标3中，我们将自动部署规则库和ML模型 并最大限度地减少开发ML模型的手动工作。该建议建立在脂质管理的基础上 布里格姆妇女医院（Brigham and Women's Hospital，BWH）我们的工作将集中在寻找病人的血脂- 然而，我们的方法和工具将推广到其他医疗领域， 机构职能体系该研究团队包括心脏病学，机器学习，健康信息， 技术（HIT）和开源软件开发。我们将创建一个开源软件平台 （i2b2-ML）通过扩展流行的“整合生物学和床边信息学”（i2b2）平台， 我们在过去的10年里开发和支持了200多个医疗中心。I2b2-ML 将扩展i2b2的证明能力，以表征患者队列的研究进入临床领域。我们的研究 将产生用于准确发现可以通过临床干预受益的患者的方法，从而将 使人口方案具有成本效益。它将直接帮助扩大脂质管理计划， BWH使更广泛的患者群体受益。我们详细描述了一个国家在脂质管理方面的差距， 大型医疗保健系统将可能为血脂管理指南的改进提供信息。的 在这个项目中开发的方法和工具将以开源的形式传播， 纳入其他机构的临床数据基础设施，这将有助于实施 全国范围内的人口规模的临床项目。此外，由此产生的基础设施将作为 该平台用于开发基于人工智能的应用程序，以改善临床护理。这些结果 预计将对医疗保健服务产生积极影响，使更多的患者获得最佳护理。

PROJECT SUMMARY Existing Electronic Health Record (EHR) systems have limited functionality to find patients that can benefit by particular interventions. Hence, clinicians are unable to proactively reach-out to these patients, leading to an ever-widening evidence-care gap. The goal of this proposal is to improve healthcare delivery. The objective is to develop a data-framework for accurately finding patients that can benefits from large/population scale interventions. We will investigate two dimensions for improving the patient search—increasing the resolution of the search (aim 1) and increasing the quality of the data that the search is performed on (aim 2). In aim 1 we will test the hypothesis that a rule-based model of the relevant clinical guideline, will find patients with higher accuracy than the conventional queries. The rational is that the rule-based approach will allow complex groupings of the eligibility criteria and will thereby provide a higher resolution of search than conventional query. In aim 2 we will investigate whether machine learning (ML) can improve the accuracy of patient data and contribute to the accuracy of search results obtained through the conventional query and the guideline rule-base. In addition to the above aims, in aim 3 we will automate deployment of the rule-base and ML models and minimize the manual effort for developing ML models. This proposal builds on the lipid management program at Brigham and Women's hospital (BWH). Our work will be focused on finding patients for lipid- management; however, our methodology and tooling will be generalizable to other medical areas and institutions. The study team includes national experts in cardiology, machine learning, health information technology (HIT) and open-source software development. We will create an open-source software platform (i2b2-ML) by extending the popular `Informatics for Integration Biology and the Bedside' (i2b2) platform that we have developed and supported over the past 10 years and that is used by over 200 medical centers. I2b2-ML will extend i2b2's proven ability to characterize patient cohorts for research into the clinical realm. Our study will yield methodology for accurately finding patients that can benefit by clinical intervention and will thereby enable cost-effectiveness of population programs. It will directly help scale the lipid management program at BWH to benefit a wider patient population. Our detailed characterization of the gaps in lipid management at a large healthcare system will potentially inform improvements in the lipid management guideline. The methodology and tooling developed in this project will be disseminated in open-source for potential incorporation in the clinical data infrastructure at other institutions, which will facilitate implementation of population-scale clinical programs across the nation. In addition, the resultant infrastructure will serve as a platform for development of artificial intelligence-based applications to improve clinical care. These outcomes are expected to have a positive impact on health care delivery so that more patients will get the optimal care.