A framework to stratify patient cohorts for clinical management

项目总结 现有的电子健康记录(EHR)系统在查找可以受益的患者方面的功能有限 通过特殊的干预。因此，临床医生无法主动接触这些患者，导致 证据与护理的差距在不断扩大。这项提案的目标是改善医疗保健服务。目标是 是开发一个数据框架，用于准确地找到可以从大规模/人口规模中受益的患者 干预措施。我们将调查两个方面来改进患者搜索-提高分辨率 提高搜索的质量(目标1)和提高搜索数据的质量(目标2)。在目标1中，我们 将检验假设，即基于规则的模型的相关临床指南，将发现患者的 比传统查询更准确。理性的是，基于规则的方法将允许复杂的 对资格标准进行分组，从而提供比传统搜索更高的分辨率 查询。在目标2中，我们将调查机器学习(ML)是否可以提高患者数据的准确性 并且有助于通过传统查询和指南获得的搜索结果的准确性 规则库。除了上述目标之外，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。这项建议建立在血脂管理的基础上 计划在布里格姆和妇女医院(BWH)。我们的工作将集中在寻找血脂患者- 然而，我们的方法和工具将推广到其他医疗领域和 机构。研究团队包括心脏病学、机器学习、健康信息等方面的国家专家 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)，通过扩展流行的用于集成生物学和床边的信息学(I2b2)平台 在过去的10年里，我们开发和支持了这项服务，目前有200多家医疗中心在使用这项服务。I2b2-ML 将把i2b2的S证明的描述患者队列的能力扩展到临床领域。我们的研究 将产生准确地找到可以从临床干预中受益的患者的方法，从而 实现人口规划的成本效益。它将直接帮助扩大血脂管理计划的规模， BWH使更广泛的患者群体受益。我们对血脂管理方面的差距的详细描述 大型医疗保健系统可能会为血脂管理指南的改进提供信息。这个 在这个项目中开发的方法和工具将以开放源码的形式发布，以实现潜在的 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临床项目。此外，由此产生的基础设施将作为 开发基于人工智能的应用程序的平台，以改善临床护理。这些结果 预计将对医疗服务的提供产生积极影响，使更多的患者得到最佳护理。

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.