Cueing COVID-19: NLM Administrative Supplement for Research on Coronavirus Disease 2019

项目总结 临床护理所需数据的变异性和复杂性要求临床医生准确和 在个体之间高效识别新冠肺炎，范围从无症状感染到多器官和 全身性表现。新冠肺炎和脓毒症一样，涉及不同的病因，涉及范围广泛 症状(例如，初始、炎症、高炎症反应)。因为患者可能会出现轻微的， 对于中度或重度症状，临床医生必须同时确定疾病的阶段和最佳治疗方案。这个 引发新冠肺炎患者严重疾病的因素尚不完全清楚。像其他建筑群一样， 具有挑战性的诊断，战壕中的临床医生努力使用可获得的数据诊断和治疗患者 电子健康记录(EHR)。在我们目前的NIH NLMR01“信号败血症：开发一个框架，以 优化警报设计“，我们创建了高于首选项的脓毒症专用增强视觉显示模型 与通常的零散的、非定向的信息收集相比，性能更好。为了这个 作为补充，我们提出了新冠肺炎诊疗与临床管理系统的设计与开发 增强的视觉显示模型支持临床医生识别新冠肺炎的关键阶段 诊断和治疗决策。为了创建模型，我们将确定相关的诊断和 将包括临床特征、实验室结果和放射学结果的治疗数据元素(例如， 胸部CT)。我们的项目将调查新冠肺炎的新兴模式及其阶段，并确保我们的模型 与我们的知识随着医学界的发展而出现的最佳实践相一致。模特们 提供一种基于电子病历的方法来挖掘临床数据以识别新冠肺炎的存在，该方法支持 新冠肺炎的各种呈现方式、数据元素的可用性、诊断测试的准确性以及 这种疾病具有高度传染性。具体目标1：确定新出现的患者特定的临床特征 新冠肺炎和测试分析，为新冠肺炎诊断和临床管理提供关键信息。 元素包括上面列出的特征(例如，症状、并存)以及新冠肺炎特定测试 结果，包括测试的阳性预测值和阴性预测值的特定数据。具体目标2：发展 电子病历嵌入式CDS工具，使用我们的新冠肺炎增强的视觉显示模型，使用合成信息 通过NLM家长拨款和具体目标获得1.评估技术可行性和可用性 新颖的新冠肺炎CDS工具。为什么重要：在大流行期间，不能像临床医生那样含糊其辞 需要梳理一下电子病历。更好地可视化和解释EHR数据的能力支持最佳 诊断和临床处理。我们增强的视觉显示模型将支持临床医生评估 确定发病风险较高的患者的人口统计因素、基础条件和合并症 和死亡率，因此将推动更好的临床管理。

PROJECT SUMMARY The variability and the complexity of the data needed for clinical care requires clinicians to accurately and efficiently recognize COVID-19 amongst individuals, ranging from asymptomatic infection to multiorgan and systemic manifestations. COVID-19, like sepsis, involves different disease etiologies that span a wide range of syndromes (e.g., initial, inflammatory, hyperinflammatory response). Because patients can present with mild, moderate, or severe symptoms, clinicians must both identify the disease stage and optimal treatment. The factors that trigger severe illness in COVID-19 patients are not completely understood. Like other complex, challenging diagnoses, clinicians in the trenches struggle to diagnose and treat patients using data available in the electronic health record (EHR). In our current NIH NLM R01 “Signaling Sepsis: Developing a Framework to Optimize Alert Design”, we created sepsis specific enhanced visual display models that outranked preference and performance when compared with the usual care of fragmented, non-directed information gathering. For this supplement, we propose the design and development of COVID-19 diagnosis and clinical management enhanced visual display models to support clinicians’ recognition of critical phases in COVID-19 diagnosis and treatment decisions. In order to create the models, we will identify relevant diagnostic and treatment data elements that will include clinical characteristics, laboratory results, and radiology results (e.g., chest CT). Our project will survey emerging models of COVID-19 and its stages, and ensure our models are congruent with best practices that emerge as our knowledge as a medical community evolves. The models provide an EHR based method to mine clinical data to identify the presence of COVID-19 which supports the variety of ways in which COVID-19 presents, availability of data elements, accuracy of diagnostic tests, and the highly infective nature of the disease. Specific Aim 1: To identify emerging patient-specific clinical features of COVID-19 and testing analytics to present critical information for COVID-19 diagnosis and clinical management. Elements include the characteristics listed above (e.g., symptoms, co-morbidities) plus COVID-19 specific test results, including data specific to the tests’ positive and negative predictive values. Specific Aim 2: To develop an EHR embedded CDS tool using our COVID-19 enhanced visual display models using synthesized information obtained through the NLM parent grant and Specific Aim 1. Evaluate the technical feasibility and usability of the novel COVID-19 CDS tool. Why It Matters: During a pandemic, there’s no room for ambiguity as clinicians are required to comb through the EHR. The ability to better visualize and interpret EHR data supports optimal diagnosis and clinical management. Our enhanced visual display models will support clinicians as they evaluate demographic factors, underlying conditions, and comorbidities that identify patients at higher risk of morbidity and mortality and will therefore drive better clinical management.