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

提示 COVID-19：NLM 2019 年冠状病毒病研究行政补充

基本信息

批准号：
10177308
负责人：
Kristen Elizabeth Miller
金额：
$ 7.5万
依托单位：
MEDSTAR HEALTH RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-10 至 2021-07-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10177308
关键词：
2019-nCoV Administrative Supplement Age COVID-19 Cardiovascular Diseases Case Study Characteristics Clinical Clinical Data Clinical Management Comb animal structure Communities Complex Coronavirus Coughing Country Creatinine Cues Data Data Aggregation Data Element Demographic Factors Development Diabetes Mellitus Diagnosis Diagnostic Diagnostic tests Disease Documentation Elderly Electronic Health Record Elements Ensure Etiology Fatigue Fever Functional disorder Goals Health care facility Hypertension Individual Infection Inflammatory Influenza Knowledge Laboratories Lung diseases Medical Methods Modeling Morbidity - disease rate Nature Organ Patients Pattern Performance Phase Plasma Predictive Value Provider Public Health Radiology Specialty Research Resources Respiratory Failure Sepsis Septic Shock Severities Signal Transduction Surveys Symptoms Syndrome Test Result Testing United States National Institutes of Health Virus Visual associated symptom base chest computed tomography clinical care clinical decision support comorbidity design diagnostic accuracy evidence base flu health economics high risk information gathering mortality mortality risk novel optimal treatments pandemic disease parent grant preference research clinical testing response support tools treatment as usual usability user centered design

项目摘要

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.

项目摘要临床护理所需的数据的可变性和复杂性要求临床医生准确和有效识别个体之间的COVID-19，从无症状感染到多器官感染，系统性表现。COVID-19与脓毒症一样，涉及不同的疾病病因，这些病因涵盖广泛的综合征（例如，初始、炎性、高度炎性反应）。因为患者可能会出现轻度，中度或重度症状，临床医生必须确定疾病阶段和最佳治疗。的引发COVID-19患者严重疾病的因素尚未完全了解。像其他复杂的，具有挑战性的诊断，在战壕中的临床医生努力诊断和治疗患者使用的数据，电子健康记录（EHR）。在我们目前的NIH NLM R 01“信号脓毒症：开发一个框架，优化警报设计”，我们创建了脓毒症特异性增强的视觉显示模型，和性能相比，通常的照顾碎片，非定向信息收集。为此作为补充，我们提出了COVID-19诊断和临床管理的设计和开发增强的视觉显示模型，以支持临床医生识别COVID-19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COVID-19及其阶段的新兴模型，并确保我们的模型与我们作为医学界的知识发展而出现的最佳实践相一致。模型提供一种基于EHR的方法来挖掘临床数据，以识别COVID-19的存在， COVID-19呈现的各种方式，数据元素的可用性，诊断测试的准确性，以及这种疾病的高传染性。具体目标1：确定新出现的患者特异性临床特征， COVID-19和测试分析为COVID-19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、合并症）加COVID-19特异性检测结果，包括特定于测试的阳性和阴性预测值的数据。具体目标2：发展 EHR嵌入式CDS工具，使用我们的COVID-19增强视觉显示模型，使用合成信息通过NLM父母补助金和具体目标1获得。评估的技术可行性和可用性新型COVID-19 CDS工具。重要性：在大流行期间，临床医生没有模棱两可的余地需要梳理电子病历更好地可视化和解释EHR数据的能力支持最佳的诊断和临床管理。我们增强的视觉显示模型将支持临床医生，因为他们评估确定患者发病风险较高的人口统计学因素、基础疾病和合并症和死亡率，因此将推动更好的临床管理。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Score at the Door: A retrospective data analysis of sepsis scoring criteria in the Emergency Department.

门口评分：急诊科脓毒症评分标准的回顾性数据分析。

DOI：
10.1177/2327857919081071
发表时间：
2019
期刊：
Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. International Symposium of Human Factors and Ergonomics in Healthcare
影响因子：
0
作者：
Bonk,Christopher;Schubel,Laura;Ferrell,Brandon;Ramdeen,Sanjhai;Littlejohn,Robin;Ladkany,Diana;Miller,Kristen
通讯作者：
Miller,Kristen

Data-driven approach to Early Warning Score-based alert management.

DOI：
10.1136/bmjoq-2017-000088
发表时间：
2018
期刊：
BMJ open quality
影响因子：
1.4
作者：
Capan M;Hoover S;Miller KE;Pal C;Glasgow JM;Jackson EV;Arnold RC
通讯作者：
Arnold RC

A Framework to Tackle Risk Identification and Presentation Challenges in Sepsis.

应对脓毒症风险识别和呈现挑战的框架。

DOI：
10.24150/ajhm/2018.002
发表时间：
2018
期刊：
American journal of hospital medicine
影响因子：
0
作者：
Capan,Muge;Mosby,Danielle;Miller,Kristen;Tao,Jun;Wu,Pan;Weintraub,William;Kowalski,Rebecca;Arnold,Ryan
通讯作者：
Arnold,Ryan

FUTURES: Forecasting the Unexpected Transfer to Upgraded REsources in Sepsis.

未来：预测脓毒症中意外向升级资源的转移。