An EHR-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department

项目总结/摘要 这项拟议的研究将开发一种使用电子健康记录数据的筛查工具， 艾德恢复和相关的发病率或死亡率，以支持在艾德中的安全和适当处置， 2019年新型冠状病毒病患者。由于COVID-19的挑战， 多变的症状，现有研究的缺乏，以及艾德能力的紧张，急诊临床医生必须 利用有限的信息做出快速的临床决策。此外，在艾德中，患者经常出现评估 在他们患病的早期，也就是COVID-19临床轨迹最不稳定的时候， 并且随后失代偿的风险最高。使用自然语言进行预测建模 自然语言处理（NLP）和机器学习（ML）技术可以利用艾德的数据丰富环境， 提高为COVID-19患者提供的护理质量。 本研究通过将研究证据引入临床，直接解决PA-17-246中强调的优先事项 通过开发和评估健康IT解决方案，将NLP的使用与 决策支持工具，将非结构化临床数据转化为可应用于实践的知识。 开发和实施拟议的COVID-19艾德返回筛查工具（CERST）可以帮助艾德 临床医生避免过早出院，并与COVID-19患者进行循证讨论 关于出院计划它还可以通过识别安全的患者来减少医院容量的压力。 为高风险的COVID-19患者提供出院和储备资源。 该项目将由一个多学科团队执行，该团队具有紧急护理，质量结果 研究，护理过渡和应用数据科学来改善临床护理，包括ML和NLP方法。 它还将使用创新的方法，包括混合方法的方法，以迭代方式开发概念图 这将为预测模型提供信息。此外，所提出的项目旨在优化推广 CERST，通过使用大量的，多样化的研究人群，包括来自第二卫生系统的数据， 不同的EHR使用快速健康互操作性资源（FHIR）规范，以协助模型 互用性这将有助于针对不同的患者群体、卫生系统和医疗机构优化模型性能。 EHR平台。由于本研究的主要数据来源对研究小组来说是容易获得的， 拥有使用数据源和执行 根据该建议，该小组完全有能力执行这项研究，并及时传播项目结果。

PROJECT SUMMARY/ABSTRACT The proposed study will develop a screening tool using electronic health record data that predicts the risk of ED return and associated morbidity or mortality to support safe and appropriate dispositions in the ED for patients with the novel coronavirus disease-2019 (COVID-19). Due to the challenges of COVID-19, with highly variable symptoms, the paucity of existing research, and strains on ED capacity, emergency clinicians must make rapid clinical decisions with limited information. Moreover, in the ED, patients often present for evaluation early on during the course of their illness, which is when the clinical trajectory for COVID-19 is most volatile and the risk for subsequent decompensation is highest. Using predictive modeling with natural language processing (NLP) and machine learning (ML) techniques can leverage the data-rich environment of the ED to improve the quality of care delivered to patients with COVID-19. This study directly addresses priorities highlighted in PA-17-246 by bringing research evidence to clinical practice through the development and evaluation a health IT solution that combines the use of NLP with a decision support tool to turn unstructured clinical data into knowledge that can be applied to practice. Developing and operationalizing the proposed COVID-19 ED return screening tool (CERST) can help ED clinicians avoid premature discharges and engage in evidence-based discussions with COVID-19 patients regarding discharge plans. It may also reduce strain on hospital capacity by identifying patients safe for discharge and reserving resources for higher-risk COVID-19 patients. The project will be executed by a multidisciplinary team with expertise in emergency care, quality outcomes research, care transitions, and applying data science to improve clinical care, including ML and NLP methods. It will also use innovative methods, including a mixed methods approach to iteratively develop the concept map that will inform the predictive model. Moreover, the proposed project is designed to optimize the generalizability of CERST, by using a large, diverse study population, including data from a second health system with a different EHR using Fast Health Interoperability Resources (FHIR) specifications to assist with model interoperability. This will help optimize model performance for differing patient populations, health systems, and EHR platforms. Since the primary data source for this study is readily accessible to the study team, who possesses prior experience working with the data sources and performing the analytic procedures outlined in the proposal, the team is well-positioned to execute this study with timely dissemination of project findings.