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

项目摘要/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，以预测患上糖尿病的风险 ED的复发和相关的发病率或死亡率，以支持在ED中安全和适当地处置 新型冠状病毒病患者-2019年(新冠肺炎)。由于新冠肺炎的挑战，具有很高的 症状多变，现有研究的匮乏，以及ED能力的紧张，急诊临床医生必须 在有限的信息下快速做出临床决策。此外，在急诊室，患者经常到场接受评估 在他们病程的早期，也就是新冠肺炎的临床轨迹最不稳定的时候 而随后出现失代偿的风险最高。使用自然语言进行预测建模 处理(NLP)和机器学习(ML)技术可以利用教育署丰富的数据环境 提高新冠肺炎患者的护理质量。 这项研究通过将研究证据带到临床，直接解决了PA-17-246中强调的优先事项 通过开发和评估将NLP的使用与 决策支持工具，将非结构化的临床数据转化为可应用于实践的知识。 开发和实施拟议的新冠肺炎ED退货筛选工具(CERST)可以帮助ED 临床医生避免过早出院，并与新冠肺炎患者进行循证讨论 关于出院计划。它还可以通过识别安全的患者来减轻医院容量的压力 为高危新冠肺炎患者出院和预留资源。 该项目将由一个多学科团队执行，该团队在紧急护理、质量结果等方面具有专业知识 研究、护理过渡和应用数据科学来改进临床护理，包括ML和NLP方法。 它还将使用创新的方法，包括采用混合方法反复开发概念图 这将为预测模型提供信息。此外，所提出的方案旨在优化泛化能力 通过使用大量、多样化的研究人群，包括来自第二个卫生系统的数据， 不同的电子病历使用快速健康互操作性资源(FHIR)规范来协助建模 互操作性。这将有助于针对不同的患者群体、医疗保健系统和 电子病历平台。由于这项研究的主要数据来源对研究小组来说很容易获得，谁 具有使用数据源和执行中概述的分析过程的经验 根据该提案，该小组有能力进行这项研究，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.