Characterizing COVID-19 Patients through a Community Health Information Exchange and EHR databases

通过社区健康信息交换和 EHR 数据库描述 COVID-19 患者的特征

• 批准号: 10177252
• 负责人: XIA NING
• 金额: $ 7.5万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10177252
• 关键词: Age; Algorithms; Back; COVID-19; COVID-19 pandemic; Centers for Disease Control and Prevention (U.S.); Cessation of life; Chills; Clinical; Clinical Course of Disease; Clinical Data; Communities; Community Health; Contact Tracing; Coronavirus; Coughing; Data; Data Discovery; Data Set; Databases; Dimensions; Disadvantaged; Disease; Ear; Electronic Health Record; Fever; Foundations; Geographic Locations; Goals; Grant; Headache; Health; Health Personnel; Health system; Incidence; Indiana; Individual; Infection; Influenza; Intervention; Knowledge; Learning; Methods; Myalgia; Nature; Parents; Patient Care; Patient Monitoring; Patients; Phenotype; Population; Population Surveillance; Public Health; Research; Resources; Severities; Shortness of Breath; Signs and Symptoms; Smell Perception; Sore Throat; Source; Taste Perception; Testing; Time; Treatment outcome; Writing; base; clinical data warehouse; cohort; data exchange; data quality; demographics; disease transmission; electronic data; experience; health care service organization; health data; improved; innovation; interest; novel; novel strategies; pandemic disease; respiratory distress syndrome; seropositive; sex; structured data; symptomatology; transmission process

Project Summary/Abstract The COVID-19 pandemic is a significant public health problem that will require novel approaches for management and intervention. Knowledge of the disease’s transmission, symptomatology, clinical course, treatment and outcomes is rapidly evolving based on many sources. An important source for advancing this knowledge are data from electronic health records (EHR) and health information exchanges (HIE) because they can provide a real-time, unvarnished view of the disease. However, the initially “invisible” nature of the disease makes clear that clinicians and public health personnel were at a significant disadvantage in discovering and quantifying the pandemic. There is an urgent need to learn rapidly from EHR and other data to improve discovery and monitoring of patients infected by the coronavirus. The evolving dynamic and understanding of the incidence and course of COVID-19 requires that we develop new methods for discovery from data. The long-term goal of our research is to develop collaborative filtering algorithms to facilitate access to and analysis of clinical data. The goal of this application is to characterize COVID-19 patients through data in a community HIE, specifically the Indiana Network for Patient Care (INPC) within Indiana’s HIE (IHIE), and understand how that characterization differs from that within the EHRs of individual health systems. Understanding how COVID-19 patients are represented in HIEs and EHRs will build an important foundation for downstream computational activities, such as real-time discovery, public health surveillance, intervention management and contact tracing. The two specific aims of this project are to (1) extract a cohort of patients suffering from COVID-19 and similar diseases from IHIE and (2) characterize patients according to several dimensions, such as demographics, signs and symptoms, and disease course using both the INPC as well as separate EHR data sets. The data, going back to 1/1/2015, will be extracted from the INPC, and the clinical data warehouses at IU Health and Eskenazi Health, two of our major health system partners. As of this writing, 230,749 individuals in Indiana (3.4 percent of the population of 6.73m) have been tested for the coronavirus, of whom 32,078 (13.9 percent) have tested positive. We will apply computational phenotyping approaches using both HIE and individual EHR data in order to help us evaluate to what degree data from individual EHRs can help approximate characterizations based on HIE data. This proposal is significant because it will help us understand how HIE and EHR data can be used to characterize both COVID-19 and non-COVID-19 patients. It is innovative because it leverages multiple computational phenotyping methods on both individual organizations’ EHR, as well as HIE, data to generate a comprehensive characterization of COVID-19 and non-COVID-19 patients.

项目摘要/摘要 新冠肺炎大流行是一个重大的公共卫生问题，需要采取新的方法来应对 管理和干预。疾病传播、症状学、临床知识 当然，基于许多来源，治疗和结果正在迅速演变。一个重要的来源 来自电子健康记录(EHR)和健康信息的数据推动了这一知识的发展 交流(HIE)，因为它们可以提供对疾病的实时、原封不动的看法。然而， 这种疾病最初的“隐形”性质清楚地表明，临床医生和公共卫生人员 在发现和量化大流行方面处于明显的劣势。有一件急事 需要从电子病历和其他数据中快速学习，以改进对患者的发现和监控 被冠状病毒感染。疾病的发生和过程的演变动态和认识 新冠肺炎要求我们开发从数据中发现数据的新方法。我们的长期目标是 研究是开发协作过滤算法，以方便访问和分析临床 数据。此应用程序的目标是通过社区中的数据来描述新冠肺炎患者的特征 HIE，特别是印第安纳州HIE内的印第安纳患者护理网络(INPC)，以及 了解这一特征与个别卫生系统EHR中的特征有何不同。 了解新冠肺炎患者在HIE和EHR中的代表将建立一个重要的 为下游计算活动奠定基础，如实时发现、公共卫生 监测、干预管理和接触者追踪。这个项目的两个具体目标是 (1)从新生儿缺氧缺血性脑病(IHIE)中提取新冠肺炎及类似疾病患者的队列；(2) 根据人口统计、体征和症状等几个维度来描述患者的特征， 以及使用INPC和单独的EHR数据集的疾病病程。数据，回溯到 2015年1月1日，将从INPC和IU Health和 Eskenazi Health，我们的两个主要卫生系统合作伙伴。截至撰写本文时，全球有230,749人 印第安纳州(673万人口中的3.4%)接受了冠状病毒检测，其中 有32,078人(13.9%)检测呈阳性。我们将应用计算表型方法 同时使用HIE和个人EHR数据，以帮助我们评估数据在多大程度上 单独的EHR可以帮助根据HIE数据近似描述特征。这项建议是 意义重大，因为它将帮助我们了解如何使用HIE和EHR数据来表征 包括新冠肺炎和非新冠肺炎患者。它具有创新性，因为它利用了多个 两个组织的EHR和HIE数据的计算表型方法 生成新冠肺炎和非新冠肺炎患者的全面特征。

项目成果

A Deep Generative Model for Molecule Optimization via One Fragment Modification.

• DOI: 10.1038/s42256-021-00410-2
• 发表时间: 2021-12
• 期刊: Nature machine intelligence
• 影响因子: 23.8
• 作者: Chen Z;Min MR;Parthasarathy S;Ning X
• 通讯作者: Ning X

Preliminary evaluation of the Chest Pain Dashboard, a FHIR-based approach for integrating health information exchange information directly into the clinical workflow.

胸痛仪表板的初步评估，这是一种基于 FHIR 的方法，用于将健康信息交换信息直接集成到临床工作流程中。

• 发表时间: 2019
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Schleyer,TitusKL;Rahurkar,Saurabh;Baublet,AllissiaM;Kochmann,Matthias;Ning,Xia;Martin,DouglasK;Finnell,JohnT;Kelley,KeithW;FHIRDevelopmentTeam;Schaffer,JasonT
• 通讯作者: Schaffer,JasonT

Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.

• DOI: 10.1371/journal.pone.0255467
• 发表时间: 2021
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ning X;Fan Z;Burgun E;Ren Z;Schleyer T
• 通讯作者: Schleyer T

Hybrid collaborative filtering methods for recommending search terms to clinicians.

• DOI: 10.1016/j.jbi.2020.103635
• 发表时间: 2021-01
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Ren Z;Peng B;Schleyer TK;Ning X
• 通讯作者: Ning X