Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.

Andrew J King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J McVerry, Christopher W Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar Marroquin, Derek C Angus, Christopher M Horvat
{"title":"Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.","authors":"Andrew J King, Lisa Higgins, Carly Au, Salim Malakouti, Edvin Music, Kyle Kalchthaler, Gilles Clermont, William Garrard, David T Huang, Bryan J McVerry, Christopher W Seymour, Kelsey Linstrum, Amanda McNamara, Cameron Green, India Loar, Tracey Roberts, Oscar Marroquin, Derek C Angus, Christopher M Horvat","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial.</p><p><strong>Methods: </strong>The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization.</p><p><strong>Results: </strong>Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center.</p><p><strong>Conclusions: </strong>We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141839/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Objectives: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial.

Methods: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization.

Results: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center.

Conclusions: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

在 COVID-19 大流行中自动生成国际多因素自适应平台试验的病例报告表。
目的为一项国际性、务实、多因素、反应自适应、贝叶斯 COVID-19 平台试验自动填充病例报告表 (CRF):重点研究地点包括隶属于美国同一医疗系统的 27 家医院和 2 个大型电子病历 (EHR) 实例(1 个 Cerner Millennium 和 1 个 Epic)。本文介绍了我们在使用电子病历数据自动填充试验的四种表格方面所做的努力:基线、日常、出院和反应自适应随机化:2020年4月至2022年5月期间,UPMC医疗系统的417名患者加入了试验。基于 MySQL 的提取、转换和加载管道自动填充了 526 个 CRF 变量中的 499 个。填充后的表格经过统计和人工审核,然后报告给试验的国际数据协调中心:我们在一项大型平台试验中实现了 CRF 的自动填充,并为今后的试验提出了改进这一流程的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
文献相关原料
公司名称 产品信息 采购帮参考价格
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信