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":"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。","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":"2024 ","pages":"276-284"},"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":"{\"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\":\"2024 \",\"pages\":\"276-284\"},\"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}","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}
Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.
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.