Madeleine D Jones, Aubrey Winger, Christian Wernz, Jonathan Michel, Sihang Jiang, A. Zhou, Ebony J. Hilton, M. Zemmel, S. Sengupta, Kierah Barnes, Johanna J. Loomba, Donald E. Brown
{"title":"调查COVID-19急诊科就诊时间标记的影响:使用综合单站点数据与国家COVID队列协作(N3C)数据比较医疗差异分析","authors":"Madeleine D Jones, Aubrey Winger, Christian Wernz, Jonathan Michel, Sihang Jiang, A. Zhou, Ebony J. Hilton, M. Zemmel, S. Sengupta, Kierah Barnes, Johanna J. Loomba, Donald E. Brown","doi":"10.1109/SIEDS58326.2023.10137801","DOIUrl":null,"url":null,"abstract":"National COVID Cohort Collaborative (N3C) enclave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes.","PeriodicalId":267464,"journal":{"name":"2023 Systems and Information Engineering Design Symposium (SIEDS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the Impact of Temporal Labeling of Emergency Department Visits for COVID-19: Comparing Healthcare Disparities Analyses Using Comprehensive, Single-Site Data with National COVID Cohort Collaborative (N3C) Data\",\"authors\":\"Madeleine D Jones, Aubrey Winger, Christian Wernz, Jonathan Michel, Sihang Jiang, A. Zhou, Ebony J. Hilton, M. Zemmel, S. Sengupta, Kierah Barnes, Johanna J. Loomba, Donald E. Brown\",\"doi\":\"10.1109/SIEDS58326.2023.10137801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"National COVID Cohort Collaborative (N3C) enclave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes.\",\"PeriodicalId\":267464,\"journal\":{\"name\":\"2023 Systems and Information Engineering Design Symposium (SIEDS)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Systems and Information Engineering Design Symposium (SIEDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIEDS58326.2023.10137801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS58326.2023.10137801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating the Impact of Temporal Labeling of Emergency Department Visits for COVID-19: Comparing Healthcare Disparities Analyses Using Comprehensive, Single-Site Data with National COVID Cohort Collaborative (N3C) Data
National COVID Cohort Collaborative (N3C) enclave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes.