Anne M Walling, Karl A Lorenz, Anita Yuan, Claire E O'Hanlon, Michael McClean, Benjamin Fayyazuddin Ljungberg, Karleen F Giannitrapani, Selen Bozkurt, Sidharth Anand, John Glaspy, Neil S Wenger, Charlotta Lindvall
{"title":"在癌症中心创建学习型医疗系统：晚期实体癌电子健康记录表型的推广性。","authors":"Anne M Walling, Karl A Lorenz, Anita Yuan, Claire E O'Hanlon, Michael McClean, Benjamin Fayyazuddin Ljungberg, Karleen F Giannitrapani, Selen Bozkurt, Sidharth Anand, John Glaspy, Neil S Wenger, Charlotta Lindvall","doi":"10.1200/OP.24.00389","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To test the generalizability of an electronic health record (EHR) phenotype for patients with advanced solid cancer, which was previously developed in a single cancer center.</p><p><strong>Methods: </strong>We compared an algorithm to identify patients with advanced solid cancer from a random sample of patients with active cancer in the Veterans Health Administration (VA) and an academic cancer center with a human-coded reference standard between January 1, 2016, and December 31, 2019.</p><p><strong>Results: </strong>Compared with the human-coded reference standard, the algorithm had high specificity (93%; 95% CI, 87 to 99 and 97%; 95% CI, 93 to 100) and reasonable sensitivity (85%; 95% CI, 76 to 94 and 87%; 95% CI, 77 to 97) in the VA and academic cancer center populations, respectively. Patients with advanced cancer (compared with those with active nonadvanced cancer) had higher mortality at the VA and academic cancer center (29.2% and 17.0% 6-month mortality <i>v</i> 6.8% and 3.5%), respectively.</p><p><strong>Conclusion: </strong>This EHR phenotype can be used to measure and improve the quality of palliative care for patients with advanced cancer within and across health care settings.</p>","PeriodicalId":14612,"journal":{"name":"JCO oncology practice","volume":" ","pages":"OP2400389"},"PeriodicalIF":4.7000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Creating a Learning Health System in a Cancer Center: Generalizability of an Electronic Health Record Phenotype for Advanced Solid Cancer.\",\"authors\":\"Anne M Walling, Karl A Lorenz, Anita Yuan, Claire E O'Hanlon, Michael McClean, Benjamin Fayyazuddin Ljungberg, Karleen F Giannitrapani, Selen Bozkurt, Sidharth Anand, John Glaspy, Neil S Wenger, Charlotta Lindvall\",\"doi\":\"10.1200/OP.24.00389\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To test the generalizability of an electronic health record (EHR) phenotype for patients with advanced solid cancer, which was previously developed in a single cancer center.</p><p><strong>Methods: </strong>We compared an algorithm to identify patients with advanced solid cancer from a random sample of patients with active cancer in the Veterans Health Administration (VA) and an academic cancer center with a human-coded reference standard between January 1, 2016, and December 31, 2019.</p><p><strong>Results: </strong>Compared with the human-coded reference standard, the algorithm had high specificity (93%; 95% CI, 87 to 99 and 97%; 95% CI, 93 to 100) and reasonable sensitivity (85%; 95% CI, 76 to 94 and 87%; 95% CI, 77 to 97) in the VA and academic cancer center populations, respectively. Patients with advanced cancer (compared with those with active nonadvanced cancer) had higher mortality at the VA and academic cancer center (29.2% and 17.0% 6-month mortality <i>v</i> 6.8% and 3.5%), respectively.</p><p><strong>Conclusion: </strong>This EHR phenotype can be used to measure and improve the quality of palliative care for patients with advanced cancer within and across health care settings.</p>\",\"PeriodicalId\":14612,\"journal\":{\"name\":\"JCO oncology practice\",\"volume\":\" \",\"pages\":\"OP2400389\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JCO oncology practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1200/OP.24.00389\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCO oncology practice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1200/OP.24.00389","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Creating a Learning Health System in a Cancer Center: Generalizability of an Electronic Health Record Phenotype for Advanced Solid Cancer.
Purpose: To test the generalizability of an electronic health record (EHR) phenotype for patients with advanced solid cancer, which was previously developed in a single cancer center.
Methods: We compared an algorithm to identify patients with advanced solid cancer from a random sample of patients with active cancer in the Veterans Health Administration (VA) and an academic cancer center with a human-coded reference standard between January 1, 2016, and December 31, 2019.
Results: Compared with the human-coded reference standard, the algorithm had high specificity (93%; 95% CI, 87 to 99 and 97%; 95% CI, 93 to 100) and reasonable sensitivity (85%; 95% CI, 76 to 94 and 87%; 95% CI, 77 to 97) in the VA and academic cancer center populations, respectively. Patients with advanced cancer (compared with those with active nonadvanced cancer) had higher mortality at the VA and academic cancer center (29.2% and 17.0% 6-month mortality v 6.8% and 3.5%), respectively.
Conclusion: This EHR phenotype can be used to measure and improve the quality of palliative care for patients with advanced cancer within and across health care settings.
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