Abstract：
BACKGROUND: Review of emergency department (ED) revisits with admission allows the identification of improvement opportunities. Applying a health equity lens to revisits may highlight potential disparities in care transitions. Universal definitions or practicable frameworks for these assessments are lacking. The authors aimed to develop a structured methodology for this quality assurance (QA) process, with a layered equity analysis.
METHODS: The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher\'s exact tests.
RESULTS: The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education.
CONCLUSIONS: The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.
METHODS: The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher\'s exact tests.
RESULTS: The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education.
CONCLUSIONS: The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.
摘要：
背景：急诊（ED）复查入院可以识别改善机会。将健康公平视角应用于重诊可能会凸显护理过渡中的潜在差异。这些评估缺乏普遍的定义或切实可行的框架。作者旨在为这一质量保证(QA)过程开发一种结构化的方法，进行分层的股权分析。
方法：作者开发了一种分类工具，用于识别可能可预防的72小时入院返回（PPRA-72）。指导会计，无关,意想不到的,或疾病进展返回。第二个审查小组评估了仪器的可靠性。开发了一种自我报告的种族/种族（R/E）和语言算法，以最大程度地减少无法分类的数据。处置分布,退货率，使用Pearson卡方和Fisher精确检验分析PPRA-72分类的差异。
结果：2022年需要入院的ED回诊的PPRA-72率为4.8%。审查团队对PPRA-72与PPRA-72的二元测定达成了93％的一致性（κ=0.51）。不可预防的回报。在ED倾向上，R/E和语言之间存在显着差异（p<0.001），在索引访问和其他72小时回访时，R/E怀特的入院频率更高。72小时内的回访率显着差异的R/E（p<0.001），而不是语言（p=0.156），R/EBlack最常返回72小时。R/E（p=0.446）或语言（p=0.248）之间的PPRA-72率没有差异。该计划通过信息学优化导致了系统改进，分诊协议，提供商反馈，和教育。
结论：作者开发了一种综述方法，用于识别ED72小时回报的改善机会。这个QA过程能够识别视差区域，持续致力于制定下一步措施，以确保医疗过渡中的健康公平。
方法：作者开发了一种分类工具，用于识别可能可预防的72小时入院返回（PPRA-72）。指导会计，无关,意想不到的,或疾病进展返回。第二个审查小组评估了仪器的可靠性。开发了一种自我报告的种族/种族（R/E）和语言算法，以最大程度地减少无法分类的数据。处置分布,退货率，使用Pearson卡方和Fisher精确检验分析PPRA-72分类的差异。
结果：2022年需要入院的ED回诊的PPRA-72率为4.8%。审查团队对PPRA-72与PPRA-72的二元测定达成了93％的一致性（κ=0.51）。不可预防的回报。在ED倾向上，R/E和语言之间存在显着差异（p<0.001），在索引访问和其他72小时回访时，R/E怀特的入院频率更高。72小时内的回访率显着差异的R/E（p<0.001），而不是语言（p=0.156），R/EBlack最常返回72小时。R/E（p=0.446）或语言（p=0.248）之间的PPRA-72率没有差异。该计划通过信息学优化导致了系统改进，分诊协议，提供商反馈，和教育。
结论：作者开发了一种综述方法，用于识别ED72小时回报的改善机会。这个QA过程能够识别视差区域，持续致力于制定下一步措施，以确保医疗过渡中的健康公平。
