Evaluation of a Structured Review Process for Emergency Department Return Visits with Admission

IF 2.3 Q2 HEALTH CARE SCIENCES & SERVICES
Zoe Grabinski MD (is Assistant Professor, Ronald O. Perelman Department of Emergency Medicine and Department of Pediatrics, New York University Grossman School of Medicine.), Kar-mun Woo MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Olumide Akindutire MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Cassidy Dahn MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Lauren Nash PA (is Senior Physician Assistant, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Inna Leybell MD (is Clinical Assistant Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Yelan Wang MS (is Senior Data Analyst, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Danielle Bayer MS (is Senior Data Analyst, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Jordan Swartz MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Catherine Jamin MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.), Silas W. Smith MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine and Institute for Innovations in Medical Education, New York University Grossman School of Medicine. Please address correspondence to Zoe Grabinski)
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Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.),&nbsp;Yelan Wang MS (is Senior Data Analyst, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.),&nbsp;Danielle Bayer MS (is Senior Data Analyst, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.),&nbsp;Jordan Swartz MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.),&nbsp;Catherine Jamin MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine.),&nbsp;Silas W. Smith MD (is Clinical Associate Professor, Ronald O. Perelman Department of Emergency Medicine, New York University Grossman School of Medicine and Institute for Innovations in Medical Education, New York University Grossman School of Medicine. 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Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher's exact tests.</p></div><div><h3>Results</h3><p>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 (<em>p</em> &lt; 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 (<em>p</em> &lt; 0.001) but not by language (<em>p</em> = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (<em>p</em> = 0.446) or language (<em>p</em> = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education.</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":14835,"journal":{"name":"Joint Commission journal on quality and patient safety","volume":"50 7","pages":"Pages 516-527"},"PeriodicalIF":2.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Joint Commission journal on quality and patient safety","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1553725024000795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

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.

Conclusion

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.

评估急诊科入院回访的结构化审查流程
背景回顾急诊科(ED)入院后的再次就诊情况可以发现改进的机会。将健康公平视角应用于复诊,可以突出护理过渡中的潜在差异。目前还缺乏对这些评估的通用定义或切实可行的框架。作者旨在为这一质量保证(QA)流程开发一种结构化方法,并进行分层公平分析。方法作者开发了一种分类工具,用于识别潜在可预防的入院 72 小时复诊(PPRA-72),包括定向、无关、非预期或疾病进展的复诊。第二个评审小组对该工具的可靠性进行了评估。为了尽量减少无法归类的数据,还开发了一种自我报告种族/民族(R/E)和语言算法。使用皮尔逊卡方检验(Pearson chi-square)和费雪精确检验(Fisher's exact tests)对处置分布、回访率和 PPRA-72 分类进行了差异分析。审查小组在二元判定 PPRA-72 与非可预防性回访方面的一致性达到 93% (κ = 0.51)。在急诊室处置方面,R/E 和语言之间存在明显差异(p < 0.001),R/E 白人在指标就诊时入院的频率更高,而其他白人在 72 小时回访时入院的频率更高。不同种族/族裔的 72 小时内复诊率存在显著差异(p < 0.001),但不同语言的 72 小时内复诊率没有显著差异(p = 0.156),其中黑人 72 小时内复诊率最高。在 PPRA-72 比率方面,R/E(p = 0.446)和语言(p = 0.248)之间没有差异。该倡议通过信息学优化、分诊协议、医疗服务提供者反馈和教育来改进系统。这一质量保证流程能够识别出存在差异的领域,从而不断制定下一步措施,确保护理过渡中的健康公平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.80
自引率
4.30%
发文量
116
审稿时长
49 days
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