Procurement characteristics of high- and low-performing OPOs as seen in OPTN/SRTR data.

来自 PUBMED

作者:

Lynch RJDoby BLGoldberg DSLee KJCimeno AKarp SJ

展开

摘要:

To meet new Centers for Medicare and Medicaid Services (CMS) metrics, organ procurement organizations (OPOs) will benefit from understanding performance across decedent and hospital types. We sought to determine the utility of existing data-reporting structures for this purpose by reviewing Scientific Registry of Transplant Recipient (SRTR) OPO-Specific Reports (OSRs) from 2013 to 2019. OSRs contain both the Standardized donation rate ratio (SDRR) metric and OPO-reported numbers of "eligible deaths" and donors by hospital. Donor hospitals were characterized using information from Homeland Infrastructure Foundation-Level Data, Dartmouth Atlas Hospital Service Area data, and the US Census Bureau. Hospital data reported by OPOs showed 51% higher eligible death donors and 140% higher noneligible death donors per 100 inpatient beds in CMS ranked top versus bottom-quartile OPOs. Top-quartile OPOs by the CMS metric recovered 78% more donors than those in the bottom quartile, but were indistinguishable by SDRR rankings. These differences persisted across hospital sizes, trauma case mix, and area demographics. OPOs with divergent performance were indistinguishable over time by SDRR, but showed changes to hospital-level recovery patterns in SRTR data. Contemporaneous recognition of underperformance across hospitals may provide important and actionable data for regulators and OPOs for focused quality improvement projects.

收起

展开

DOI:

10.1111/ajt.16832

被引量:

7

年份:

1970

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(189)

参考文献(0)

引证文献(7)

来源期刊

-

影响因子:暂无数据

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读