他们应该留下还是离开?自动图像解读和通信平台实施前后医院网络的卒中转院情况。

IF 1.5 4区 医学 Q4 CLINICAL NEUROLOGY
James Bonner, Christopher J Love, Vipul Bhat, James E Siegler
{"title":"他们应该留下还是离开?自动图像解读和通信平台实施前后医院网络的卒中转院情况。","authors":"James Bonner, Christopher J Love, Vipul Bhat, James E Siegler","doi":"10.1177/15910199241272652","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>A key decision facing nonthrombectomy capable (spoke) hospitals is whether to transfer a suspected large vessel occlusion (LVO) patient to a comprehensive stroke center (CSC). In a retrospective cohort study, we investigated the rate of transfers resulting in endovascular thrombectomy (EVT) and associated costs before and after implementation of an artificial intelligence (AI)-based software.</p><p><strong>Methods: </strong>All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.</p><p><strong>Results: </strong>A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).</p><p><strong>Conclusions: </strong>The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.</p>","PeriodicalId":49174,"journal":{"name":"Interventional Neuroradiology","volume":" ","pages":"15910199241272652"},"PeriodicalIF":1.5000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571365/pdf/","citationCount":"0","resultStr":"{\"title\":\"Should they stay or should they go? Stroke transfers across a hospital network pre- and post-implementation of an automated image interpretation and communication platform.\",\"authors\":\"James Bonner, Christopher J Love, Vipul Bhat, James E Siegler\",\"doi\":\"10.1177/15910199241272652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>A key decision facing nonthrombectomy capable (spoke) hospitals is whether to transfer a suspected large vessel occlusion (LVO) patient to a comprehensive stroke center (CSC). In a retrospective cohort study, we investigated the rate of transfers resulting in endovascular thrombectomy (EVT) and associated costs before and after implementation of an artificial intelligence (AI)-based software.</p><p><strong>Methods: </strong>All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.</p><p><strong>Results: </strong>A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).</p><p><strong>Conclusions: </strong>The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.</p>\",\"PeriodicalId\":49174,\"journal\":{\"name\":\"Interventional Neuroradiology\",\"volume\":\" \",\"pages\":\"15910199241272652\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571365/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interventional Neuroradiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15910199241272652\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interventional Neuroradiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15910199241272652","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:无血栓切除能力(辐条)医院面临的一个关键决策是是否将疑似大血管闭塞(LVO)患者转至综合卒中中心(CSC)。在一项回顾性队列研究中,我们调查了实施基于人工智能(AI)软件前后导致血管内血栓切除术(EVT)的转院率及相关费用:方法:所有最终诊断为急性缺血性卒中的患者均被纳入一个隶属于 CSC 的五辐社区医院网络。Viz LVO(Viz.ai, Inc.)软件在各辐条上实施,并在各医疗机构之间共享图像和发送信息。在实施前(AI 前,2018 年 12 月至 2020 年 10 月)和实施后(AI 后,2020 年 10 月至 2022 年 8 月)的患者队列中,我们比较了从医疗系统转出至 CSC 的缺血性卒中患者的 EVT 率。次要结果包括基于轮辐计算机断层扫描血管造影(CTA)的 EVT 率和估计的转运成本:共有 3113 名符合条件的患者(平均年龄 71 岁,50% 为女性)在辐条医院就诊,其中 162 人在 AI 前转院,127 人在 AI 后转院。经 EVT 治疗的转院率明显增加(AI 前为 32.1%,AI 后为 45.7%,P = 0.02)。AI 后,辐照医院对所有患者和转院患者使用 CTA 的比例急剧上升,这可能是 EVT 转院率上升的原因之一,但仅凭辐照医院之前使用 CTA 并不足以解释 EVT 转院率的所有改善(AI 前为 37.2%,AI 后为 49.2%,P = 0.12)。在二元逻辑回归模型中,干预期间发生 EVT 转运的几率比干预前高 1.85(调整后几率比 1.85,95% 置信区间 1.12-3.06)。非EVT转院的减少估计每年可带来206,121美元的辐照收入和119,921美元的支付方节省(均为美元):结论:自动化图像解读和交流平台的实施与 CTA 使用率的提高、更多转院患者接受 EVT 治疗以及潜在的经济效益有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Should they stay or should they go? Stroke transfers across a hospital network pre- and post-implementation of an automated image interpretation and communication platform.

Background: A key decision facing nonthrombectomy capable (spoke) hospitals is whether to transfer a suspected large vessel occlusion (LVO) patient to a comprehensive stroke center (CSC). In a retrospective cohort study, we investigated the rate of transfers resulting in endovascular thrombectomy (EVT) and associated costs before and after implementation of an artificial intelligence (AI)-based software.

Methods: All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.

Results: A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).

Conclusions: The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Interventional Neuroradiology
Interventional Neuroradiology CLINICAL NEUROLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
3.60
自引率
11.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: Interventional Neuroradiology (INR) is a peer-reviewed clinical practice journal documenting the current state of interventional neuroradiology worldwide. INR publishes original clinical observations, descriptions of new techniques or procedures, case reports, and articles on the ethical and social aspects of related health care. Original research published in INR is related to the practice of interventional neuroradiology...
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信