Suppr超能文献

使用一线护士主导的 EHR 集成临床决策支持工具减少急性呼吸道感染抗生素的处方:一项 stepped wedge 随机对照试验方案。

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial.

机构信息

NYU Grossman School of Medicine, New York, NY, USA.

University of Utah Health, Salt Lake City, UT, USA.

出版信息

BMC Med Inform Decis Mak. 2023 Nov 14;23(1):260. doi: 10.1186/s12911-023-02368-0.

Abstract

BACKGROUND

Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model.

METHODS

Following qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout.

DISCUSSION

This study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .

摘要

背景

在门诊环境中,过度开具抗生素治疗急性呼吸道感染(ARI)仍然是一个主要问题。使用临床预测规则(CPR)可以减少不适当的抗生素处方,但医生和高级执业护士的使用率仍然较低。电子病历(EMR)整合的低危 ARI 临床预测规则(iCPR)由注册护士(RN)主导的模式可能是解决以医生为导向模式的障碍的有效替代方法。

方法

在进行定性可用性测试之后,我们将进行一项阶梯式实践水平的群组随机对照试验(RCT),以检验 iCPR 指导的 RN 护理对低危 ARI 患者的效果。要检验的主要假设是:实施 RN 主导的 iCPR 工具将减少不同初级保健环境中的抗生素处方。具体来说,本研究旨在:(1)确定 iCPR 对 RN 使用时快速链球菌检测和胸部 X 光检查的订单和抗生素处方率的影响;(2)研究不同临床环境下 RN 就诊的资源利用模式和成本效益;(3)确定 iCPR 指导护理对患者满意度的影响;(4)确定干预对 RN 和医生倦怠的影响。

讨论

本研究代表了一种使用 iCPR 模型的创新方法,该模型由 RN 主导,专门用于解决不适当的抗生素处方问题。本研究有可能为将低危 ARI 患者的护理委托给 RN 以增加 iCPR 的使用并减少门诊环境中 ARI 的抗生素过度处方提供指导。

试验注册

ClinicalTrials.gov 标识符:NCT04255303,注册日期:2020 年 2 月 5 日,https://clinicaltrials.gov/ct2/show/NCT04255303。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/865f/10644670/6c82f8fd2176/12911_2023_2368_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验