Adaptation and pilot implementation of a validated, electronic real time clinical decision support tool for care of Pneumonia patients in 12 Utah Urgent Care Centers

项目摘要 临床医生准确诊断肺炎和选择适当治疗的能力得到了加强， 设计的临床决策支持（CDS），从而提高患者的安全性。Pneumonia CDS在历史上 一直专注于住院治疗，但具有高肺炎患者数量的门诊护理环境， 不同的护理过程也需要CDS。我们建议调整和评估一个创新的，经过验证的电子 基于肺炎（ePNa）紧急护理共识指南的临床决策支持（CDS）工具 中心（UCC）。该提案支持四个目标：1）使ePNa适应UCC，并在计算机测试后进行试点 “超级用户”临床医生在UCC轮班期间，并评估其可用性。ePNa需要适应有限的 UCC中可用的患者数据，用于降低观察到的死亡率的严重性测量的校准，以及胸部 影像学提示肺炎患者的体征和症状。用于UCC的ePNa将包含人工 智能CheXpert模型，提供实时（数秒内）的胸部图像电子分类， 肺炎诊断和治疗的要素（放射学肺炎，单叶与多叶，胸膜 积液）。2)使用CFIR框架、UCC临床医生焦点小组和工作流程观察， 适应和实施ePNa以适应UCC的障碍和促进因素。3)测试实施策略 通过在两个随机选择的Intermountain Healthcare UCC集群之一部署ePNa，每个集群约 每年800例肺炎患者-另一个是常规护理对照。4)共同主要结局为a）患者 在ePNa组中，未进行胸部影像学检查而诊断为肺炎的患者将降低≥50%。B）抗生素 肺炎治疗处方与共识指南的一致性≥90%，并且在 ePNa簇。安全性措施将是计划外的后续7天艾德访视/住院和30天 mortality.基于这一严格的初步研究，我们期待随后的多系统随机分组试验 包括犹他州外的Cerner系统。 我们的工作结合了CDS的五项权利，以确保这项技术的优势得到优化， 临床环境。我们将利用创新肺炎研究、开创CDS的经验， Intermountain现有的实施科学，以成功完成该提案。该提案将 通过与太平洋共同体区域办事处网络学习保健的山区间关系促进和传播 系统网络、变革和组织振兴方案CDS学习网络、医疗保健服务平台联盟、Cerner，以及 clinicaltrials.gov. 我们的创新提案有望提高疑似肺炎患者的安全性， 高容量门诊护理环境。

Project Summary Clinicians’ ability to accurately diagnose pneumonia and choose appropriate treatment is enhanced by well- designed clinical decision support (CDS), thereby increasing patient safety. Pneumonia CDS has historically been focused on inpatient settings, but ambulatory care settings with high pneumonia patient volumes and different care processes also need CDS. We propose to adapt and evaluate an innovative, validated electronic clinical decision support (CDS) tool based on consensus guidelines for pneumonia (ePNa) to urgent care centers (UCC). The proposal supports four aims: 1) Adapt ePNa for UCC and after in silico testing, pilot it among “super user” clinicians during UCC shifts and assess its usability. ePNa needs adaptation for the limited patient data available in UCC, calibration of severity measures for lower observed mortality, and a chest imaging prompt in patients with pneumonia signs and symptoms. ePNa for UCC will incorporate the artificial intelligence CheXpert model to provide real-time (within seconds) electronic classification of chest images for elements of pneumonia diagnosis and treatment (radiographic pneumonia, single vs multiple lobes, and pleural effusion). 2) Use the CFIR framework, a focus group of UCC clinicians, and workflow observations to identify barriers and facilitators to adaptation and implementation of ePNa to UCC. 3) Test the implementation strategy by deploying ePNa at one of two randomly chosen Intermountain Healthcare UCC clusters each with about 800 annual pneumonia patients - the other a usual care control. 4) Co-primary outcomes are a) Patients diagnosed with pneumonia without chest imaging will be ≥50% lower in the ePNa cluster. b) Antibiotic prescribing for treatment of pneumonia will be ≥90% consistent with consensus guidelines and higher in the ePNa cluster. Safety measures will be unplanned subsequent 7-day ED visits/hospitalizations and 30-day mortality. Based on this rigorous pilot study, we anticipate a subsequent multi-system cluster-randomized trial including Cerner systems outside Utah. Our work incorporates the Five Rights of CDS to ensure that the strengths of this technology are optimized in the clinical environment. We will leverage experience in innovative pneumonia research, pioneering CDS, and implementation science available at Intermountain to successfully complete this proposal. The proposal will be facilitated by and disseminated through Intermountain relationships with the PCORnet Learning Health Systems Network, PCOR CDS Learning Network, the Healthcare Services Platform Consortium, Cerner, and clinicaltrials.gov. Our innovative proposal promises to advance safety for patients suspected of pneumonia in an understudied, high-volume ambulatory care setting.