Suppr超能文献

利用血流中断评估机器人辅助手术中的系统安全性:阶梯楔形交叉设计方案

Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design.

作者信息

Alfred Myrtede C, Cohen Tara N, Cohen Kate A, Kanji Falisha F, Choi Eunice, Del Gaizo John, Nemeth Lynne S, Alekseyenko Alexander V, Shouhed Daniel, Savage Stephen J, Anger Jennifer T, Catchpole Ken

机构信息

Medical University of South Carolina, Department of Anesthesia and Perioperative Medicine, Charleston, SC, United States.

Cedars-Sinai Medical Center, Department of Surgery, Los Angeles, CA, United States.

出版信息

JMIR Res Protoc. 2021 Feb 9;10(2):e25284. doi: 10.2196/25284.

Abstract

BACKGROUND

The integration of high technology into health care systems is intended to provide new treatment options and improve the quality, safety, and efficiency of care. Robotic-assisted surgery is an example of high technology integration in health care, which has become ubiquitous in many surgical disciplines.

OBJECTIVE

This study aims to understand and measure current robotic-assisted surgery processes in a systematic, quantitative, and replicable manner to identify latent systemic threats and opportunities for improvement based on our observations and to implement and evaluate interventions. This 5-year study will follow a human factors engineering approach to improve the safety and efficiency of robotic-assisted surgery across 4 US hospitals.

METHODS

The study uses a stepped wedge crossover design with 3 interventions, introduced in different sequences at each of the hospitals over four 8-month phases. Robotic-assisted surgery procedures will be observed in the following specialties: urogynecology, gynecology, urology, bariatrics, general, and colorectal. We will use the data collected from observations, surveys, and interviews to inform interventions focused on teamwork, task design, and workplace design. We intend to evaluate attitudes toward each intervention, safety culture, subjective workload for each case, effectiveness of each intervention (including through direct observation of a sample of surgeries in each observational phase), operating room duration, length of stay, and patient safety incident reports. Analytic methods will include statistical data analysis, point process analysis, and thematic content analysis.

RESULTS

The study was funded in September 2018 and approved by the institutional review board of each institution in May and June of 2019 (CSMC and MDRH: Pro00056245; VCMC: STUDY 270; MUSC: Pro00088741). After refining the 3 interventions in phase 1, data collection for phase 2 (baseline data) began in November 2019 and was scheduled to continue through June 2020. However, data collection was suspended in March 2020 due to the COVID-19 pandemic. We collected a total of 65 observations across the 4 sites before the pandemic. Data collection for phase 2 was resumed in October 2020 at 2 of the 4 sites.

CONCLUSIONS

This will be the largest direct observational study of surgery ever conducted with data collected on 680 robotic surgery procedures at 4 different institutions. The proposed interventions will be evaluated using individual-level (workload and attitude), process-level (perioperative duration and flow disruption), and organizational-level (safety culture and complications) measures. An implementation science framework is also used to investigate the causes of success or failure of each intervention at each site and understand the potential spread of the interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25284.

摘要

背景

将高科技融入医疗保健系统旨在提供新的治疗选择,并提高医疗服务的质量、安全性和效率。机器人辅助手术是高科技在医疗保健领域融合的一个例子,它在许多外科领域已变得十分普遍。

目的

本研究旨在以系统、定量且可重复的方式理解和衡量当前的机器人辅助手术流程,以便根据我们的观察结果识别潜在的系统性威胁和改进机会,并实施和评估干预措施。这项为期5年的研究将采用人因工程方法,以提高美国4家医院机器人辅助手术的安全性和效率。

方法

该研究采用阶梯楔形交叉设计,包含3种干预措施,在4家医院的每个医院分四个8个月阶段以不同顺序引入。将观察以下专科的机器人辅助手术程序:泌尿妇科、妇科、泌尿外科、减肥外科、普通外科和结直肠外科。我们将使用从观察、调查和访谈中收集的数据,为专注于团队合作、任务设计和工作场所设计的干预措施提供依据。我们打算评估对每种干预措施的态度、安全文化、每个病例的主观工作量、每种干预措施的效果(包括通过直接观察每个观察阶段的一部分手术)、手术室时长、住院时间和患者安全事件报告。分析方法将包括统计数据分析、点过程分析和主题内容分析。

结果

该研究于2018年9月获得资助,并于2019年5月和6月获得各机构机构审查委员会的批准(CSMC和MDRH:Pro00056245;VCMC:研究270;MUSC:Pro00088741)。在第1阶段对3种干预措施进行完善后,第2阶段(基线数据)的数据收集于2019年11月开始,计划持续到2020年6月。然而,由于COVID-19大流行,数据收集于2020年3月暂停。在大流行之前,我们在4个地点共收集了65次观察数据。第2阶段的数据收集于2020年10月在4个地点中的2个地点恢复。

结论

这将是有史以来规模最大的直接观察性手术研究,在4个不同机构收集了680例机器人手术程序的数据。拟议的干预措施将使用个体层面(工作量和态度)、流程层面(围手术期时长和流程中断)和组织层面(安全文化和并发症)的指标进行评估。还将使用实施科学框架来调查每个地点每种干预措施成败的原因,并了解干预措施的潜在传播情况。

国际注册报告识别码(IRRID):DERR1-10.2196/25284。

相似文献

5
6
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.

本文引用的文献

1
Updates in Urologic Robot Assisted Surgery.泌尿外科机器人辅助手术的进展
F1000Res. 2018 Dec 18;7. doi: 10.12688/f1000research.15480.1. eCollection 2018.
2
Framework for direct observation of performance and safety in healthcare.医疗保健中绩效与安全直接观察框架。
BMJ Qual Saf. 2017 Dec;26(12):1015-1021. doi: 10.1136/bmjqs-2016-006407. Epub 2017 Sep 28.
3
Diagnosing barriers to safety and efficiency in robotic surgery.诊断机器人手术中安全性和效率的障碍。
Ergonomics. 2018 Jan;61(1):26-39. doi: 10.1080/00140139.2017.1298845. Epub 2017 Mar 8.
7
Patient safety and the problem of many hands.患者安全与“人多手杂”问题
BMJ Qual Saf. 2016 Jul;25(7):485-8. doi: 10.1136/bmjqs-2016-005232. Epub 2016 Feb 24.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验