Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design.
Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design.
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使用流动中断检查机器人辅助手术中的系统安全性：阶梯楔形交叉设计协议。
DOI：
10.2196/25284
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发表时间：
2021-02-09
影响因子：
1.7
通讯作者：
Catchpole K
中科院分区：
文献类型：
--
作者：
Alfred MC;Cohen TN;Cohen KA;Kanji FF;Choi E;Del Gaizo J;Nemeth LS;Alekseyenko AV;Shouhed D;Savage SJ;Anger JT;Catchpole K
将高科技融入卫生保健系统的目的是提供新的治疗选择，提高护理质量、安全性和效率。机器人辅助手术是医疗保健中高科技集成的一个例子，在许多外科学科中已经无处不在。本研究旨在以系统，定量和可复制的方式了解和测量当前的机器人辅助手术过程，以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年的研究将遵循人为因素工程方法，以提高美国4家医院机器人辅助手术的安全性和效率。该研究采用了一种阶梯式楔形交叉设计，其中有3种干预措施，在4个8个月的阶段内，在每家医院以不同的顺序引入。将在以下专科观察机器人辅助手术：泌尿妇科学、妇科学、泌尿科、减肥科、普通科和结直肠。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提供信息。我们打算评估对每种干预措施的态度、安全文化、每种病例的主观工作量、每种干预措施的有效性（包括通过直接观察每个观察阶段的手术样本）、手术室持续时间、住院时间和患者安全事件报告。分析方法包括统计数据分析、点过程分析和主题内容分析。该研究于2018年9月获得资助，并于2019年5月和6月获得每家机构的机构审查委员会批准（CSMC和MDRH：Pro 00056245; VCMC：研究270; MUSC：Pro 00088741）。在完善第1阶段的3项干预措施后，第2阶段的数据收集（基线数据）于2019年11月开始，计划持续到2020年6月。然而，由于COVID-19疫情，数据收集于二零二零年三月暂停。我们在大流行前在4个地点收集了总共65个观察结果。第二阶段的数据收集于2020年10月在4个研究中心中的2个恢复。这将是有史以来最大的直接观察性手术研究，收集了4个不同机构的680例机器人手术的数据。将使用个人水平（工作量和态度）、过程水平（围手术期持续时间和血流中断）和组织水平（安全文化和并发症）指标评价拟定的干预措施。实施科学框架也被用来调查每个地点每项干预措施成功或失败的原因，并了解干预措施的潜在传播。DERR1-10.2196/25284
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. 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. 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. 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. 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. DERR1-10.2196/25284
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DOI：
10.1186/1748-5908-4-50
发表时间：
2009-08-07
期刊：
Implementation science : IS
影响因子：
--
作者：
Damschroder LJ;Aron DC;Keith RE;Kirsh SR;Alexander JA;Lowery JC
通讯作者：
Lowery JC
影响因子：
7.2
作者：
Anger, Jennifer T.;Mueller, Elizabeth R.;Kenton, Kimberly
通讯作者：
Kenton, Kimberly
DOI：
10.1007/s00464-015-4671-2
发表时间：
2016-09-01
影响因子：
3.1
作者：
Catchpole, Ken;Perkins, Colby;Anger, Jennifer T.
通讯作者：
Anger, Jennifer T.
DOI：
10.1016/j.jamcollsurg.2012.02.005
发表时间：
2012-07-01
影响因子：
5.2
作者：
Anderson, Jamie E.;Chang, David C.;Talamini, Mark A.
通讯作者：
Talamini, Mark A.
影响因子：
2.2
作者：
Catchpole, Ken R;Dale, Trevor J;Giddings, Tony A E B
通讯作者：
Giddings, Tony A E B