PP13 Utilising in-situ simulation and failure modes and effects analysis techniques to prepare a maternity hospital and neonatal intensive care unit for preterm delivery via emergency caesarean section in a pregnant woman with suspected covid-19

Sebastian Brown, M. Nash
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Abstract

Introduction During the emergence of the Covid-19 pandemic, our tertiary maternity hospital was rapidly preparing strategies to manage expected dramatic changes to practice. One of the most complex medical emergencies is the ‘Category 1’ Emergency Caesarean Section, which requires a multitude of professional teams (midwives, obstetricians, anaesthetists and theatre teams, and neonatologists). A preterm delivery of a Covid-19-positive woman could increase risks to not only to mother and baby, but also staff members. We looked at using Human Factors methodology in anticipatory planning. FMEA (Failure Modes and Effects Analysis) was an attractive tool for a novel challenge, as it uses an expert team-based approach to consider a process to mitigate potential risk.1 FMEA has been used across medical specialties, with evidence supporting harm-reduction (including in NICU therapeutics2 3), as well as in identifying potential risk in obstetric emergencies, using simulated scenarios [4]. Methods We created a standard pre-term, Category 1 Emergency Section (under General Anaesthetic) scenario. To ensure maximal learning and relevance, we engaged senior members of all key specialities, by first running a ‘walk-through’ pre-simulation brief where we followed the patient journey from hospital entrance to recovery/neonatal unit. Once we had considered tentative new-style pathways within our teams, we ran a formal high-fidelity in-situ simulation, with volunteers for all staff roles, as well as a senior representative from each speciality to observe each stage. Following on from the simulation, the volunteer actors were summarily debriefed by their individual specialty leads, who then attended a post-simulation cross-specialty meeting to discuss the simulation chronologically, where we identified potential challenges or barriers to the ideal running of a future scenario. Outcomes This debrief formed the basis of the putative, streamlined, FMEA. Using discussion and analysis of raised issues, estimate RPNs (Risk Prioritisation Numbers) were calculated to allow a hierarchy of problems by combining risk of occurrence, likelihood of detection and severity. Expert opinions identified strategies to improve critical systems processes. Being aware of ‘Covid-19 information overload’ we carefully disseminated department-relevant key learning points, updated trust SOPs (Standard Operating Procedures), and developed Human Factors-based tools, such as pre-made equipment boxes, checklists and visual guides (eg. PPE posters/videos). Small-group in-situ simulation teaching was used to embed new practices. Conclusion This highlights the critical role of in-situ simulation for stress-testing hospital systems for novel challenges, and how to combine established Human Factors methods, such as FMEA, to maximise future patient safety.
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PP13利用现场模拟和失效模式及影响分析技术,为一名疑似covid-19孕妇紧急剖腹产早产做好妇产医院和新生儿重症监护病房的准备
在2019冠状病毒病大流行期间,我们的三级妇产医院正在迅速制定战略,以应对预期的巨大变化。最复杂的医疗紧急情况之一是“第一类”紧急剖腹产,它需要众多专业团队(助产士、产科医生、麻醉师和手术室团队以及新生儿专家)。一名covid -19阳性妇女的早产不仅会增加母亲和婴儿的风险,还会增加工作人员的风险。我们研究了在预期计划中使用人为因素方法。失效模式和影响分析(Failure Modes and Effects Analysis, FMEA)是一种有吸引力的工具,可以应对新的挑战,因为它使用基于专家团队的方法来考虑一个过程,以降低潜在风险FMEA已被用于医学专业,有证据支持减少伤害(包括新生儿重症监护室治疗s2 3),以及通过模拟场景识别产科急诊的潜在风险[4]。方法:我们创建了一个标准的早产,第一类急诊科(全身麻醉下)的场景。为了确保最大程度的学习和相关性,我们聘请了所有关键专业的高级成员,首先进行了“演练”预模拟简报,我们跟踪了患者从医院入口到康复/新生儿病房的旅程。一旦我们在我们的团队中考虑了尝试性的新型路径,我们就运行了一个正式的高保真的现场模拟,所有员工角色都有志愿者,每个专业的高级代表都来观察每个阶段。在模拟之后,志愿者演员由他们各自的专业领导简要汇报,然后他们参加了模拟后的跨专业会议,按时间顺序讨论模拟,在那里我们确定了未来场景理想运行的潜在挑战或障碍。结果:这次汇报形成了假定的、简化的FMEA的基础。通过对提出问题的讨论和分析,估算rpn(风险优先级编号)被计算出来,通过结合发生风险、检测可能性和严重程度来允许问题的层次结构。专家意见确定了改进关键系统流程的策略。意识到“Covid-19信息超载”,我们认真传播了与部门相关的关键学习要点,更新了信任标准操作程序(sop),并开发了基于人为因素的工具,如预制设备盒、清单和可视化指南(例如:PPE海报/视频)。采用小组现场模拟教学嵌入新实践。这突出了原位模拟在医院系统压力测试中的关键作用,以及如何结合既定的人为因素方法,如FMEA,以最大限度地提高未来患者的安全。
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BMJ Simulation & Technology Enhanced Learning
BMJ Simulation & Technology Enhanced Learning HEALTH CARE SCIENCES & SERVICES-
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