REST-PSLL: Re-engineering Surgical Recovery and Transitions Using Technology Patient Safety Learning Laboratory

项目摘要/摘要： 急诊普通外科(EGS)--占美国入院人数的11%；手术人数的50%以上 死亡率；普通外科手术的最高风险、敏锐度、并发症发生率和费用--尤其是 急诊剖腹手术(EL)患者的EGS结局最差，越来越被认为是一种 公共卫生危机。虽然增强的恢复计划(ERP)极大地提升了 住院患者术后循证最佳实践(EBPs)在很大程度上忽视了随后的 术后居家康复。尽管某些术后并发症经常发生在 出院(尤其是超过40%的手术部位感染[SSI])，手术患者一旦出院即可康复 家庭仍然没有得到充分的研究，需要改进和创新的时机已经成熟，特别是作为外科护理模式 越来越多地将更大比例的术后恢复转移到家庭环境中。数字解决方案 对于远程监控，患者参与/教育和手术预测显示出希望，但仍在使用 最小限度地用于手术恢复。我们假设为El Home精心设计的技术解决方案--以及 让不同利益相关者参与的基于过渡的工作流程将增强El患者、他们的护理人员和护理人员的能力 这是一项重要的国际合作，旨在帮助团队实现最佳结果和安全恢复。REST-PSLL(外科再工程)的目标 使用技术恢复和过渡患者安全学习实验室)是应用系统 共同为EL提供以病人和护理团队为中心的创新且可扩展的解决方案的工程方法 基于家庭的康复。REST-PSLL有以下具体目标。目标1：确定障碍和促进者 对于EL后的患者来说，理想的居家康复。我们将利用系统工程吸引利益相关者 为患者安全(SEIPS)模式识别和评估因素，以了解障碍和促进者 以SSI为重点的最佳护理和术后并发症。目标2：迭代设计和开发新的 有助于消除或减少实现理想居家恢复的障碍的流程、工具和技术。我们将使用 泳道示意图，然后分析需要改进的区域，以生成初始设计要求和 工具/技术的原型，遵循迭代的以用户为中心的设计，以指定设计要求； 开发解决方案，并在我们的临床站点进行最终用户测试。目标3：实施和评估解决方案 在实践中。执行试行测试和实施，评估主要问题(解决或避免触发/潜在问题 在RE-AIM框架指导下的问题、解决方案)和次要成果；评估实施阶段 在EPIS实施框架的指导下，生成实施学习和解决方案修订计划(S)。 REST-PSLL准备非常成功和有影响力，突出表现在：a)预期的学习可以 外推到其他外科人群；b)利用跨学科的补充系统专门知识和 方法；c)深厚的临床专业知识；d)患者和护理者共同制作；e)扩大或 优化现有解决方案/平台。

PROJECT SUMMARY/ABSTRACT: Emergency general surgery (EGS) - accounting for 11% of US hospital admissions; over 50% of operative mortality; the highest risk, acuity, rate of complications, and cost in general surgery - particularly the subset of emergency laparotomy (EL) patients which suffer the worst EGS outcomes - is increasingly recognized as a public health crisis. While enhanced recovery programs (ERPs) have dramatically advanced pre-, intra-, and inpatient postoperative evidenced-based best practices (EBPs), ERPs have largely ignored subsequent postoperative home-based recovery. Despite certain postoperative complications occurring commonly after discharge (notably over 40% of surgical site infections [SSI]), recovery for surgical patients once discharged to home remains understudied and ripe for improvement and innovation, especially as surgical care models increasingly shift greater proportions of postoperative recovery into the home-based setting. Digital solutions for remote monitoring, patient engagement/education, and surgical prediction show promise but are used minimally for surgical recovery. We hypothesize that well-designed technology solutions for EL home- and transition-based workflows engaging diverse stakeholders will empower EL patients, their caregivers, and care teams to achieve optimal outcomes and safe recovery. The goal of REST-PSLL (Re-engineering Surgical Recovery and Transitions Using Technology Patient Safety Learning Laboratory) is to apply systems engineering approaches to co-produce innovative and scalable patient- and care team-centric solutions for EL home-based recovery. REST-PSLL has the following specific aims. Aim 1: Identify barriers and facilitators to ideal at-home recovery for patients following EL. We will engage stakeholders using the Systems Engineering for Patient Safety (SEIPS) model to identify and evaluate factors to understand barriers and facilitators of optimal care and postoperative complications with a focus on SSI. Aim 2: Iteratively design and develop new processes, tools, and technologies that help remove or reduce barriers to ideal at-home recovery. We will use swimlane diagrams to then analyze for areas of improvement to generate initial design requirements and prototypes for tools/technologies followed by iterative user-centered design to specify design requirements, develop solutions, and conduct end-user testing at our clinical sites. Aim 3: Implement and evaluate solutions in practice. Perform pilot testing and implementation, assess primary (addressing or avoiding trigger/potential issue, solution us) and secondary outcomes guided by RE-AIM framework; evaluate implementation phases guided by EPIS implementation framework to generate implementation learnings and solution revision plan(s). REST-PSLL is poised to be highly successful and impactful, highlighted by: a) anticipated learnings that can be extrapolated to other surgical populations; b) use of transdisciplinary complementary systems expertise and approaches; c) deep clinical expertise; d) patient- and caregiver- co-production; and e) expanding or optimizing existing solutions/platforms.