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

项目总结/摘要： 急诊普通外科（EGS）-占美国住院人数的11%;超过50%的手术 死亡率;最高的风险，敏锐度，并发症的发生率，以及普通外科手术的费用-特别是 急诊剖腹手术（EL）患者的EGS结果最差-越来越多地被认为是一种 公共卫生危机。虽然增强型恢复计划（ERP）已经大大提高了前，内， 住院患者术后循证最佳实践（EBP），ERP在很大程度上忽略了随后的 术后居家康复尽管某些术后并发症发生后， 出院（特别是超过40%的手术部位感染[SSI]），手术患者一旦出院， 家庭仍然是研究不足和成熟的改进和创新，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 远程监测、患者参与/教育和手术预测显示出前景，但 最低限度的手术恢复。我们假设，精心设计的技术解决方案，为EL家庭-和 涉及不同利益相关者的基于过渡的工作流程将使EL患者，他们的护理人员和护理人员 团队以实现最佳结果和安全恢复。REST-PSLL（Re-engineering Surgical） 恢复和过渡使用技术患者安全学习实验室）是应用系统 工程方法，共同为EL生产创新和可扩展的以患者和护理团队为中心的解决方案 居家康复REST-PSLL具有以下具体目标。目标1：确定障碍和促进因素， 对于EL术后患者来说是理想的在家恢复。我们将利用系统工程， 患者安全（SEIPS）模型，以确定和评估因素，以了解障碍和促进因素， 最佳护理和术后并发症，重点关注SSI。目标2：迭代设计和开发新的 流程、工具和技术，帮助消除或减少实现理想的在家恢复的障碍。我们将使用 泳道图，然后分析改进区域，以生成初始设计要求， 工具/技术的原型，然后是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，并在我们的临床研究中心进行最终用户测试。目标3：实施和评估解决方案 在实践中进行试点测试和实施，评估主要问题（解决或避免触发/潜在问题 问题、解决方案用户）和次级成果;评估实施阶段 在EPIS实施框架的指导下，生成实施经验和解决方案修订计划。 REST-PSLL有望获得高度成功和影响力，突出表现在：a）可以实现的预期学习 外推到其他手术人群; B）使用跨学科互补系统专业知识， 方法; c）深厚的临床专业知识; d）患者和护理人员共同生产;以及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.