Identifying and Reducing Errors in Perioperative Anesthesia Medication Delivery

识别并减少围手术期麻醉药物输送中的错误

• 批准号: 9982270
• 负责人: James Abernathy
• 金额: $ 58.75万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982270
• 关键词: Identifying; Reducing; Errors; Perioperative; Anesthesia

SUMMARY In the USA every year there are an estimated 200,000 to 2 million anesthetisa medication injuries in the USA with an estimated annual health care cost of $3.1-$46 billion and more than US$300 billion worldwide. Though sensible recommendations have been proposed by professional societies, they generally address preparation and delivery errors (Failures of Execution), with little attention paid to the decision making involved in diagnosing and prescribing (Failures of Intention), and may not address the complexity of the working environment, the physical workspace, or safety culture (Performance Shaping Factors). We will use a multi-disciplinary team of anesthesiologists, human factors professionals, biomedical engineers, pharmacists, and certified registered nurse anesthesiologists to engineer reductions in anesthesia medication errors in operating rooms (ORs) that address all three sources of failure. We will use wire-frame models and rapid prototypes, to develop and evaluate off-the-shelf and novel technologies, process improvements, teamwork, task allocation and management, checklists, clinical decision support, and engineering strategies to enhance performance and to help avoid errors before they happen, trap them before they reach the patient, or mitigate the effects to avoid serious consequences. These will be tested individually and in combination, in simulated and real clinical settings, at two sites, with adult and pediatric populations, in procedures of low and high clinicaly complexity. The Naturalistic Decision Making paradigm, will fame our understanding of this complex multi-factorial, real-work decision- making. Distributed Cognition theory will be used to understand how different information sources integrated cognitively by the anesthesiologist. Task analysis techniques will be used predict procedural weaknesses and develop new processes, while heuristic usability analysis of technology will identify error-producing designs and evaluate new candidates for testing. This will be the most comprehensive study of anesthesia medication safety systems ever conducted, delivered by an highly experienced multi-disciplinary team of clinicans, scientists and engineers, working at the intersection of clinical practice and academic endeavor, using a combination of innovative techniques, approaches and perspectives, across simulated and real environments, to solve one of the most frequently, costly and under-researched threats to safety in acute care.

概括 每年在美国，美国估计有200,000至200万麻醉剂受伤 估计每年的医疗保健费用为31-460亿美元，全球超过3000亿美元。尽管 专业社会提出了明智的建议，他们通常解决准备工作 和交货错误（执行失败），很少关注诊断所涉及的决策 和开处方（意图失败），并且可能无法解决工作环境的复杂性， 物理工作区或安全文化（性能塑造因素）。我们将使用一个多学科团队 麻醉师，人为因素专业人士，生物医学工程师，药剂师和认证注册 护士麻醉医生在手术室（ORS）（ORS）中减少麻醉药物的减少（ORS） 解决所有三个故障来源。我们将使用电线框架模型和快速原型来开发和评估 现成的和新颖的技术，过程改进，团队合作，任务分配和管理， 清单，临床决策支持和工程策略，以提高绩效并帮助避免错误 在发生之前，请在到达患者之前将它们捕获，或减轻效果以避免严重 结果。这些将在模拟和真实的临床环境中进行单独和组合测试，在两个 在低临床复杂性和高临床复杂性的过程中，具有成人和小儿种群的地点。自然主义 决策范式，将为我们对这一复杂的多因素，真实的决策的理解而名称 - 制作。分布式认知理论将用于了解不同的信息源如何整合 麻醉师认知。任务分析技术将用于预测程序弱点和 开发新的流程，而技术的启发式可用性分析将确定产生错误的设计和 评估新的候选人进行测试。这将是麻醉药物安全性最全面的研究 由经验丰富的多学科团队通过临床家，科学家和 工程师，在临床实践和学术努力的交汇处，结合 跨模拟和真实环境的创新技术，方法和观点，以解决之一 急性护理安全性的最频繁，昂贵和研究不足的威胁。