System dynamics modeling of emergency department and hospital crowding

描述（由申请人提供）：急诊科/医院拥挤是一个主要的公共卫生问题，在各种情况下（例如，急性心脏病护理、老年人护理、肺炎、疼痛管理、儿科护理和公共健康筛查），导致过程和结果指标的退化。拥挤问题显示出“政策阻力”；尽管20年来一直是一个令人担忧的问题，但它仍在继续恶化，目前许多建议都有反馈，使问题陷入恶性循环。由于它的动态复杂性、延迟反馈循环和社会行为成分，这个问题非常适合系统动力学方法。例如，对拥挤的系统动力学理解将有助于：“发展潜在拥挤危机的早期预警能力”确定管理患者需求或组织响应能力的动态和意外变化的杠杆点“确定需要避免的潜在功能失调的干预措施，即可能提供短期缓解，但最终使整体问题变得更糟。本项目的总体目标是使用系统科学方法，特别是系统动力学建模，来研究急诊科（ED）和医院拥挤问题，以便为部门、组织、地区和社会政策和干预措施提供信息，以缓解这一问题。该项目将邀请一个由组织利益相关者和系统科学专家组成的团队，根据一个经历过严重拥挤问题的医疗机构的数据和经验，构建急诊科/医院拥挤问题的系统动态模型。这些模型将侧重于普通操作，也就是说，不是外部灾难场景。然后，我们将使用模型进行探索和实验，以确定特征行为，评估潜在的干预措施，并确定支持弹性性能的杠杆点和关键变量。这些模型将使用急性心脏护理和老年人护理作为具体的“示踪剂”来评估表现。急性心脏护理是这一角色的理想选择，因为它很严重，需要高质量的急诊护理才能获得最佳结果，对拥挤导致的退化很敏感，具有重大的公共卫生利益，是急诊护理工作者和卫生保健组织的核心使命，并且具有良好的过程和结果措施。另一个潜在的示踪条件是对老年人的护理，他们在几个方面受到急诊科/医院拥挤的不成比例的影响（例如，在拥挤的情况下获得护理的机会受损，在走廊或其他非标准护理空间“寄宿”，以及过早出院以腾出空间给新入院者）。因此，它最适合NHLBI作为主要资助者，NIA作为次要资助者。

DESCRIPTION (provided by applicant): Emergency department / hospital crowding is a major public health problem that leads to degradation in both process and outcome measures in a variety of conditions (eg, acute cardiac care, care for the elderly, pneumonia, pain management, pediatric care, and public health screening). The crowding problem has shown "policy resistance"; despite being a source of concern for 20 years, it continues worsen, and many current proposals have feedbacks that exacerbate the problem in a vicious cycle. Because of its dynamic complexity, delayed feedback loops, and social-behavioral components, the problem is ideally suited to a system dynamics approach. For example, a system dynamics understanding of crowding would be useful in: " Developing early warning capabilities of a potential overcrowding crisis " Identifying leverage points for managing dynamic and unexpected changes in patient demand or organizational capacity to respond" Identifying potentially dysfunctional interventions to be avoided, ie, that might provide short term relief but ultimately make the overall problem worse. The broad, overall objective of this project is use a systems science approach, specifically system dynamics modeling, to study the problem of emergency department (ED) and hospital crowding in order to inform departmental, organizational, regional, and societal policies and interventions aimed at alleviating it. The project will engage a team of organizational stakeholders and system science experts to construct system dynamic models of the ED / hospital crowding problem, based on data and experience at a single healthcare organization that has experienced severe crowding problems. These models will focus on ordinary operations, i.e., not external disaster scenarios. We will then use the models for exploration and experimentation to identify characteristic behaviors, evaluate potential interventions, and identify leverage points and critical variables supporting resilient performance. The models will use acute cardiac care and care for the elderly as specific 'tracers' to evaluate performance. Acute cardiac care is ideal for this role, because it is serious, depends on high quality emergency care for optimal results, is sensitive to degradation due to crowding, is of great public health interest, is central to the mission of emergency care workers and health care organizations, and has good process and outcome measures. Another potential tracer condition is care for the elderly, who are disproportionately affected by ED / hospital crowding in several ways (e.g., impaired access to care in crowding situations, 'boarding' in hallways or other non-standard care spaces, and premature discharge from the hospital to make room for new admissions). It would thus be most suitable for the NHLBI as primary funder, and for the NIA as secondary. PUBLIC HEALTH RELEVANCE: ED - hospital crowding affects roughly 110 million Americans per year, and is associated with decreased quality of care and increased morbidity and mortality. Crowding has been increasing over the past 2 decades, despite many efforts to control it. This project will apply a new method to the study of crowding to inform management and policy by better identifying leverage points, and characterizing potentially useful and potentially counterproductive interventions.