Computer Simulation of Acute Coronary Syndrome Care in the Emergency Department

描述(申请人提供)：这项研究的主要目标是使用排队理论--排队研究--和计算机模拟，以确定医院和急诊科(ED)之间的紧密耦合如何影响急诊科中时间紧迫的心脏护理。这项研究旨在解释急诊和医院资源需求的变化如何造成服务延误(放射科、实验室和会诊)和病人流动的瓶颈，以及这些因素如何导致怀疑患有急性冠脉综合征(ACS)的患者的诊断和治疗中的表现错误。急性冠脉综合征是一个总括的术语，包括所有符合急性心肌缺血的临床表现。由于循证护理延迟交付会影响急性冠脉综合征患者的预后，这项研究将把绩效错误定义为护理交付过程中可避免的延迟。每年，胸痛导致超过530万次急诊和100多万次住院治疗，原因是急性心肌梗死(AMI)，这是美国的主要死亡原因，通常发生在患者入院之前。急性冠脉综合征是未来急性心肌梗死的有力预测指标，临床研究表明，快速的危险分层和及时的治疗对急性冠脉综合征患者的良好预后至关重要。美国心脏病学会(ACC)、美国心脏协会(AHA)和其他机构已经制定并认可了急性冠脉综合征的实践指南，其中许多强调护理的时间维度。尽管越来越多的临床证据支持这些指南，但许多急诊室未能达到这些循证标准。[研究假设是，与急性冠脉综合征质量指标(QI)相关的等待时间分布更多地受到急诊室和医院工作流程中人为(即人为)变异性的影响，而不是急诊室患者到达人数和临床因素中的自然变异性。这项计划将对入院的疑似急性冠脉综合征的成年患者进行回顾性队列研究。具体目标是：1)重建35个月期间所有疑似ACS患者的急诊护理的时间进程；2)使用系统排队度量来表征在ACS患者护理的每个离散事件期间对急诊和医院系统资源的并发需求；3)使用COX比例风险回归来模拟患者特征(即临床和人口学)、辅助服务利用(例如心电图)、人员配备以及表征急诊和医院患者流动的系统排队度量对时间依赖的ACS QI的影响；4)开发和验证医院心脏护理系统的基于证据的离散事件仿真(DES)模型，以促进我们对ACS性能错误的理解，并促进旨在消除错误和延迟的临床改进的预测性(即，“如果”)分析。将为每个ACS处理间隔(即事件发生的时间)开发COX模型。这些模型将作为函数被编程到适用的DES实体(即，患者、实验室、X光等)流逻辑中，以计算每个护理过程间隔(ED到达到心电等)的等待时间分布。模拟方法将用于前瞻性地测试旨在提高急诊室心脏护理及时性的系统干预措施]。公共卫生相关性：该项目与公共卫生相关，因为它旨在确定阻碍或阻止急诊科(ED)临床医生遵守急性冠脉综合征循证实践指南的医院系统障碍。这项研究的一个主要组成部分将集中在ED拥挤的影响，这是一个全国性但研究不足的问题，对紧急心脏护理的影响。这项研究具有创新性，因为它将使用系统工程工具来补充传统的统计方法，以建模患者和医疗保健系统之间复杂的动态相互作用。

DESCRIPTION (provided by applicant): The primary objective of this study is to use queuing theory - the study of waiting lines - and computer simulation, to determine how tight coupling between hospitals and emergency departments (ED) affects time-critical cardiac care in the ED. The study aims to explain how variability in the demand for ED and hospital resources creates service delays (radiology, laboratory, and consults) and bottlenecks in patient flow, and how these factors lead to performance errors in the diagnosis and treatment of patients suspected of having acute coronary syndrome (ACS). ACS is an umbrella term that includes all clinical findings consistent with acute myocardial ischemia. Because delays in delivery of evidence-based care affects outcome in ACS, this study will define performance errors as avoidable delays in care delivery. Chest pain prompts over 5.3 million ED visits and more than 1 million hospitalizations annually for acute myocardial infarction (AMI), the leading cause of death in the U.S., often occurring before the patient is admitted to the hospital. ACS is a strong predictor of future AMI, and clinical research has shown that rapid risk stratification and timely treatment are critical to favorable outcomes in ACS patients. The American College of Cardiology (ACC), the American Heart Association (AHA), and others have developed and endorsed practice guidelines for ACS, many of which emphasize the temporal dimension of care. Despite growing clinical evidence supporting these guidelines, many EDs fail to meet these evidence-based standards. [The study hypothesis is that wait time distributions associated with ACS quality indicators (QI) are influenced more by artificial (i.e., man-made) variability in ED and hospital work processes than by natural variability in ED patient arrivals and clinical factors. This project will use a retrospective cohort study of adult patients admitted to the ED with suspected ACS. The specific aims are to: 1) Recreate the time-course of ED care for all suspected ACS patients during a 35-month period; 2) Use system queuing metrics to characterize concurrent demands placed on ED and hospital system resources during each discrete episode of ACS patient care; 3) Use Cox proportional hazards regression to model the effects of patient characteristics (i.e., clinical and demographic), ancillary service utilization (e.g., ECG), staffing provisions, and system queuing metrics characterizing ED and hospital patient flow on time-dependent ACS QIs; 4) Develop and validate an evidence-based discrete-event simulation (DES) model of the hospital cardiac care system to advance our understanding of ACS performance errors and to facilitate predictive (i.e., "what if") analyses of clinical improvements aimed at eliminating errors and delays. A Cox model will be developed for each ACS process interval (i.e., time to event). These models will be programmed as functions into the applicable DES entity (i.e., patient, lab, X-ray, etc) flow logic to compute wait-time distributions for each care process interval (ED arrival-to-ECG, etc). The simulation methodology will be used to prospectively test system interventions designed to improve the timeliness of cardiac care in the ED]. Public Health Relevance: This project is relevant to public health because it aims to identify hospital system barriers that hinder or prevent emergency department (ED) clinicians from adhering to evidence-based practice guidelines for acute coronary syndrome. A major component of the research will focus on the impact of ED crowding, a national but understudied problem, on emergency cardiac care. The research is innovative because it will use system engineering tools to complement conventional statistical methods in modeling complex dynamic interactions between patients and the healthcare care system.