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.
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.
| France, Daniel J; Levin, Scott; Ding, Ru et al. (2016) Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome. J Patient Saf : |
