Emergency Department Crowding: Factors Influencing Flow

Background: The objective of this study was to evaluate those factors, both intrinsic and extrinsic to the emergency department (ED) that influence two specific components of throughput: “door-to-doctor” time and dwell time.Methods: We used a prospective observational study design to determine the variables that played a significant role in determining ED flow. All adult patients seen or waiting to be seen in the ED were observed at 8pm (Monday-Friday) during a three-month period. Variables measured included daily ED volume, patient acuity, staffing, ED occupancy, daily admissions, ED boarder volume, hospital volume, and intensive care unit volume. Both log-rank tests and time-to-wait (survival) proportional-hazard regression models were fitted to determine which variables were most significant in predicting “door-to-doctor” and dwell times, with full account of the censoring for some patients.Results: We captured 1,543 patients during our study period, representing 27% of total daily volume. The ED operated at an average of 85% capacity (61-102%) with an average of 27% boarding. Median “door-to-doctor” time was 1.8 hours, with the biggest influence being triage category, day of the week, and ED occupancy. Median dwell time was 5.5 hours with similar variable influences.Conclusion: The largest contributors to decreased patient flow through the ED at our institution were triage category, ED occupancy, and day of the week. Although the statistically significant factors influencing patient throughput at our institution involve problems with inflow, an increase in ED occupancy could be due to substantial outflow obstruction and may indicate the necessity for increased capacity both within the ED and hospital. [West J Emerg Med. 2010; 11(1):10-15

Emergency Department Crowding: Factors Influencing Flow

Background: The objective of this study was to evaluate those factors, both intrinsic and extrinsic to the emergency department (ED) that influence two specific components of throughput: “door-to-doctor” time and dwell time.Methods: We used a prospective observational study design to determine the variables that played a significant role in determining ED flow. All adult patients seen or waiting to be seen in the ED were observed at 8pm (Monday-Friday) during a three-month period. Variables measured included daily ED volume, patient acuity, staffing, ED occupancy, daily admissions, ED boarder volume, hospital volume, and intensive care unit volume. Both log-rank tests and time-to-wait (survival) proportional-hazard regression models were fitted to determine which variables were most significant in predicting “door-to-doctor” and dwell times, with full account of the censoring for some patients.Results: We captured 1,543 patients during our study period, representing 27% of total daily volume. The ED operated at an average of 85% capacity (61-102%) with an average of 27% boarding. Median “door-to-doctor” time was 1.8 hours, with the biggest influence being triage category, day of the week, and ED occupancy. Median dwell time was 5.5 hours with similar variable influences.Conclusion: The largest contributors to decreased patient flow through the ED at our institution were triage category, ED occupancy, and day of the week. Although the statistically significant factors influencing patient throughput at our institution involve problems with inflow, an increase in ED occupancy could be due to substantial outflow obstruction and may indicate the necessity for increased capacity both within the ED and hospital. [West J Emerg Med. 2010; 11(1):10-15

Comparing an Unstructured Risk Stratification to Published Guidelines in Acute Coronary Syndromes

Introduction: Guidelines are designed to encompass the needs of the majority of patients with a particular condition. The American Heart Association (AHA) in conjunction with the American College of Cardiology (ACC) and the American College of Emergency Physicians (ACEP) developed risk stratification guidelines to aid physicians with accurate and efficient diagnosis and management of patients with acute coronary syndrome (ACS). While useful in a primary care setting, in the unique environment of an emergency department (ED), the feasibility of incorporating guidelines into clinical workflow remains in question. We aim to compare emergency physicians’ (EP) clinical risk stratification ability to AHA/ACC/ACEP guidelines for ACS, and assessed each for accuracy in predicting ACS.Methods: We conducted a prospective observational cohort study in an urban teaching hospital ED. All patients presenting to the ED with chest pain who were evaluated for ACS had two risk stratification scores assigned: one by the treating physician based on clinical evaluation and the other by the AHA/ACC/ACEP guideline aforementioned. The patient’s ACS risk stratification classified by the EP was compared to AHA/ACC/ACEP guidelines. Patients were contacted at 30 days following the index ED visit to determine all cause mortality, unscheduled hospital/ED revisits, and objective cardiac testing performed.Results: We enrolled 641 patients presenting for evaluation by 21 different EPs. There was a difference between the physician’s clinical assessment used in the ED, and the AHA/ACC/ACEP task force guidelines. EPs were more likely to assess patients as low risk (40%), while AHA/ACC/ACEP guidelines were more likely to classify patients as intermediate (45%) or high (45%) risk. Of the 119 (19%) patients deemed high risk by EP evaluation, 38 (32%) were diagnosed with ACS. AHA/ACC/ACEP guidelines classified only 57 (9%) patients low risk with 56 (98%) of those patients diagnosed with no ACS.Conclusion: In the ED, physicians are more efficient at correctly placing patients with underlying ACS into a high-risk category. A small percentage of patients were considered low risk when applying AHA/ACC/ACEP guidelines, which demonstrates how clinical insight is often required to make an efficient assessment of cardiac risk and established criteria may be overly conservative when applied to an acute care population

Comparing an Unstructured Risk Stratification to Published Guidelines in Acute Coronary Syndromes

Introduction: Guidelines are designed to encompass the needs of the majority of patients with a particular condition. The American Heart Association (AHA) in conjunction with the American College of Cardiology (ACC) and the American College of Emergency Physicians (ACEP) developed risk stratification guidelines to aid physicians with accurate and efficient diagnosis and management of patients with acute coronary syndrome (ACS). While useful in a primary care setting, in the unique environment of an emergency department (ED), the feasibility of incorporating guidelines into clinical workflow remains in question. We aim to compare emergency physicians’ (EP) clinical risk stratification ability to AHA/ACC/ACEP guidelines for ACS, and assessed each for accuracy in predicting ACS. Methods: We conducted a prospective observational cohort study in an urban teaching hospital ED. All patients presenting to the ED with chest pain who were evaluated for ACS had two risk stratification scores assigned: one by the treating physician based on clinical evaluation and the other by the AHA/ACC/ACEP guideline aforementioned. The patient’s ACS risk stratification classified by the EP was compared to AHA/ACC/ACEP guidelines. Patients were contacted at 30 days following the index ED visit to determine all cause mortality, unscheduled hospital/ED revisits, and objective cardiac testing performed. Results: We enrolled 641 patients presenting for evaluation by 21 different EPs. There was a difference between the physician’s clinical assessment used in the ED, and the AHA/ACC/ACEP task force guidelines. EPs were more likely to assess patients as low risk (40%), while AHA/ACC/ ACEP guidelines were more likely to classify patients as intermediate (45%) or high (45%) risk. Of the 119 (19%) patients deemed high risk by EP evaluation, 38 (32%) were diagnosed with ACS. AHA/ACC/ACEP guidelines classified only 57 (9%) patients low risk with 56 (98%) of those patients diagnosed with no ACS. Conclusion: In the ED, physicians are more efficient at correctly placing patients with underlying ACS into a high-risk category. A small percentage of patients were considered low risk when applying AHA/ACC/ACEP guidelines, which demonstrates how clinical insight is often required to make an efficient assessment of cardiac risk and established criteria may be overly conservative when applied to an acute care population