What are we waiting for? Factors influencing completion times in an academic and peripheral emergency department.

BACKGROUND: A long completion time in the Emergency Department (ED) is associated with higher morbidity and in-hospital mortality. A completion time of more than four hours is a frequently used cut-off point. Mostly, older and sicker patients exceed a completion time of four hours on the ED. The primary aim was to examine which factors currently contribute to overcrowding and a time to completion of more than four hours on the EDs of two different hospitals, namely: the VU Medical Center (VUmc), an academic level 1 trauma centre and the St. Antonius Hospital, a large community hospital in Nieuwegein. In addition, we compared the differences between these hospitals. METHODS: In this observational study, the time steps in the process of diagnosing and treatment of all patients visiting the EDs of the two hospitals were measured for four weeks. Patients triaged as Emergency Severity Index (ESI) category 2/3 or Manchester Triage System (MTS) orange/yellow were followed more closely and prospectively by researchers for detailed information in the same period from 12.00-23.00 hrs. RESULTS: In the VUmc, 89% of the patients had a completion time of less than four hours. The average completion time (n = 2262) was 2:10 hours, (median 1:51 hours, range: 0:05-12:08). In the St. Antonius Hospital, 77% of patients had a completion time shorter than four hours (n = 1656). The average completion time in hours was 2:49 (n = 1655, median 2:34, range: 0:08-11:04). In the VUmc, a larger percentage of ESI 1, 2 and 3 patients did not achieve the 4-hour target (14%, 20% and 19%) compared with ESI 4 and 5 patients (2.7% and 0%), p < 0.001. At the St. Antonius Hospital, a greater percentage of orange and yellow categorised patients exceeded four hours on the ED (32% and 28%) compared with red (8%) and green/blue (13%), p < 0.001. For both hospitals there was a significant dependency between exceeding four hours on the ED and the following: whether a consultation was performed (p < 0.001), the number of radiology tests performed (p < 0.001), and an age above 65 years. CONCLUSION: Factors leading to ED stagnation were similar in both hospitals, namely old age, treatment by more than one speciality and undergoing radiological tests. Uniform remedial measures should be taken on a nationwide level to deal with these factors to reduce stagnation in the EDs.

Analysing completion times in an academic emergency department: coordination of care is the weakest link.

Congestion with prolonged stay in the emergency department (ED) is associated with poor health outcomes. Many factors contribute to ED congestion. This study investigates the length of time spent in the ED (time to completion) and the factors contributing to prolonged stay in an academic ED. Data of ED patients were prospectively collected during four weeks in February 2010. Presentation time, referrer, discharge destination, and medical specialities involved were registered in 2510 patients. Additional detailed data about relevant time steps were collected from 66 patients in the triage category Emergency Severity Index (ESI) 3. The Pearson's chi-square test and the Mann-Whitney test were used for statistical analysis. Time to completion was longer than four hours in 13% of patients (average in total population 2:23 hours). In ESI 3 patients, 24% stayed longer than four hours in the ED (p<0.001). Internal medicine had most patients exceeding the four-hour target (37%), followed by neurology (29%). Undergoing a CT scan, treatment by multiple specialities, age above 65 years and hospital admission were associated with exceeding the four-hour target (p<0.001). The elapsed time between receiving test results and admission/discharge also influenced the completion time (p<0.001). A significant percentage of vulnerable and ill patients with triage category ESI 3 exceeded the four-hour completion time in our ED. Absence of coordination of care when multiple specialists were involved and delay in the process of decision-making after completion of all diagnostics on the ED were among other factors responsible for this prolonged stay. Improving the coordination of care will, in our opinion, speed up the decision-making process and lead to shortening of completion times in many patients.

Improving CT scan capabilities with a new trauma workflow concept: simulation of hospital logistics using different CT scanner scenarios.

INTRODUCTION: The Amsterdam Trauma Workflow (ATW) concept includes a sliding gantry CT scanner serving two mirrored (trauma) rooms. In this study, several predefined scenarios with a varying number of CT scanners and CT locations are analyzed to identify the best performing patient flow management strategy from an institutional perspective on process quality. MATERIALS AND METHODS: A total of six clinically relevant scenarios with variables that included the number of CT scanners, CT scanner location, and different patient categories (regular, urgent, and trauma patients) were evaluated using computer simulation. Each scenario was simulated using institutional data and was assessed for patient waiting times, idle time of CT scanners, and overtime due to scheduling. The best 2- and 3-scanner scenarios were additionally evaluated with the ATW-concept. RESULTS: Based on institutional data, the best 2-scanner scenario distributes all 3 patient categories over both scanners and plans 4 urgent patients per hour while locating both scanners outside of the trauma room. The best 3-scanner scenario distributes urgent and regular patients over all 3 scanners and trauma patients on only 1 scanner and locates all CT scanners outside of the trauma room. The ATW concept reduces waiting times and overtime, while increasing idle time. CONCLUSION: Choosing the optimal planning and distribution strategies depends on the number and location of available CT scanners, along with number of trauma, urgent and regular patients. The Amsterdam Trauma Workflow concept could provide institutions with the ability of early CT scanning in trauma patients without influencing regular and urgent CT scanning.

Modeling the emergency cardiac in-patient flow: an application of queuing theory.

This study investigates the bottlenecks in the emergency care chain of cardiac in-patient flow. The primary goal is to determine the optimal bed allocation over the care chain given a maximum number of refused admissions. Another objective is to provide deeper insight in the relation between natural variation in arrivals and length of stay and occupancy rates. The strong focus on raising occupancy rates of hospital management is unrealistic and counterproductive. Economies of scale cannot be neglected. An important result is that refused admissions at the First Cardiac Aid (FCA) are primarily caused by unavailability of beds downstream the care chain. Both variability in LOS and fluctuations in arrivals result in large workload variations. Techniques from operations research were successfully used to describe the complexity and dynamics of emergency in-patient flow.