Robert J. Batt

The Financial Consequences of Lost Demand and Reducing Boarding in Hospital Emergency Departments

STUDY OBJECTIVE Some have suggested that emergency department (ED) boarding is prevalent because it maximizes revenue as hospitals prioritize non-ED admissions, which reimburse higher than ED admissions. We explore the revenue implications to the overall hospital of reducing boarding in the ED. METHODS We quantified the revenue effect of reducing boarding-the balance of higher ED demand and the reduction of non-ED admissions-using financial modeling informed by regression analysis and discrete-event simulation with data from 1 inner-city teaching hospital during 2 years (118,000 ED visits, 22% ED admission rate, 7% left without being seen rate, 36,000 non-ED admissions). Various inpatient bed management policies for reducing non-ED admissions were tested. RESULTS Non-ED admissions generated more revenue than ED admissions (

Early Task Initiation and Other Load-Adaptive Mechanisms in the Emergency Department

4,118 versus

Setting wait times to achieve targeted left-without-being-seen rates ☆ ☆☆ ★

2,268 per inpatient day). A 1-hour reduction in ED boarding time would result in

Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits: Do Hendrich II Scores Predict Outpatient Falls?

9,693 to

Waiting Patiently: An Empirical Study of Queue Abandonment in an Emergency Department

13,298 of additional daily revenue from capturing left without being seen and diverted ambulance patients. To accommodate this demand, we found that simulated management policies in which non-ED admissions are reduced without consideration to hospital capacity (ie, static policies) mostly did not result in higher revenue. Many dynamic policies requiring cancellation of various proportions of non-ED admissions when the hospital reaches specific trigger points increased revenue. The optimal strategies tested resulted in an estimated

The Impact of Emergency Department Census on the Decision to Admit.

2.7 million and

Modeling for Insight: A Master Class for Business Analysts

3.6 in net revenue per year, depending on whether left without being seen patients were assumed to be outpatients or mirrored ambulatory admission rates, respectively. CONCLUSION Dynamic inpatient bed management in inner-city teaching hospitals in which non-ED admissions are occasionally reduced to ensure that EDs have reduced boarding times is a financially attractive strategy.

Cherry Picking Patients: Examining the Interval Between Patient Rooming and Resident Self-assignment

We study a multistage service process that adapts to system occupancy level. Using operational data from more than 140,000 patient visits to a hospital emergency department, we show that the system-level performance of the emergency department is an aggregation of several simultaneous server-level workload response mechanisms. We identify early task initiation as a between-stage adaptive response mechanism that occurs when an upstream stage initiates tasks that are normally handled by a downstream stage. We show that having some diagnostic tests ordered during the triage process reduces treatment time by 20 minutes, on average. However, ordering too many tests at triage can lead to an increase in the total number of tests performed on the patient. We also demonstrate the presence of other response mechanisms such as queuing delays for tasks such as medication delivery, and rushing as nurses spend less time with their patients when the queue length is high. This paper was accepted by Serguei Netessine, ope...

Modeling for Insight

BACKGROUND Although several studies have demonstrated that wait time is a key factor that drives high leave-without-being-seen (LWBS) rates, limited data on ideal wait times and impact on LWBS rates exist. STUDY OBJECTIVES We studied the LWBS rates by triage class and target wait times required to achieve various LWBS rates. METHODS We conducted a 3-year retrospective analysis of patients presenting to an urban, tertiary, academic, adult emergency department (ED). We divided the 3-year study period into 504 discrete periods by year, day of the week, and hour of the day. Patients of same triage level arriving in the same bin were exposed to similar ED conditions. For each bin, we calculate the mean actual wait time and the proportion of patients that abandoned. We performed a regression analysis on the abandonment proportion on the mean wait time using weighted least squares regression. RESULTS A total of 143,698 patients were included for analysis during the study period. The R(2) value was highest for Emergency Severity Index (ESI) 3 (R(2) = 0.88), suggesting that wait time is the major factor driving LWBS of ESI 3 patients. Assuming that ESI 2 patients wait less than 10 minutes, our sensitivity analysis shows that the target wait times for ESI 3 and ESI 4/5 patients should be less than 45 and 60 minutes, respectively, to achieve an overall LWBS rate of less than 2%. CONCLUSION Achieving target LWBS rates requires analysis to understand the abandonment behavior and redesigning operations to achieve the target wait times.

Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits.

To evaluate the utility of routinely collected Hendrich II fall scores in predicting returns to the emergency department (ED) for falls within 6 months.