Process Modeling of ABCDE Primary Survey in Trauma Resuscitations: A Crucial First Step for Agent-Based Simulation Modeling of Complex Team-Based Clinical Processes.

INTRODUCTION: Trauma teams are ad hoc, multidisciplinary teams that perform complex patient care and medical decision making under dynamic conditions. The ability to measure and thus understand trauma team processes is still limited. Agent-based simulation modeling (ABSM) can be used to investigate complex relationships and performance within a trauma team. However, the foundational work to support such efforts is lacking. The goal of this work is to develop a comprehensive process model for the primary survey in trauma that can support ABSM. METHODS: A process model for the primary survey of patients with blunt traumatic injuries was developed using Advanced Trauma Life Support guidelines and peer-reviewed publications. This model was then validated using video recordings of 25 trauma resuscitations in a level 1 trauma center. The assessment and treatment pathway followed in each video were mapped against the defined pathway in the process model. Deviations were noted when resuscitations performance did not follow the defined pathway. RESULTS: Overall the process model contains 106 tasks and 78 decision points across all domains, with the largest number appearing in the circulation domain, followed by airway and breathing. A total of 34 deviations were observed across all 25 videos, and a maximum of 3 deviations were observed per video. CONCLUSIONS: Overall, our data offered validity support for the blunt trauma primary survey process model. This process model was an important first step for the use of ABSM for the support of trauma care operations and team-based processes.

Data-Driven Design Strategies to Address Crowding and Boarding in an Emergency Department: A Discrete-Event Simulation Study.

OBJECTIVE: To address prolonged lengths of stay (LOS) in a Level 1 trauma center, we examined the impact of implementing two data-driven strategies with a focus on the physical environment. BACKGROUND: Crowding in emergency departments (EDs) is a widely reported problem leading to increased service times and patients leaving without being seen. METHODS: Using ED historical data and expert estimates, we created a discrete-event simulation model. We analyzed the likely impact of initiating care and boarding patients in the hallway (hallway care) instead of the exam rooms and adding a dedicated triage space for patients who arrive by emergency medical services (EMS triage) to decrease hallway congestion. The scenarios were compared in terms of LOS, time spent in exam rooms and hallway spaces, service time, blocked time, and utilization rate. RESULTS: The hallway care scenario resulted in significantly lower LOS and exam room time only for EMS patients but when implemented along with the EMS triage scenario, a significantly lower LOS and exam room time was observed for all patients (EMS and walk-in). The combination of two simulated scenarios resulted in significant improvements in other flow metrics as well. CONCLUSIONS: Our findings discourage boarding of admitted patients in ED exam rooms. If space limitations require that admitted patients be placed in ED hallways, designers and planners should consider enabling hallway spaces with features recommended in this article. Alternative locations for boarding should be prioritized in or out of the ED. Our findings also encourage establishing a triage area dedicated to EMS patients in the ED.

Forecasting emergency department arrivals: a tutorial for emergency department directors.

This article is a tutorial for emergency department (ED) medical directors needing to anticipate ED arrivals in support of strategic, tactical, and operational planning and activities. The authors demonstrate our regression-based forecasting models based on data obtained from a large teaching hospital's ED. The versatility of the regression analysis is shown to readily accommodate a variety of forecasting situations. Trend regression analysis using annual ED arrival data shows the long-term growth. The monthly and daily variation in ED arrivals is captured using zero/one variables while Fourier regression effectively describes the wavelike patterns observed in hourly ED arrivals. In our study hospital, these forecasting methods uncovered: long-term growth in demand of about 1,000 additional arrivals per year; February was generally the slowest month of the year while July was the busiest month of the year; there were about 20 fewer arrivals on Fridays (the slowest day) than Sundays (the busiest); and arrivals typically peaked at about 10 per hour in the afternoons from 1 p.m. to 6 p.m., approximately. Because similar data are routinely collected by most hospitals and regression analysis software is widely available, the forecasting models described here can serve as an important tool to support a wide range of ED resource planning activities.

A network flow approach to optimizing hospital bed capacity decisions.

The delivery of cost-effective and quality hospital-based health care remains an important and ongoing challenge for the American health care industry. Despite numerous advances in medical procedures and technologies, a growing array of outpatient health care options, limits on inpatient reimbursements, and almost two decades of hospital contraction and consolidation, annual inpatient admissions in the United States are currently at levels not seen since the early 1980s. This combination of increased demand and diminished resources makes planning for hospital bed capacity a difficult problem for health care decision makers. We examine this problem by developing a network flow model that incorporates facility performance and budget constraints to determine optimal hospital bed capacity over a finite planning horizon. Under modest assumptions, we demonstrate that for realistic sized capacity planning problems, our network formulation is not computationally intensive, and allows us to obtain optimal bed capacity plans quickly.