Estimating Key Performance Indicators of a New Emergency Department Model

Introduction

Healthcare, being a necessity for all nations, is considered as one of the effective and essential entity for all societies, where care treatment is given to needed patients (Mifflin, 2007). The media and politics are given much more attention to the challenges faced by healthcare since it threatens the health status of the nation. These challenges can be listed as staffing shortages (“Nurse Staffing”, 2015; “Staffing Survey”, 2001), aging population (Hellmich, 2008), rising costs (Bodenheimer, 2005), and inefficient hospital processes (Berwick & Hackbarth, 2012). Simulation Modeling is proved through the literature to be the most effective way to model the daily operations of a queuing, concurrent system such as the emergency department. The system chosen for study is the one of a hospital in Lebanon currently suffering from large waiting times caused by resource shortages (equipment and manpower), absence of computer technology and the heterogeneity of insurance types. These problems caused a loss of potential revenue where patients tend to leave the system before being treated and overcrowding/bottleneck because of the high length of stay; thus, leading to patient dissatisfaction and sometimes may affect patient’s safety. The system is studied and analyzed using several methods in order to establish a comprehensive and solid view of the ED operation: collecting real data, conducting interviews with staff/patients and site visits’ observations. Based on that, the model is designed, verified and validated leading to a reliable system. Patient flow is then improved using simulation experimentation and optimization techniques.

In order to ensure better process performance and provide efficient services to patients, always maintaining a high level of safety, improvements can be suggested by identifying new resource flow and allocation. Challenges arise here because of the various acuity levels of patients, the numerous ED processes and its different service time. Simulation modeling was proven to be efficient to combat such challenges and provide realistic results. Without interruption of the real system operations, a model can be designed to detect and imitate the flow of resources and patients through the system. Then, changes can be suggested to any process and effects can be easily analyzed before being applied. Using simulation, constraints, bottlenecks, and inefficiencies can be identified and combated thus guarantying patient and management satisfaction. The studied ED faces common issues in the healthcare industry: staffing shortage and inefficient operations. The ED is constituted of two different emergency rooms located in two different buildings and sharing some facilities, such as radiology and billing. This leads to patients’ large waiting time and high staff utilization rates. Furthermore, this hospital suffers from the existence of various insurance coverage types which imposes on the billing department to deal with different insurance companies and indirectly affects the patient flow in the ED.

This journal is organized as follows: an introduction is presented in section 1 then a hospital background in section 2. Section 3, 4 and 5 represent the methodology followed in order to accomplish this project; including data collection and analysis, designing the Arena model and testing it in order to verify its functionality and validity, and output results. In section 6, experimentation is performed and different scenarios are suggested in order to explore the effects of changing specific metrics in the ED and areas for future improvement are suggested. In section 7, model optimization is performed using Arena OptQuest. Finally, the journal is concluded in section 8 and future work perspectives are suggested.