Optimizing Emergency Department Throughput Using Best Practices to Improve Patient Flow

Emergency Department (ED) crowding and bottle necks are the reality of hospitals across the country. Patients seeking care and needing inpatient beds via the emergency rooms are facing delays with attaining the right level of care. Orchestrating a patient through an ED admission requires a multidisciplinary effort to provide safe, effective and efficient care. This quality improvement project conducted in a tertiary acute care hospital focused on Centers for Medicare and Medicaid metrics to measure Emergency Department (ED) throughput. This multidisciplinary initiative focused on reducing time stamps for patient arrival to the ED through departure to hospital or home. Outcomes showed a significant decrease in the time frame for patient arrival to being seen by a qualified provider, left without being seen rates, ED diversion, and ancillary department turnaround times. The interventions can be applied at other hospital based emergency departments

Incentives and Targets in Hospital Care: Evidence from a Natural Experiment

Performance targets are commonly used in the public sector, despite their well known problems when organisations have multiple objectives and performance is difficult to measure. It is possible that such targets may work where there is considerable consensus that performance needs to be improved. We investigate this possibility by examining the response of the English National Health Service (NHS) to waiting time targets. Long waiting times have been a key issue for the NHS for many years. Using a natural policy experiment exploiting differences between countries of the UK, supplemented with a panel of data on English hospitals, we examine whether high profile targets to reduce waiting times met their goals of reducing waiting times without diverting activity from other less well monitored aspects of health care. Using this robust design, we find that targets led to a fall in waiting times without apparent reductions in other aspects of patient care.health care, waiting times, targets, incentives

Design of experiments for non-manufacturing processes : benefits, challenges and some examples

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

Boarding in the emergency department

The purpose of this research was to identify the causes of boarding in the emergency department (ED), look into the resulting effects in which an overcrowded ED may create, and lastly, delve into potential interventions and solutions to counter the factors which create the issue of boarding. Boarding in an ED is a trending topic an dis relevant to healthcare and can be a factor in life or death. research methods included a in-depth literature review of nursing journals, medical journals, systematic reviews, and cross-sectional studies found via CINAHL and PubMed. Results showed that periods of boarding and a longer length of stay resulted in higher mortality and adverse patient outcomes. Interventions mentioned included more efficient coordination of care, higher staff to patient ratios, and quicker test results and transport times

Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient s dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient s arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of low acuity patients in ED with more accuracy when compared with regularized regression, conventional rolling average, and quantile regression methods. Finally, most influential predictors for predictability of patient wait time are identified for the best performing model

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Implementation of Fast-Track Triage Process to Improve Ambulance Patient Offloading Time (APOT) in a Psychiatric Emergency Services (PES) Unit

Abstract. Background. Ambulance delays in hospital EDs have recently been significant in the United States. This delay has been attributed to emergency department overcrowding, a record number of 911 calls, a shortage of ED staff (doctors and nurses), and poor resources available in the communities as alternatives to an ED visit. Local Emergency Medical Service Authorities (LEMSA) monitor and determine the community standards for measuring ambulance offload delays (AOD), usually in minutes. Problem. Lengthy ambulance patient offload times and their subsequent impact on EMS have been significant all around the state of California as well as nationwide. The ambulance delay problem in California is at the point that the state government is working to reduce the delay through the legislature. An assembly bill known as AB 40, authored by Assemblymember Freddie Rodriguez (D-Pomona), has officially been signed into law by Governor Newsom. AB 40 would require the California Emergency Medical Services Authority (EMSA) to take urgent actions to address the chronic issue of AOD and implement standards to ensure all Californians receive immediate care when faced with life-threatening emergencies. The Psychiatric Emergency Services/Crisis Stabilization Unit (PES/CSU) in this project is an acute care psychiatric hospital in Alameda County (Northern California) plagued by long delays of up to 48 minutes of ambulance offload delays and up to 77 minutes at the 90th percentile for transfer of care. Under the provisions of AB 40, the Local Emergency Medical Services Authority (LEMSAs) in California will be required to maintain an APOT of 30 minutes or less. Interventions. This project seeks to implement the Fast-Track triage process to reduce the average Ambulance Patient Offload Time (APOT) to Alameda County Local EMS Authority set community standard of 30 minutes. Proposed Measures. For six months of the Fast-Track triage process implementation, psychiatric emergency services (PES) nurses at this project hospital will be trained on the Fast-Track triage process; APOT data will be collected every shift and collated monthly to assess the intervention’s progress. The impact of this process will be evaluated for effectiveness at the end of the six months. The PES nurses will also be surveyed on their knowledge, satisfaction, perceived bottlenecks with the Fast-Track triage process, and their observed impact on APOT. Keywords: PES (psychiatric emergency services), APOT (ambulance patient offload time), APOD (ambulance patient offload delay), EMS (emergency medical services), triage, ambulance offload delays (AOD), hospital throughput