Using singular spectrum analysis to obtain staffing level requirements in emergency units

Many operational research (OR) techniques use historical data to populate model input parameters. Although the majority of these models take into account stochastic variation of the inputs, they do not necessarily take into account seasonal variations and other stochastic effects that might arise. One of the major applications of OR lies within healthcare, where ever increasing pressure on healthcare systems is having major implications on those who plan the provision of such services. Coping with growing demand for healthcare, as well as the volatile nature of the number of arrivals at a healthcare facility makes modelling healthcare provision one of the most challenging fields of OR. This paper proposes the use of a relatively modern time series technique, Singular Spectrum Analysis (SSA), to improve existing algorithms that give required staffing levels. The methodology is demonstrated using data from a large teaching hospital's emergency unit. Using time dependent queueing theory, as well as SSA, staffing levels are obtained. The performance of our technique is analysed using a weighted mean square error measure, introduced in this paper

Firefighter Staffing Model Implications on Fire Casualties and Fire Loss: Life Safety and Socio-Economic Impacts of the Fire Service

Fire and rescue services are considered a staple among services provided by governments to local communities. Local governments are often charged with providing these services, especially across the United States and Canada. As with any professional service, there are standards set forth in order to ensure services are adequate and provide equity to the citizens that they serve. The purpose of this dissertation will be to delve into the common staffing configurations of career fire departments across the United States and Canada, particularly related to staffing levels on fire engines and ladder trucks. Fire departments utilize various staffing models, but commonly, fire engines and ladder trucks have complements of three or four firefighter crews in career departments in the United States and Canada. Industry standards suggests that a minimum of four firefighters should be staffed on each of these apparatus types. However, as a standard, there is flexibility for local departments to staff according to need, whether based on fiscal need or service demand. This dissertation examines correlations between staffing fire engines and ladder trucks with three personnel and higher property loss, as well as greater numbers of human casualties related to fire, verses communities that staff these apparatuses with four personnel. Data was collected from career fire departments across the United States and Canada, then statistically analyzed to determine if there was a correlation of lower staffing and higher property loss and greater human casualties as the result of fire incidents. The results illustrated some surprise findings where it is questionable if staffing levels impact fire loss and human casualties

Time-dependent stochastic methods for managing and scheduling Emergency Medical Services

Emergency Medical Services (EMS) are facing increasing pressures in many nations given that demands on the service are rising. This article focuses in particular on the operations of the Welsh Ambulance Service Trust (WAST), which is the only organisation that provides urgent paramedical care services on a day-to-day basis across the whole of Wales. In response to WAST’s aspiration to improve the quality of care it provides, this research investigates several interrelated advanced statistical and operational research (OR) methods, culminating in a suite of decision support tools to aid WAST with capacity planning issues. The developed techniques are integrated in a master workforce capacity planning tool that may be independently operated by WAST planners. By means of incorporating methods that seek to simultaneously better predict future demands, recommend minimum staffing requirements and generate low-cost rosters, the tool ultimately provides planners with an analytical base to effectively deploy resources. Whilst the tool is primarily developed for WAST, the generic nature of the methods considered means they could equally be applied to any service subject to demand that is of an urgent nature, cannot be backlogged, is heavily time-dependent and highly variabl

Capacity Pooling in Healthcare Systems

Healthcare systems are facing a continuously increasing demand for care while healthcare providers express a need for additional capacity. However, increased capacity in healthcare systems will not be a sufficient option in the near future, and previous research has found a need to improve healthcare capacity planning and management. Capacity planning is aggravated with the presence of variations in a system, and proactive and reactive tools for short-term flexibility in capacity management can be applied to cope with variations in both capacity and demand. One such proactive tool is a capacity pool, which is a general capacity that can be allocated to parts of the system where the temporary need for resources is unusually high. The purpose of this thesis is to develop principles and guidelines for a capacity pooling system in the healthcare sector. A theoretical framework that describes the limiting effects of aggregated variations is modern portfolio theory, first originated in the finance sector. Portfolio theory is in this thesis used to demonstrate the effects on resource utilization when capacity is organized into capacity pools. The study object is Region V\ue4stra G\uf6taland, a healthcare provider and multihospital system in Sweden. The approach of the research project has been systematic, using a mixed-methods approach with predominantly quantitative studies. An interview study, a questionnaire study and a literature review have been conducted to answer the research questions, resulting in three papers.This research project has resulted in several findings which can be useful for healthcare managers when designing and implementing capacity pools. The results include examples on how portfolio theory could be used to design capacity pools, knowledge on the use of proactive and reactive tools for short-term flexibility solutions in healthcare capacity management, and perceived barriers to a capacity pooling approach in healthcare systems. Furthermore, the findings in the three papers contribute to the existing research in several ways. For example, previous studies have requested research with a holistic approach on capacity management in healthcare systems and have highlighted the importance of researching temporary capacity changes in healthcare. The research in this thesis has through a mixed-method systematic approach focused on capacity management in a multihospital system consisting of several healthcare providers, including all types of healthcare personnel, and has provided knowledge on the use of flexibility tools for managing variations in capacity and demand

Time-dependent stochastic modelling for predicting demand and scheduling of emergency medical services

As the prominence of the service sector is increasing in developed nations, new and exciting opportunities are arising for operational researchers to develop and apply models which offer managers solutions to improve the quality of their services. The development of time-dependent stochastic models to analyse complex service systems and generate effective personnel schedules are key to this process, enabling organisations to strike a balance between the provision of a good quality service whilst avoiding unnecessary personnel costs. Specifically within the healthcare sector, there is a need to promote efficient management of an Emergency Medical Service (EMS), where the probability of survival is directly related to the speed of assistance. Motivated by case studies investigating the operation of the Welsh Ambulance Service Trust (WAST), this thesis aims to investigate how operational research (OR) techniques can be developed to analyse priority service systems subject to demand that is of an urgent nature, cannot be backlogged, is heavily time-dependent and highly variable. A workforce capacity planning tool is ultimately developed that integrates a combination of forecasting, queueing theory, stochastic modelling and optimisation techniques into a single spreadsheet model in order to predict future demand upon WAST, set staffing levels, and optimise shift schedules and rosters. The unique linking together of the techniques in a planning tool which further captures time-dependency and two priority classes enables this research to outperform previous approaches, which have generally only considered a single class of customer, or generated staffing recommendations using approximation methods that are only reliable under limited conditions. The research presented in this thesis is novel in several ways. Primarily, the first section considers the potential of a nonparametric modelling technique known as Singular Spectrum Analysis (SSA) to improve the accuracy of demand forecasts. Secondly, the main body of work is dedicated to adapting numerical queueing theory techniques to accurately model the behaviour of time-dependent multi-server priority systems across shift boundaries and evaluate the likelihood of excessive waits for service for two customer classes. The final section addresses how shifts can be optimally scheduled using heuristic search techniques. The main conclusions are that in addition to offering a more flexible approach, the forecasts generated by SSA compare favourably to those obtained using traditional methods, and both approximate and numerical modelling techniques may be duly extended to set staffing levels in complex priority systems

Wisdom at Work: The Importance of the Older and Experienced Nurse in the Workplace

Focuses on promising strategies and opportunities for retaining experienced nurses, one of many approaches the authors recommend to alleviate the current nurse shortage crisis

Reimagining Public Safety in the City of St. Louis: A Vision for Change

This report outlines recommendations for a unilateral reimagining of public safety systems. It details guidance for redirecting police services to critical areas of public need and building a network of systems and services to support community needs and ensure measures of safety for the entire community.The recommendations are designed to address racial disparities and reduce the harm caused by the reliance on police. The report addresses gaps and inconsistencies in law enforcement policies, staffing and resourcing needs in the police department, and a need for improvement in both oversight and community resources. The recommendations are divided into suggestions to the Mayor's Office, the St. Louis Department of Public Safety, and guidance to the St. Louis Metropolitan Police Department (SLMPD)

Improving Patient Safety, Patient Flow and Physician Well-Being in Emergency Departments

Over 151 million people visit US Emergency Departments (EDs) annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated settings in healthcare to study. ED overcrowding is a recognized worldwide public health problem, and its negative impacts include patient safety concerns, increased patient length of stay, medical errors, patients left without being seen, ambulance diversions, and increased health system expenditure. Additionally, ED crowding has been identified as a leading contributor to patient morbidity and mortality. Furthermore, this chaotic working environment affects the well-being of all ED staff through increased frustration, workload, stress, and higher rates of burnout which has a direct impact on patient safety. This research takes a step-by-step approach to address these issues by first forecasting the daily and hourly patient arrivals, including their Emergency Severity Index (ESI) levels, to an ED utilizing time series forecasting models and machine learning models. Next, we developed an agent-based discrete event simulation model where both patients and physicians are modeled as unique agents for capturing activities representative of ED. Using this model, we develop various physician shift schedules, including restriction policies and overlapping policies, to improve patient safety and patient flow in the ED. Using the number of handoffs as the patient safety metric, which represents the number of patients transferred from one physician to another, patient time in the ED, and throughput for patient flow, we compare the new policies to the current practices. Additionally, using this model, we also compare the current patient assignment algorithm used by the partner ED to a novel approach where physicians determine patient assignment considering their workload, time remaining in their shift, etc. Further, to identify the optimal physician staffing required for the ED for any given hour of the day, we develop a Mixed Integer Linear Programming (MILP) model where the objective is to minimize the combined cost of physician staffing in the ED, patient waiting time, and handoffs. To develop operations schedules, we surveyed over 70 ED physicians and incorporated their feedback into the MILP model. After developing multiple weekly schedules, these were tested in the validated simulation model to evaluate their efficacy in improving patient safety and patient flow while accounting for the ED staffing budget. Finally, in the last phase, to comprehend the stress and burnout among attending and resident physicians working in the ED for the shift, we collected over 100 hours of physiological responses from 12 ED physicians along with subjective metrics on stress and burnout during ED shifts. We compared the physiological signals and subjective metrics to comprehend the difference between attending and resident physicians. Further, we developed machine learning models to detect the early onset of stress to assist physicians in decision-making