Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital

Objective: Healthcare faces the continual challenge of improving outcome whilst aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. Design:  Discrete event simulation was used to model and analyse cost and resource utilisation with an activity based costing approach. Data for a full comparison before the process change was unavailable so we utilised a modelling approach, comparing a Virtual Fracture Clinic (VFC) to a simulated Traditional Fracture Clinic (TFC). Setting:  The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. Outcome measures: Our study focused exclusively on non-operative trauma patients attending Emergency Department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries in association with activity costs from the models.ResultsPatients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p=<0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI: 21.74, 23.92) per patient compared with £36.81 (95% CI: 35.65, 37.97) for the TFC pathway.  Conclusions:  Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional face-to-face clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings

Simulation-based metrics analysis of an outpatient center.

With more and more attention surrounding healthcare, Industrial Engineers have championed the task to help hospitals and outpatient centers operate as efficiently as possible. Simulation is often used to analyze hospital performance measures. The University of Louisville Health Care Outpatient Center is a relatively new building occupying 169,000 ft2 and opened in October of 2008. The clinic is experiencing uneven workloads, over scheduling of Medical Assistants, and highly variable patient waiting times. The Arena Simulation package has been used to develop a model of the outpatient center\u27s current state. Using the data from July 2009 the model has been validated and verified. The model uses actual patient arrival data and discrete probability distributions to describe the current patient processing scheme for 41 doctors who regularly operate out of the outpatient center. Utilization rates for doctors were generally very high, but great variability was also present. Room utilization rates were lower than the author expected, confirming that the clinic could potentially house more doctors. Medical Assistants (MA) and doctors had almost equal numbers of patients waiting for them so it can be suggested that when deciding staffing levels MAs and doctors should be added at a one to one ratio. Patient waiting times were also highly variable based on which doctor was being visited and it was suggested that the current doctors review their scheduled patient visit times to make sure they are scheduling for an accurate time. Further research can be done to increase usability and add animation to the model

Acuity-based Performance Evaluation and Tactical Capacity Planning in Primary Care

Effective primary care requires timely and equitable access to care for patients as well as efficient and balanced utilization of physician time. Motivated by a family health clinic in Ontario, Canada, this research proposes ways to improve both of these aspects of primary care through tactical capacity planning based on acuity-based performance targets. First, we propose a new metric based on acuity levels to evaluate timely access to primary care. In Canada, as well as other participant countries in the Organization for Economic Co-operation and Development (OECD), the main metric currently used to evaluate access is the proportion of patients who are able to obtain a same- or next-day appointment. However, not all patients in primary care are urgent and require a same- or next-day appointment. Therefore, accurate evaluation of timely access to primary care should consider the urgency of the patient request. To address this need, we define multiple acuity levels and relative access targets in primary care, akin to the CTAS system in emergency care. Furthermore, current access time evaluation in the province is mostly survey-based, while our evaluation is based on appointment data and hence more objective. Thus, we propose a novel, acuity-based, data-driven approach for evaluation of timely access to primary care. Second, we develop a deterministic tactical capacity planning (TCP) model to balance workload between weeks for each family physician in the specific primary care clinic in this study. Unbalanced workload among weeks may lead to provider overtime for the weeks with high workload and provider idle time for weeks with low workload. In the proposed TCP model, we incorporate the results from access time evaluation in the first study as constraints for access time. The proposed TCP model considers 11 appointment types with multiple access targets for each appointment type. The TCP model takes as input a forecast of demand coming from an ARIMA model. We compare the results of the TCP model based on current access time targets as well as targets resulting from our acuity-based metrics. The use of our proposed acuity-based targets leads to allocation of time slots which is more equitable for patients and also improves physician workload balance. Third, we also propose a robust TCP model based on the cardinality-constrained method to minimize the highest potential physician peak load between weeks. Therefore, the developed robust TCP model enables protection against uncertainty through providing a feasible allocation of capacity for all realizations of demand. The proposed robust TCP model considers two interdependent appointment types (e.g., new patients and follow ups), multiple access time targets for each appointment type and uncertainty in demand for appointments. We conduct a set of experiments to determine how to set the level of robustness based on extra cost and infeasibility probability of a robust solution. In summary, this dissertation advocates for the definition and subsequent use of acuity-based access time targets for both performance evaluation and capacity allocation in primary care. The resulting performance metrics provide a more detailed view of primary care and lead to not only more equitable access policies but also have the potential to improve physician workload balance when used as input to capacity planning models

Managing Operational Efficiency And Health Outcomes At Outpatient Clinics Through Effective Scheduling

A variety of studies have documented the substantial deficiencies in the quality of health care delivered across the United States. Attempts to reform the United States health care system in the 1980s and 1990s were inspired by the system\u27s inability to adequately provide access, ensure quality, and restrain costs, but these efforts had limited success. In the era of managed care, access, quality, and costs are still challenges, and medical professionals are increasingly dissatisfied. In recent years, appointment scheduling in outpatient clinics has attracted much attention in health care delivery systems. Increase in demand for health care services as well as health care costs are the most important reasons and motivations for health care decision makers to improve health care systems. The goals of health care systems include patient satisfaction as well as system utilization. Historically, less attention was given to patient satisfaction compared to system utilization and conveniences of care providers. Recently, health care systems have started setting goals regarding patient satisfaction and improving the performance of the health system by providing timely and appropriate health care delivery. In this study we discuss methods for improving patient flow through outpatient clinics considering effective appointment scheduling policies by applying two-stage Stochastic Mixed-Integer Linear Program Model (two-stage SMILP) approaches. Goal is to improve the following patient flow metrics: direct wait time (clinic wait time) and indirect wait time considering patient’s no-show behavior, stochastic server, follow-up surgery appointments, and overbooking. The research seeks to develop two models: 1) a method to optimize the (weekly) scheduling pattern for individual providers that would be updated at regular intervals (e.g., quarterly or annually) based on the type and mix of services rendered and 2) a method for dynamically scheduling patients using the weekly scheduling pattern. Scheduling templates will entertain the possibility of arranging multiple appointments at once. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. First, we use risk-neutral two-stage stochastic programming model where the objective function considers the expected value as a performance criterion in the selection of random variables like total waiting times and next, we expand the model formulation to mean-risk two-stage stochastic programming in which we investigate the effect of considering a risk measure in the model. We apply Conditional-Value-at-Risk (CVaR) as a risk measure for the two-stage stochastic programming model. Results from testing our models using data inspired by real-world OBGYN clinics suggest that the proposed formulations can improve patient satisfaction through reduced direct and indirect waiting times without compromising provider utilization

Exploring alternative routes to realising the benefits of simulation in healthcare

Discrete event simulation should offer numerous benefits in designing healthcare systems but the reality is often problematic. Healthcare modelling faces particular challenges: genuine, fundamental variations in practice and an opposition to any suggestion of standardisation from some professional groups. This paper compares the experiences of developing a new simulation in an Accident and Emergency (A&amp;E) Department, a subsequent adaptation for modelling an outpatient clinic and applications of a generic A&amp;E simulation. These studies provide examples of three distinct approaches to realising the potential benefits of simulation: the bespoke, the reuse and the generic route. Reuse has many advantages: it is relatively efficient in exploiting previous modelling experience, delivering timely results while providing scope for adaptations to local practice. Explicitly demonstrating this willingness to adapt to local conditions and engaging with stakeholders is particularly important in healthcare simulation

Applying and integer Linear Programming Model to an appointment scheduling problem

Dissertação de Mestrado, Ciências Económicas e Empresariais (Economia e Políticas Públicas), 28 de fevereiro de 2022, Universidade dos Açores.A gestão de consultas ambulatórias pode ser um processo complexo, uma vez que envolve vários stakeholders com diferentes objetivos. Para os utentes poderá ser importante minimizar os tempos de espera. Simultaneamente, para os trabalhadores do setor da saúde, condições de trabalho justas devem ser garantidas. Assim, é cada vez mais necessário ter em conta o equilíbrio de cargas horárias e a otimização dos recursos disponíveis como principais preocupações no agendamento e planeamento de consultas. Nesta dissertação, uma abordagem com dois modelos para a criação de um sistema de agendamento de consultas é proposta. Esta abordagem é feita em programação linear, com dois modelos que têm como objetivo minimizar as diferenças de cargas horárias e melhorar o seu equilíbrio ao longo do planeamento. Os modelos foram estruturados e parametrizados de acordo com dados gerados aleatoriamente. Para isso, o desenvolvimento foi feito em Java, gerando assim os dados referidos. O Modelo I minimiza as diferenças de carga horária entre os quartos disponíveis. O Modelo II, por outro lado, propõe uma nova função objetivo que minimiza a diferença máxima observada, com um processo de decisão minxmax. Os modelos mostram resultados eficientes em tempos de execução razoáveis para instâncias com menos de aproximadamente 10 quartos disponíveis. Os tempos de execução mais altos são observados quando as instâncias ultrapassam este número de quartos disponíveis. Em relação ao equilíbrio da carga horária, observou-se que o número de especialidades disponíveis para atendimento e a procura por dia foram o que mais influenciou a minimização da diferença da carga horária. Os resultados do Modelo II mostram melhor tempo de execução e um maior número de soluções ótimas. Uma vez que as diferenças entre os dois modelos não são consideráveis, o Modelo I poderá representar um melhor conjunto de soluções para os decisores já que minimiza a diferença da carga horária total entre quartos em vez de apenas minimizar o valor máximo da diferença de carga horária entre quaisquer dois quartos.ABSTRACT: Outpatient appointment management can be a complex process since it involves many conflicting stakeholders. As for the patients it might be important to minimize waiting time. Simultaneously, for healthcare workers, fair working conditions must be guaranteed. Thus, it is increasingly necessary to have workload balance and resource optimization as the main concerns in the scheduling and planning of outpatient appointments. In this dissertation, a two-model approach for designing an appointment scheduling is proposed. This approach is formulated as two mathematical Integer Linear Programming models that integrate the objective of minimizing workload difference and improving workload balance. The models were structured and parameterized according to randomly generated data. For this, the work was developed in Java, generating said data. Model I minimizes the workload differences among rooms. Model II, on the other hand, proposes a new objective function that minimizes the maximum workload difference, with a minxmax decision process. The computational models behaves efficiently in reasonable run times for numerical examples with less than approximately 10 rooms available. Higher run times are observed when numerical examples surpass these number of available rooms. Regarding workload balance, it was observed that the number of specialties available for appointments and the demand for each day were the most influential in the minimization of workload difference. Model II results show a shorter model run time and more optimal solutions. As the differences between both Models are not considerable, Model I might propose a better set of solution for decision makers since it minimizes the total workload difference amongst rooms instead of only minimizing the maximum workload difference between any two rooms

A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability and number of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled walk-in patient visits. The simulation model captures the complex characteristics of the Orlando Veteran\u27s Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint