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

Towards Patient-oriented Design: A Case of the Egyptian Private Outpatient Clinics

In this paper, outpatient clinics’ systems are addressed and their common issues were discussed. As a case study, the private clinics in Egypt are used, and a designed service solution was synthesized. In the case study, the system and application was mainly physician-oriented rather than patient-oriented. With such physician-oriented systems alongside with the current design approaches, waiting times and appointment scheduling are two critical issues. Therefore, in this study, a design thinking approach was implemented to address these design problems for patients and clinic staffs. Based on that analysis, a design application solution called “YourClinic” was developed to support online booking and consultation. This study provides design features and functions to improve the quality of communication between patients and clinic staffs, reduce waiting time, and develop schedules for patients and staffs in order to move towards a patient-oriented clinic service

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

Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Re-engineering the outpatient process flow of a multi-speciality hospital

Manufacturing concepts such as Just-in-Time, Lean and Six-Sigma, Japanese 5S, Materials Requirement Planning, Scheduling and Capacity Management have been applied in the Healthcare industries in the West for the last decade and has yielded positive results. In this study, these concepts and philosophies have been applied to an Indian Multi-speciality Hospital to improve its OPD process flow and increase patient satisfaction. The Outpatients Department (OPD) is usually the most crowded sector in a hospital. The frequent problems encountered include the waiting period for consultation, an unpredictable number of Walk-in patients, insufficient and operationally deficient OPD reception staff and unattended appointment patients. This study aims at, identifying methods to standardise OPD operations management. It has made the process more efficient through optimum resource utilisation. This will increase patient satisfaction by meeting and exceeding their expectations while maintaining quality of care. This research was conducted by mapping the process flow and using the data that was collected through an observational, cross-sectional, non-interventional study. Though there were a comprehensive set of recommendations at the end of the study, only a few could be implemented due to the introduction of a new Hospital Information System (HIS) software putting the implementation plan on hold

Operational decision making for medical clinics through the use of simulation and multi-attribute utility theory.

Currently, health care is a large industry that concerns everyone. Outpatient health care is an important part of the American health care system and is one of the strongest growth areas in the health care system. Many people pay attention to how to keep basic health care available to as many people as possible. A large health care system is usually evaluated by many performance measures. For example, the managers of a medical clinic are concerned about increasing staff utilization; both managers and patients are concerned about patient waiting time. In this dissertation, we study decision making for clinics in determining operational policies to achieve multiple goals (e.g. increasing staff utilization，reducing patient waiting time, reducing overtime). Multi-attribute utility function and discrete even simulation are used for the study. The proposed decision making framework using simulation is applied to two case studies, i.e., two clinics associated with University of Louisville in Louisville, Kentucky. In the first case, we constructed of a long period simulation model for a multi-resource medical clinic. We investigated changes to the interarrival times for each type of patient, assigned patients to see different staff in different visits (e.g., visit #2, visit #5) and assigned medical resources accordingly. Two performance measures were considered: waiting time for patients, and utilization of clinic staff. The second case involved the construction of a one-morning simulation model for an ambulatory internal medicine clinic. Although all the resident doctors perform the same task, their service times are different due to their varying levels of experience. We investigated the assignment of examination rooms based on residents’ varying service times. For this model, we also investigated the effect of changing the interarrival times for patients. Four performance measures were considered: waiting time for patients, overtime for the clinic staff, utilization of examination rooms and utilization of clinic staff. We developed a ranking and selection procedure to compare the various policies, each based on a multiple attribute performance. This procedure combines the use of multi-attribute utility functions with statistical ranking and selection in order to choose the best results from a set of possible outputs using an indifferent-zone approach. We applied this procedure to the outputs from “Healthy for Life” clinic and “AIM” clinic simulation models in selecting alternative operational policies. Lastly, we performed sensitivity analyses with respect to the weights of the attributes in the multi-attribute utility function. The results will help decision makers to understand the effects of various factors in the system. The clinic managers can choose a best scheduling method based on the highest expected utility value with different levels of weight on each attribute. The contribution of this dissertation is two-fold. First, we developed a long term simulation model for a multi-resource clinic consisting of providers with diverse areas of expertise and thus vastly different no-show rate and service times. Particularly, we modeled the details on assigning patients to providers when they come to the clinic in their different visits. The other contribution was the development of a special ranking and selection procedure for comparing performances on multiple attributes for alternative policies in the outpatient healthcare modeling area. This procedure combined a multiple attribute utility function with statistical ranking and selection in determining the best result from a set of possible outputs using the indifferent-zone approach

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

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

Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis

Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis