The emergency observation and assessment ward

A recent development to reduce ED crowding and increase urgent patient admissions is the opening of an Emergency Observation and Assessment Ward (EOA Ward). At these wards urgent patients are temporarily hospitalized until they can be transferred to an inpatient bed. In this paper we present an overflow model to evaluate the effect of employing an EOA Ward on elective and urgent patient admissions

A hybrid algorithm to size the hospital resources in the case of a massive influx of victims

Disaster situations either natural or made-man caused a large number of deaths and injured people. Morocco has experienced several disasters recently, the last one was the railway accident on 16 October 2018, which caused 127 serious injuries and 7 deaths. This large number was a big problem for the hospital to manage the received victims in right direction, which caused lives lost and disability. In this article, in collaboration with Mohammed (V) hospital in Casablanca city in Morocco, we suggested a solution that saves lives and eliminates number of disability by using a hybrid algorithm to size the hospital resources in the case of a massive influx of victims. We also suggested a support decision tool that is called Emergency Support Decision Tool. This helpful tool gives an idea about the needed resources that support these emergencies according to the victim’s number. The proposed solution consisted in making a hybrid algorithm that mixed the theoretical simulation process and the experience feedback by developing hybrid genetic and hybrid heuristic algorithms. These algorithms using as an input the matrix solutions that generated under ARENA software and the solution generated by neural networks that based on experiences feedback. The objective was to provide a solution based on available resources. In fact, the results showed that the hybrid heuristic algorithm is more performant than the hybrid genetic algorithm

Pesquisa operacional em organizações públicas brasileiras

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade e Gestão de Políticas Públicas, Departamento de Administração, 2019.Este estudo científico, de caráter qualitativo, tem como objetivo analisar a utilização da Pesquisa Operacional em Organizações públicas brasileiras. Para seu alcance foi necessário responder a 3 objetivos específicos definidos que são: (a) Verificar estudos e literaturas sobre PO no Serviço Público; (b) Verificar em algumas Organizações públicas brasileiras a aplicação de PO; (c) Identificar vantagens e desvantagens da utilização de PO nas Organizações públicas brasileiras. A partir dessas informações foram utilizadas 2 metodologias cientificas, a Revisão Sistemática de Literatura (RSL) e a aplicação da entrevista semiestruturada. A Revisão Sistemática foi realizada em 2 bases científicas e as entrevistas realizadas em 3 órgãos diferentes, com servidores de cargo de chefia. Como resposta à pergunta desse trabalho, foi deduzido que a aplicação da Pesquisa Operacional tem sido superficial no âmbito do serviço público brasileiro e com poucos artigos científicos abordando tal temática

The Optimal Number of Hospital Beds Under Uncertainty: A Costs Management Approach

Equipping hospital beds uses a great deal of a hospital''''s resources. Therefore, it is essential to consider the hospital beds'''' efficiency. To increase its efficiency, a fuzzy unrestricted model for managing hospital expenses is presented in this paper. The lack of beds in hospitals leads to patients’ admission loss and consecutively profit loss. On the other hand, increasing the bed count leads to an increase in equipment expenses. Therefore, in order to determine optimal bed capacity, it is of utmost importance to consider these two costs simultaneously. In our paper, hospital admission system is modeled with a multi-server queuing system (M/M/K). Therefore, to calculate the total cost function, limiting probabilities of multi-server queueing model is used. Furthermore, due to uncertain nature of parameters, such as interest rate and hospitalization profit in various future time periods, these uncertainties are covered by fuzzy logic. Finally, to determine the optimal bed count, Lee and Li''''s fuzzy ranking method is used. This model is implemented ona case study. Its goal is to determine the optimal bed count for emergency unit of Razi hospital in Torbat Heydarieh. Considering the high capability of Markovian chains in modeling different circumstances and the various queueing models, the proposed model can be extended for various hospital units

Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources

This paper develops a two-stage planning procedure for master planning of elective and emergency patients while allocating at best the available hospital resources. Four types of resources are considered: operating theatre, beds in the medium and in the intensive care units, and nursing hours in the intensive care unit. A tactical plan is obtained by minimizing the deviations of the resources consumption to the target levels of resources utilization. Some capacity is reserved for emergency care. To deal with the deviation between actually arriving patients and the average number of patients on which the tactical plan is based, we consider the option of planning a higher number of patients (overplanning). To adapt the tactical plan to the actual stream of elective patients, we also consider flexibility rules. Overplanning and flexibility leads to a weekly schedule of elective patients. This schedule is modified to account for emergency patients. Scheduled elective patients may be cancelled and emergency patients may be sent to other hospitals. Cancellations rules for both types of patients rely on the possibility to exceed the available capacities. Several performance indicators are defined to assess patient service/dissatisfaction and hospital efficiency. Simulation results show a trade-off between hospital efficiency and patient service. We also obtain a rank of the different strategies: overplanning, flexibility and cancellation rules

Developing service supply chains by using agent based simulation

The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers’ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Modeling and analysis of mass casualty triage systems

In the aftermath of a mass casualty incident, a large number of patients are likely to arrive at a hospital for medical care. The large patient demand often overwhelms the capacity of the medical resources available in an event called a patient surge, in which medical triage is often utilized. This study develops analytical models based on queuing theory that can be used as the basis for a tool to determine what staffing and other hospital resources are needed during a patient surge. Additionally, this study presents a simulation model that can also be utilized alongside the analytical models to analyze details that are not captured in the analytical approach. These models allow different patient volume and makeup scenarios to be evaluated so that the resources needed can be estimated. The models and codes developed in this study could be paired with a decision support system that hospital administrators and planners could use to develop contingency plans for mass casualty incidents with a variety of patient volumes and makeup. Finally, this study also made a small contribution to the queuing body of knowledge by extending results available for a Markovian multi-server priority queue to yield simple and reasonably accurate approximations for the general multi-server priority queue

Análisis de redes de colas modeladas con tiempos entre llegadas exponenciales e híper erlang para la asignación eficiente de los recursos

Ingeniero (a) IndustrialPregrad