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Application of discrete event simulation in the performance analysis of an outpatient unit
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Abstract(s)
O Departamento da Consulta Externa do Grupo HPA Saúde, Unidade Hospitalar de Gambelas, oferece aos seus pacientes uma ampla oferta em especialidades médicas e cirúrgicas. Com o intuito de prestar serviços da melhor qualidade e, na sequência de um aumento da procura dos pacientes resultando em maiores necessidades, o hospital no início do ano de 2021, expandiu as suas instalações. Restruturou o serviço de ambulatório, aumentando a sua capacidade de resposta assim como a qualidade das suas instalações e serviços prestados aos pacientes.
A Unidade de Exames Especiais é uma das unidades integrantes deste departamento, onde são realizados exames específicos de várias especialidades tais como, cardiologia, gastroenterologia, urologia e pneumologia. A unidade foi reestruturada e iniciou funções em maio de 2021. A nível da gestão operacional, esta reestruturação e ampliação da capacidade física da unidade, trouxe vários desafios, como a alteração dos fluxos de pacientes, a redistribuição de recursos físicos e humanos, e a necessidade de fazer um adequado planeamento da capacidade da unidade.
O planeamento da capacidade é complexo e é um grande desafio para os gestores, pois o desequilíbrio entre a oferta e a procura pode desencadear aumentos de tempo de espera de pacientes ou desperdício de recursos. Qualquer um destes desfechos negativos põe em causa o desempenho do serviço, tornando-o menos eficiente. Realizar um adequado planeamento da capacidade não é, no entanto, tarefa fácil dada a imprevisibilidade associada a estes serviços, a sazonalidade da procura, as flutuações de pacientes ao longo do dia e os padrões de comportamento dos próprios pacientes. Ainda assim, torna-se muito importante estudar qual o impacto que a alteração ou o ajustamento de determinados parâmetros ao nível deste planeamento poderá ter no desempenho da Unidade de Exames Especiais, para que sejam implementadas estratégias de melhoria da eficiência dos serviços e, consequentemente, do seu desempenho. Este estudo tem como objetivo explorar a aplicação de um modelo de Simulação de Eventos Discretos (DES) para analisar o impacto do planeamento de capacidade no desempenho da Unidade de Exames Especiais. Em particular, este estudo tem como objetivos específicos, identificar quais as variáveis que mais condicionam o planeamento de capacidade e ainda identificar relações/interações entre o planeamento de capacidade e o tempo de espera dos pacientes.
A aplicação do modelo DES, irá permitir recriar o funcionamento do sistema real e realizar várias simulações, de forma a compreender como é que o sistema funciona e de que forma estratégias alternativas poderão afetar o desempenho do sistema. Irá também permitir perceber qual é o impacto do planeamento de capacidade no desempenho do serviço e qual é a relação entre a capacidade de planeamento e o tempo de espera dos pacientes.
Dada a complexidade da unidade e o caráter exploratório deste estudo, apenas foram considerados para o estudo os exames realizados pela especialidade de gastroenterologia.
Foram colhidos dados referentes a todos os exames realizados às quintas-feiras na Unidade de Exames Especiais entre janeiro e abril de 2022. Nesse período, 261 pacientes tiveram exames agendados, mas 8 não compareceram. Os dados foram, portanto, colhidos para um total de 253 pacientes. Esses dados foram extraídos do processo eletrónico do paciente, registos de enfermagem do processo clínico e do programa de agendamento de consultas.
O modelo DES foi aplicado para modelar o fluxo de pacientes da unidade utilizando o software SIMUL8, versão 2015. Após a análise dos dados e da identificação das distribuições estatística mais adequadas para representar o comportamento de alguns parâmetros, o modelo foi devidamente configurado. Para validar o modelo DES, os resultados e a animação da simulação foram verificados por elementos da equipa da unidade. Uma análise comparativa foi também realizada através da comparação de dados reais com dados e resultados gerados pela simulação. Três variáveis (Tempo médio e desvio padrão em sistema e o tempo máximo de permanência no sistema), apresentaram médias e desvio padrão com um erro relativo superior a 0,1. O tempo médio no sistema é um dos indicadores de desempenho onde se verificaram diferenças maiores entre os valores efetivamente observados e os valores simulados. Estas diferenças podem estar associadas com a construção do modelo de simulação, e com algumas limitações encontradas, como seja a dificuldade em encontrar distribuições estatísticas adequadas para alguns dos parâmetros do modelo.
Um cenário foi testado com o objetivo de maximizar a capacidade da unidade. O resultado da simulação do cenário demonstra que o agendamento de exames a cada 15 minutos duplica o número de pacientes admitidos e aumenta consideravelmente o número de exames realizados. No entanto, o tempo médio em sistema, aumenta de 125.51 para 140.39 minutos e o desvio padrão de 25.68, para 32.02 minutos. A fila para a sala de procedimentos apresenta tempos médios e desvios padrão altos, mostrando que poderá ser um gargalo no sistema.
Outro cenário foi testado para demonstrar que ao adicionar uma segunda sala de procedimentos ao modelo de simulação, esta tem um impacto relevante no desempenho da unidade. É possível observar que este cenário permite ao serviço manter a produtividade do cenário anterior, mas o tempo médio no sistema reduz consideravelmente de 140.39 minutos para 127.40 minutos e o desvio padrão de 32.02 minutos para 26.37 minutos. A análise da fila para a sala de procedimentos deste cenário de teste, mostra uma diminuição significativa do tempo de permanência dos pacientes em fila. Esses resultados confirmam que a sala de procedimentos é um gargalo. Com uma sala de procedimentos adicional, o desempenho da unidade melhora, reduzindo a média e o desvio padrão do tempo no sistema, apresentando resultados ainda melhores do que a simulação de base.
Devido ao alto nível de complexidade do modelo de simulação e escassez de tempo disponível, não foi possível resolver todos os problemas do modelo de simulação. Seria interessante, com as ferramentas certas e com mais tempo disponível, melhorar o modelo de simulação e obter resultados com maior nível de precisão. Outros cenários poderão ser testados, tais como, melhorar o desempenho e capacidade utilizando os tempos de agendamento recomendados pela Sociedade Portuguesa de Endoscopia Digestiva e compará-los com os cenários anteriormente testados. O parâmetro do modelo, vestiário, foi identificado pela equipa da unidade como um gargalo. Neste estudo, devido às limitações do SIMUL8, não foi possível estudar o impacto de apenas existir um vestiário na unidade no fluxo de pacientes e no desempenho da unidade.
The Outpatient Department of the HPA Saúde Group offers its patients a wide range of medical and surgical specialties. To provide better quality services and with the current increase in patient needs, the hospital at the beginning of 2021 expanded its facilities and restructured the outpatient service, increasing its responsiveness as well as the quality of its facilities and services provided to patients. The Special Exams Unit is one of the units that integrates this department, where specific exams are conducted in various specialties such as cardiology, gastroenterology, urology, and pulmonology. The unit was restructured and started operations in May 2021. In terms of operational management, this restructuring and expansion of the unit's physical capacity brought challenges, such as changing patient flows, redistribution of physical and human resources, and regarding the need for proper capacity planning. Capacity planning is complex and a major challenge for managers, as the imbalance between demand and supply can trigger increases in patient waiting times or waste of resources. Any of these negative outcomes jeopardize the performance of the service, making it less efficient. The intervention threshold in capacity planning is very tenuous, given factors such as unpredictability, seasonality, patient fluctuations throughout the day, and patient behavior patterns. Consequently, it is important to study the impact of this planning on the performance of the Special Exams Unit, so that strategies can be designed to improve the efficiency of the services, and as a result, their performance This study aims to explore the Application of Discrete Event Simulation (DES) to study the impact of capacity planning on the performance of the Special Exams Unit. Its specific objectives are to identify which variables affect capacity planning and to identify relationships/interactions between capacity planning and patient waiting time. The application of DES will allow us to replicate the workings of the Unit and perform simulations to understand how the system works and how alternative strategies can impact the performance of the system. It will also help us identify what the impact of capacity planning is on service performance and what is the relationship between capacity planning and patient waiting time. The Special Exams unit operates five days a week, Monday through Friday. The exams are usually scheduled between 8:30 and 12:30 a.m. and between 2:00 to 6:30 p.m., and there may be changes in the schedule according to the number of exams scheduled. Given the complexity of the Unit and the exploratory nature of this study, only exams performed by the gastroenterology specialty were considered for the study. It was collected data related to all the exams performed on Thursdays at the Special Exams Unit between January and April 2022. During this period, 261 patients had exams scheduled, but 8 did not show up. Data was, therefore, collected for a total of 253 individual patient. This data was extracted from electronic record data from the hospital, nursing records from the patients’ handling process, and from the appointment-scheduling system. This data was recorded and analyzed using Microsoft Excel version 365 and the Statistical Package for the Social Sciences version 28.0.1.0. The Stat::fit® version 2 program was used to analyze the statistical distributions of the collected data. DES was applied in the modelling of the patients’ flow at the Special Exams Unit using the software program SIMUL8, version 2015. The patients’ flow was mapped through interviews with experts from the unit and through direct observation of the real process system. After data and statistical distribution analyses the simulation model was developed. To validate the DES model, the results from baseline simulation were verified by experts from the unit. The simulation animation was also analyzed by members of staff from the unit, to confirm that patients in the model were following an appropriate flow. A comparative analysis was also performed through comparing real data and simulation output data. Three variables showed a relative error superior to 10%, in the average and standard deviation values. Average time in the system is one of the performance indicators that showed a larger error. The significant differences observed between real and simulated values might be associated with the model construction process and, particularly, with some limitations found in this process such as the difficulty in finding suitable statistical distributions to represent some model parameters. A scenario was tested with the objective of maximizing the unit’s capacity. The simulation output of this scenario demonstrates that scheduling exams each 15 minutes, would duplicate the number of patients admitted and increase considerably the number of exams performed. The average time of the patients in the system would, however, increase to 140 minutes and the standard deviation would increase to 32.02 minutes. The queue for the procedure room, demonstrates, in this scenario, high variation with high average and standard deviation times, showing that the procedure’s room is a likely bottleneck in the exams process. Another scenario test was run to demonstrate that adding an additional procedure room to the simulation model, could have a relevant impact on the performance of the unit. It is possible to observe that this scenario would maintain the same productivity of the previous one but would allow to reduce considerably the average time in the system to 127.40 minutes, with a reduction in the standard deviation to 26.37 minutes. The analysis of the queue for the procedure room of this scenario test shows a significant decrease of queuing time. These results demonstrate that the procedure room can be, in fact, a bottleneck, and that with an additional procedure room, the performance of the unit improves, reducing the average and standard deviation of the time patients spend in the system, showing even better results than the baseline simulation. Due to the high level of complexity of the simulation model and the shortage of the time available, it was not possible to resolve all the issues identified in the modeling process. It would be interesting, with the right tools and with more time available, to improve the simulation model and obtain results with higher level of accuracy. Other scenarios could also be tested, such as testing the impact of using the recommend schedule times from the Portuguese Society of Digestive Endoscopy and comparing the results of this scenario with the results of the scenarios already tested. The model parameter related with the dressing room has been identified by staff members of the unit as a likely bottleneck. Unfortunately, in this study, due to SIMUL8 limitations, it was not possible to study the real impact that having a single dressing room might have on the patients’ flow and on the performance of the unit. It will be interesting to study the impact of this bottleneck in future research.
The Outpatient Department of the HPA Saúde Group offers its patients a wide range of medical and surgical specialties. To provide better quality services and with the current increase in patient needs, the hospital at the beginning of 2021 expanded its facilities and restructured the outpatient service, increasing its responsiveness as well as the quality of its facilities and services provided to patients. The Special Exams Unit is one of the units that integrates this department, where specific exams are conducted in various specialties such as cardiology, gastroenterology, urology, and pulmonology. The unit was restructured and started operations in May 2021. In terms of operational management, this restructuring and expansion of the unit's physical capacity brought challenges, such as changing patient flows, redistribution of physical and human resources, and regarding the need for proper capacity planning. Capacity planning is complex and a major challenge for managers, as the imbalance between demand and supply can trigger increases in patient waiting times or waste of resources. Any of these negative outcomes jeopardize the performance of the service, making it less efficient. The intervention threshold in capacity planning is very tenuous, given factors such as unpredictability, seasonality, patient fluctuations throughout the day, and patient behavior patterns. Consequently, it is important to study the impact of this planning on the performance of the Special Exams Unit, so that strategies can be designed to improve the efficiency of the services, and as a result, their performance This study aims to explore the Application of Discrete Event Simulation (DES) to study the impact of capacity planning on the performance of the Special Exams Unit. Its specific objectives are to identify which variables affect capacity planning and to identify relationships/interactions between capacity planning and patient waiting time. The application of DES will allow us to replicate the workings of the Unit and perform simulations to understand how the system works and how alternative strategies can impact the performance of the system. It will also help us identify what the impact of capacity planning is on service performance and what is the relationship between capacity planning and patient waiting time. The Special Exams unit operates five days a week, Monday through Friday. The exams are usually scheduled between 8:30 and 12:30 a.m. and between 2:00 to 6:30 p.m., and there may be changes in the schedule according to the number of exams scheduled. Given the complexity of the Unit and the exploratory nature of this study, only exams performed by the gastroenterology specialty were considered for the study. It was collected data related to all the exams performed on Thursdays at the Special Exams Unit between January and April 2022. During this period, 261 patients had exams scheduled, but 8 did not show up. Data was, therefore, collected for a total of 253 individual patient. This data was extracted from electronic record data from the hospital, nursing records from the patients’ handling process, and from the appointment-scheduling system. This data was recorded and analyzed using Microsoft Excel version 365 and the Statistical Package for the Social Sciences version 28.0.1.0. The Stat::fit® version 2 program was used to analyze the statistical distributions of the collected data. DES was applied in the modelling of the patients’ flow at the Special Exams Unit using the software program SIMUL8, version 2015. The patients’ flow was mapped through interviews with experts from the unit and through direct observation of the real process system. After data and statistical distribution analyses the simulation model was developed. To validate the DES model, the results from baseline simulation were verified by experts from the unit. The simulation animation was also analyzed by members of staff from the unit, to confirm that patients in the model were following an appropriate flow. A comparative analysis was also performed through comparing real data and simulation output data. Three variables showed a relative error superior to 10%, in the average and standard deviation values. Average time in the system is one of the performance indicators that showed a larger error. The significant differences observed between real and simulated values might be associated with the model construction process and, particularly, with some limitations found in this process such as the difficulty in finding suitable statistical distributions to represent some model parameters. A scenario was tested with the objective of maximizing the unit’s capacity. The simulation output of this scenario demonstrates that scheduling exams each 15 minutes, would duplicate the number of patients admitted and increase considerably the number of exams performed. The average time of the patients in the system would, however, increase to 140 minutes and the standard deviation would increase to 32.02 minutes. The queue for the procedure room, demonstrates, in this scenario, high variation with high average and standard deviation times, showing that the procedure’s room is a likely bottleneck in the exams process. Another scenario test was run to demonstrate that adding an additional procedure room to the simulation model, could have a relevant impact on the performance of the unit. It is possible to observe that this scenario would maintain the same productivity of the previous one but would allow to reduce considerably the average time in the system to 127.40 minutes, with a reduction in the standard deviation to 26.37 minutes. The analysis of the queue for the procedure room of this scenario test shows a significant decrease of queuing time. These results demonstrate that the procedure room can be, in fact, a bottleneck, and that with an additional procedure room, the performance of the unit improves, reducing the average and standard deviation of the time patients spend in the system, showing even better results than the baseline simulation. Due to the high level of complexity of the simulation model and the shortage of the time available, it was not possible to resolve all the issues identified in the modeling process. It would be interesting, with the right tools and with more time available, to improve the simulation model and obtain results with higher level of accuracy. Other scenarios could also be tested, such as testing the impact of using the recommend schedule times from the Portuguese Society of Digestive Endoscopy and comparing the results of this scenario with the results of the scenarios already tested. The model parameter related with the dressing room has been identified by staff members of the unit as a likely bottleneck. Unfortunately, in this study, due to SIMUL8 limitations, it was not possible to study the real impact that having a single dressing room might have on the patients’ flow and on the performance of the unit. It will be interesting to study the impact of this bottleneck in future research.
Description
Keywords
Planeamento de capacidade Discrete event simulation Fluxo de pacientes Unidade de ambulatório Endoscopia