Use of discrete event simulation in hospital capacity planning

In recent years, the healthcare industry is undergoing a rapid expansion in the United States. For healthcare facilities, resource planning at early design stage is a critical step before architectural design. The ‘resources' here refer to both long term resources (pods, rooms, beds, configuration of one pod) in terms of capacity and configuration, and short term resources(staffs, equipments) in terms of capacity and allocation. To achieve performance targets defined by the clients, such as staff/equipment/bed utilization efficiency, average waiting time of all patients, turn away rate, an assessment and verification at the preliminary planning stage is necessary. There are at least two methods to solve this problem. The first is analytical in nature, relying on queuing theory, and falls under the industrial engineering field. The other is computational in nature, relying on process simulation, and specifically discrete event simulation. While queuing theory is easier to conduct, usually requiring less data, and providing more generic rules than simulation, simulation methods result in detailed information about patient flow modeling and deliver more accurate results. This paper is divided into three parts. The first part introduces queuing theory and discrete event simulation in terms of their principles, features and applications in healthcare planning. This is followed by a case study in the ED using discrete event simulation to plan pod configuration and number of pods for an emergency department. During this process, the simulation tool is introduced as an example instrument for advanced DES simulation. The paper ends with a discussion of outcomes. (1) DES is capable to differentiate between alternatives with small changes, and can be widely used to do capacity planning for healthcare facilities. (2) the chosen simulation tool supports the modelling and analysis steps well

A review of different approaches to access and people circulation within health-care facilities and the application of modelling, simulation and visualisation

Evidence suggests that improving access and people circulation in hospitals can: improve staff performance and productivity; enhance patients’ safety, privacy and rate of recovery; minimise the risk of cross-infection; reduce the delay time of external service delivery; create a more welcoming environment for visitors; and reduce the evacuation time in emergency situations. Consequently the need to design hospital layouts that benefit from the most effective system cannot be over-emphasised. This paper focuses on identifying different systems of access and people circulation in health-care facilities in general and hospitals in particular. The research on access and people circulation reported in this paper comprises three main phases. The first phase involves a literature review of existing health-care environments to identify different types of access and people circulation requirements. The second phase focuses on categorising the adopted approaches and systems in order to compare and contrast the advantages and disadvantages of each. The final phase provides a critique of current modelling and simulation tools being applied during the planning and design phases to improve access and people circulation. The paper concludes with recommendations which will be used to shape future research in the area

Performance analysis of organizations as complex systems.

This dissertation provides a method for evaluating the difference in performance after an organization makes a change while considering the stochastic nature in which it operates. A procedure that uses simulation to estimate outcomes by adjusting controllable parameters and leaving uncontrolled parameters unadjusted is proposed. As healthcare organizations are considered as highly complex systems, a case study involving a scheduling tactic change in the mother-baby service line of a hospital is used to demonstrate application of this procedure. The goal in the case study was to reduce delays in transitioning care of mother patients from the labor and delivery unit to the postpartum care unit. The Holds Rate metric measured delays as the number of mothers deemed to be unintentionally delayed from transferring to the postpartum care unit to the total number of deliveries. While the scheduling tactic change did not yield the anticipated result, the proposed procedure was used to show that performance would have been worse had the change not been made. Hospital leadership chose to keep the solution and target performance was later surpassed. Ultimately, hospital leaders heralded the project as a great success. The proposed procedure was applied with two different simulation methods. A Monte Carlo simulation model was used to measure Holds Rate and a discrete-event simulation model to measure the average delay time experienced by patients waiting to be placed in a postpartum bed following delivery. The results of the procedure with both models led to the same conclusion that the scheduling tactic change indeed reduced delays in the transitions of care between the two hospital units. The case study demonstrated the validity and applicability of the proposed procedure and organizations may benefit from its use as leaders may be more prone to act since analysis with the procedure isolates the effects of uncontrolled parameters. Isolating these effects to better understand those of controlled parameters can promote an organization’s sustainability by advancing knowledge of cause-and-effect relationships. Future research with this topic can include application with other simulation methods, investigating the impacts of technology advancements, and considering a method of analysis using Bayesian inference

Produtividade de setores de tomografia computadorizada utilizando simulação por eventos discretos

Computed tomography (CT) is now one of the most important medical equipments for anatomical exams. This work studied 11 CT scanners of 1, 2, 4, 16 and 128 slices from 3 manufacturers in 10 health units. For that, we studied: CT market worldwide; opinions of professionals who manage CTs; examination procedures, steps and times. Then, 5 computer simulation models were created using the MedModel® software with 125 combinations of CT types, structures and patient demands, to study productivity, profitability, capacity, patients’ exam times and structural changes needed to optimize processes. The results showed that the CT scan time does not only depend on the time of image acquisition and that it does not become proportionally faster replacing a CT by one with capacity for more simultaneous slices. For example, the time of image acquisition in a brain scan made on an 1-slice CT took 2’15", while the same exam on an 128-slice CT took 1’37". The simulation (scenario 4, fifth model) showed that choosing a 4-slice CT would be the most profitable and faster option. Times of other steps should be considered and examination procedures optimized. The results obtained may be useful to health managers. The simulation proved to be an important tool for the analysis of processes and changes.O tomógrafo computadorizado (TC) tornou-se um dos equipamentos médicos mais importantes por examinar muitas áreas anatômicas. Este trabalho estudou o processo de exames de tomografia computadorizada em 11 TCs de 1, 2, 4, 16 e 128 cortes de três marcas em 10 unidades. Para isso estudou-se: os mercados de TC; as opiniões de profissionais que especificam TC; os processos de exame, definindo estruturas, etapas dos exames e seus tempos. Então, usando o software MedModel® foram criados 5 modelos de simulação computacional com 125 combinações de TC, estruturas e demandas de pacientes, para estudar produtividades, lucratividades, capacidades de atendimento, tempos dos pacientes no processo e alterações estruturais que otimizem o processo. Os resultados mostraram que o tempo de exame de tomografia não depende somente do tempo da aquisição da imagem e que essa etapa não se torna proporcionalmente mais rápida trocando-se um TC por outro que produza mais cortes simultâneos. Por exemplo, o tempo de aquisição de imagem em um dos exames acompanhados de crânio em um TC de 1 corte demorou 2’15”, enquanto outro exame igual feito em um TC de 128 cortes demorou 1’37”. Os tempos das demais etapas devem ser considerados e os processos de exame otimizados. A simulação do cenário 4 do quinto modelo mostrou que a escolha de um TC de 4 cortes seria a mais lucrativa e produziria tempos curtos. Os resultados obtidos podem ser úteis aos gestores de saúde. A simulação se mostrou uma importante ferramenta para a análise dos processos e possíveis alterações

Demand and Capacity Modelling of Acute Services Using Simulation and Optimization Techniques

The level of difficulty that hospital management have been experiencing over the past decade in terms of balancing demand and capacity needs has been at an unprecedented level in the UK. Due to shortage of capacity, hospitals are unable to treat patients, and in some cases, patients are transferred to other hospitals, outpatient referrals are delayed, and accident and emergency (A&E) waiting times are prolonged. So, it’s time to do things differently, because the current status quo is not an option. A whole hospital level decision support system (DSS) was developed to assess and respond to the needs of local populations. The model integrates every component of a hospital (including A&E, all outpatient and inpatient specialties) to aid with efficient and effective use of scarce resources. An individual service or a specialty cannot be assumed to be independent, they are all interconnected. It is clear from the literature that this level of generic hospital simulation model has never been developed before (so this is an innovative DSS). Using the Hospital Episode Statistics and local datasets, 768 forecasting models for the 28 outpatient and inpatient specialties are developed (to capture demand). Within this context, a variety of forecasting models (i.e. ARIMA, exponential smoothing, stepwise linear regression and STLF) for each specialty of outpatient and inpatient including the A&E department were developed. The best forecasting methods and periods were selected by comparing 4 forecasting methods and 3 periods (i.e. daily, weekly and monthly) according to forecast accuracy values calculated by the mean absolute scaled error (MASE). Demand forecasts were then used as an input into the simulation model for the entire hospital (all specialties). The generic hospital simulation model was developed by taking into account all specialties and interactions amongst the A&E, outpatient and inpatient specialties. Six hundred observed frequency distributions were established for the simulation model. All distributions used in the model were based on age groups. Using other inputs (i.e. financial inputs, number of follow ups, etc.), the hospital was therefore modelled to measure key output metrics in strategic planning. This decision support system eliminates the deficiencies of the current and past studies around modelling hospitals within a single framework. A new output metric which is called ‘demand coverage ratio’ was developed to measure the percentage of patients who are admitted and discharged with available resources of the associated specialty. In addition, a full factorial experimental design with 4 factors (A&E, elective and non-elective admissions and outpatient attendance) at 2 levels (possible 5% and 10% demand increases) was carried out in order to investigate the effects of demand increases on the key outputs (i.e. demand coverage ratio, bed occupancy rate and total revenue). As a result, each factor is found to affect total revenue, as well as the interaction between elective and non-elective admissions. The demand coverage ratio is affected by the changes in outpatient demands as well as A&E arrivals and non-elective admissions. In addition, the A&E arrivals, non-elective admissions and elective admissions are most important for bed occupancy rates, respectively. After an exhaustive review of the literature we notice that an entire hospital model has never been developed that combines forecasting, simulation and optimization techniques. A linear optimization model was developed to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from forecasting and forecasting-simulation) for each inpatient elective and non-elective specialty. In conclusion, these results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans. This hospital decision support system can become a crucial instrument for decision makers for efficient service in hospitals in England and other parts of the world

Improving surgical patient flow through simulation of scheduling heuristics

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 79).Massachusetts General Hospital (MGH) is currently the nation's top ranked hospital and is the largest in New England. With over 900 hospital beds and approximately 38,000 operations performed each year, MGH's operating rooms (ORs) run at 90% utilization and their hospital beds at 99% operational occupancy. MGH is faced with capacity constraints throughout the perioperative (pre-, intra-, and postoperative) process and desires to improve throughput and decrease patient waiting time without adding expensive additional resources. This project focuses on matching the intraday scheduling of elective surgeries with the discharge rate and pattern of patients from the hospital floor by investigating ways surgeons could potentially schedule their cases within a given OR block. To do this, various scheduling rules are modeled to measure the impact of shifting patient flow in each step of the perioperative process. Currently the hospital floor proves to be the biggest bottleneck in the system. Delays in discharging patients result in Same Day Admits (patients that will be admitted to the hospital post-surgery) waiting for hospital beds in the Post Anesthesia Care Unit (PACU). These patients wait more than sixty minutes on average after being medically cleared to depart the PACU. A simulation model is built to evaluate the downstream effects of each scheduling rule and discharge process change. The model takes into account physical and staff resource limitations at each of the upstream and downstream steps in the perioperative process. By scheduling Same Day Admits last in each OR block, patient wait time in the PACU can be reduced up to 49%. By implementing the recommended changes the system will realize lower wait times for patients, less stress on the admitting and nursing staff, and a better overall use of the limited physical resources at MGH.by Ashleigh Royalty Range.S.M.M.B.A

Effect of Appointment Schedules on the Operational Performance of a University Medical Clinic

Healthcare costs in the United States are one of the highest in the world. The healthcare expenditure alone accounted for 17.9% of the Gross Domestic Product of US in 2011. The National Healthcare Expenditure (NHE) is expected to increase at an annual rate of 6.6% from $2.6 Trillion in 2010 to$ 4.5 Trillion in 2019. The per capita expenditure for hospital outpatients and physicians has been the highest among other hospital expenses. This escalation in expenses created a need for productivity improvements in the healthcare industry to control costs. Some of the common problems encountered in outpatient clinics are high patient wait times, physician idle times, physician overtimes and high patient congestion. These problems not only lead to the inefficient operation of a clinic but also cause frustration and dissatisfaction to the physicians and patients. A well designed appointment system is very critical for the effective operation of outpatient clinics by minimizing these problems. The objective of this research was to study the effect of different appointment systems on the operational performance of a university medical clinic. The process at the medical clinic in the LSU Student Health Center (SHC) was modeled using the Rockwell Arena® simulation software. Four scheduling rules: Individual block rule, Bailey rule, 3-Bailey rule, and the Two-at-a-time rule, were studied to understand their effect on the performance parameters of the SHC. The performance parameters considered were the provider measures (provider idle time, startup idle time, provider overtime, provider utilization) and patient measures (patient wait time and patient throughput time). The individual block rule was the most patient friendly with shortest patient measures (patient throughput time - 39.6 min and patient wait time - 15.5 min); however it had the highest provider measures (Idle time – 50.5 min, Startup idle time – 10.4 min, Overtime – 16.2 min). The 3-Bailey rule was the most provider friendly rule with the least provider times (Idle time – 17 min, Startup idle time – 4.6 min, Overtime – 5.6 min) and best provider utilization (95%), but had high patient times (throughput time – 48.1 min and wait time – 24.1 minute). To aid the decision making process of the schedule selection for the SHC, a KT analysis was performed by weighing the performance parameters. The Bailey rule was observed to be the most suitable rule for the SHC as it had a good trade-off between the patient times and provider times compared to the other rules. The Bailey rule had better provider times (Idle time – 31.8 min, Startup idle time – 6.5 min, Overtime – 6.9 min) and better provider utilization rate (92%) when compared to the individual block rule and had marginally higher patient times (throughput time – 41.4 min and wait time – 17.3 min). A test run of the Bailey rule with one provider for ten days also confirmed this behavior of the rule