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Admissions planning decides on the number of patients admitted for a specialty each day, but also on the mix of patients admitted. Within a specialty different categories of patients can be distinguished on behalf of their requirement of resources. The type of resources required for an admission may involve beds, operating theatre capacity, nursing capacity and intensive care beds. The mix of patients is, therefore, an important decision variable for the hospital to manage the workload of the inflow of patients. In this paper we will consider the following planning problem: how can a hospital generate an admission profile for a specialty, given a target patient throughput and utilization of resources, while satisfying given restrictions? For this planning problem, we will develop an integer linear programming model, that has been tested in a pilot setting in a hospital. The paper includes an analysis of the planning problem, a description of the model developed, an application of a specialty orthopaedics, and a discussion of the results obtained.

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

Singapore’s health-care infrastructure is suffering from increasing pressure due to population growth and a rapidly ageing population. This paper aims to assess the resilience of hospital facilities in Singapore’s health-care industry. The main attribute of resilience is adaptive capacity, which is also associated with vulnerability. Vulnerability is defined as the system’s susceptibility to threats that cause damage and affect its normal performance, while resilience is defined as the ability to anticipate and the capacity to change before a setback becomes obvious.

A questionnaire survey was adopted for the study, with respondents drawn randomly from both the health-care professionals as well as the public. The questionnaire survey results from 83 respondents, consisting of 31 health-care professionals and 52 members of the public, are analysed in this pilot study.

Ninety-one per cent of the respondents perceived bed shortage as an indication of vulnerability. The survey results showed that bed shortages, high bed-occupancy and long waiting hours were perceived as indications of vulnerability. The top three vulnerabilities identified were Singapore’s ageing population, the fast-growing population and the increasing trend of chronic diseases in its population. From the results, respondents appeared doubtful about the resilience of Singapore’s public hospitals. On a positive note, Singapore residents are still, relatively speaking, confident of the quality of Singapore’s health-care delivery system, which can be translated as one with relatively strong community resilience.

In conclusion, it appears fair to say that the public perceive hospital facilities in Singapore’s health-care industry to be reasonably resilient, but expect further improvements to ensure continuous delivery of quality health-care services.

Purpose – Aims to describe a project carried out within Hampshire Social Services investigating potential care pathways for older people after discharge from hospital and to show the potential of the simulation methodology in such situations. Design/methodology/approach – A discrete‐event simulation was used to determine the system capacities and to estimate the likely associated reimbursement costs. Findings – A prototype simulation model was developed showing the potential value of this approach. Research limitations/implications – Restrictions in data access shifted the focus from quantitative service mapping to a more descriptive approach. Practical implications – Currently, many older patients experience delayed discharge from acute beds because of capacity limitations in Social Services’ traditional post‐acute care services. At the same time, new regulations require Local Authorities to reimburse NHS Acute Trusts if hospital discharge is delayed solely due to inadequate provision of social care assessments and services. In order to overcome the so‐called “bed‐blocking” problem, a new range of services termed “Intermediate Care” has been introduced to offer alternative options for older patients. These services are examined in terms of capacity and appropriateness. Originality/value – This paper fulfils an identified need to record and evaluate the new post‐acute packages introduced by the Social Services and NHS and proposes simulation as one of the most suitable methodologies for such objectives.

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions.

A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients.

Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds.

This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This paper aims to propose lean-based interventions that address the main causes of emergency department overcrowding. Emergency department overcrowding (EDOC) and increased length of stay (LOS) have been key global issues for more than 20 years, as they have serious repercussions. No measurements have been done to assess the situation nationally. Expanding emergency departments (EDs) and adding more beds have never succeeded in eliminating wastes and targeting the root causes of the problem.

This paper is a quantitative analytical applied research. The paper used direct observation for seven days to collect patient flow data on ED patients at a secondary care hospital in Kuwait. It calculated wait times and services to identify the major causes of EDOC and increased LOS.

Around one-third of the ED design capacity was used by 12 per cent of the patients who stayed >6 h each. The wasted waiting time represents 56.2 per cent of the aggregated LOS, which puts lean management (LM) on the top of the process reengineering approaches suitable for improving overcrowding by reducing waste. Guided by the LM concepts, the paper proposes solutions that fall into three themes. The selected solutions address the vital few causes of the EDOC and prolonged EDLOS.

This paper is the first study of its kind in Kuwait, and one of the most outstanding studies in the Gulf region, in terms of the number of the daily ED visits and the comprehensive multi-level proposed interventions.

Admission scheduling is identified as an important strategy to match supply and demand in acute care hospitals. During the last decades, many different theoretical models of admission scheduling have been developed, but only a few of them have reached the stage of implementation. Several authors have given some indication that there may be a gap between theory and practice of admission scheduling. In this study we try to describe this gap using a two‐stage research methodology: an extensive literature review in order to determine the theoretical functional requirements for a system that supports the admission scheduling decision and a telephone survey in order to learn more about the admission scheduling practice in Belgian hospitals. The study finds a large gap between the theoretical requirements and the practical application of admission scheduling in hospitals. In summary, most hospitals have not worked out an admission scheduling policy indicating which resources are critical in the scheduling process and how information on the availability of these resources can be captured.

The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.

To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.

The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.

The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.

The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.

The purpose of this study is to reflect upon the ramifications of two decades of Lean Six Sigma implementations in Dutch healthcare institutions in the light of the current COVID-19 pandemic.

The authors provide an evaluation of the impact that Lean Six Sigma implementations have had on the ability of Dutch healthcare institutions to respond adequately to healthcare needs during the COVID-19 crisis.

Process improvement in healthcare has had a tendency to cut capacity and flexibility which are needed to deal with excessive demand shocks, such as during a pandemic. The main reason for this failure seems to be an overly strong focus on cost reduction instigated by Lean Six Sigma during stable times.

Besides the research method being an inferential procedure, the research focuses on the Netherlands and so the generalizability might be limited. However, using Lean Six Sigma to improve healthcare processes has found broad acceptance, so the implications may well carry over to other countries.

The authors call for a more comprehensive approach of process improvement within healthcare that takes flexibility and buffering in anticipation of excess variability and disruption into greater account. Therefore, this study provides a new perspective on how and to which aim Lean Six Sigma should be applied in healthcare.

An assessment is given of the impact of Lean Six Sigma implementations on the ability to respond to the COVID-19 crisis. This is done by identifying the focus points of improvement projects and considering the impact on the resilience of healthcare operations.