A multilevel integrative approach to hospital case mix and capacity planning.

Hospital case mix and capacity planning involves the decision making both on patient volumes that can be taken care of at a hospital and on resource requirements and capacity management. In this research, to advance both the hospital resource efficiency and the health care service level, a multilevel integrative approach to the planning problem is proposed on the basis of mathematical programming modeling and simulation analysis. It consists of three stages, namely the case mix planning phase, the master surgery scheduling phase and the operational performance evaluation phase. At the case mix planning phase, a hospital is assumed to choose the optimal patient mix and volume that can bring the maximum overall financial contribution under the given resource capacity. Then, in order to improve the patient service level potentially, the total expected bed shortage due to the variable length of stay of patients is minimized through reallocating the bed capacity and building balanced master surgery schedules at the master surgery scheduling phase. After that, the performance evaluation is carried out at the operational stage through simulation analysis, and a few effective operational policies are suggested and analyzed to enhance the trade-offs between resource efficiency and service level. The three stages are interacting and are combined in an iterative way to make sound decisions both on the patient case mix and on the resource allocation.Health care; Case mix and capacity planning; Master surgery schedule; Multilevel; Resource efficiency; Service level;

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

Operating room planning and scheduling: A literature review.

This paper provides a review of recent research on operating room planning and scheduling. We evaluate the literature on multiple fields that are related to either the problem setting (e.g. performance measures or patient classes) or the technical features (e.g. solution technique or uncertainty incorporation). Since papers are pooled and evaluated in various ways, a diversified and detailed overview is obtained that facilitates the identification of manuscripts related to the reader's specific interests. Throughout the literature review, we summarize the significant trends in research on operating room planning and scheduling and we identify areas that need to be addressed in the future.Health care; Operating room; Scheduling; Planning; Literature review;

Designing for Economies of Scale vs. Economies of Focus in Hospital Departments

Subject/Research problem: Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centres, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples are specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper service and patient group characteristics are examined to determine conditions where a centralized model is more efficient and conversely where a decentralized model is more efficient. - Research Question: When organizing hospital capacity what service and patient group characteristics indicate efficiency can be gained through economies of scale vs. economies of focus? - Approach: Using quantitative Queueing Theory and Simulation models the performance of centralized and decentralized hospital clinics is compared. This is done for a variety of services and patient groups. - Result: The study results in a model measuring the tradeoffs between economies of scale and economies of focus. From this model management guidelines are derived. - Application: The general results support strategic planning for a new facility at the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital. A model developed during this research is also applied in the Chemotherapy Department of the same hospital

Evaluating the capacity of clinical pathways through discrete-event simulation.

Organizing a medical facility efficiently is hard due to the numerous patient trajectories and their use of joint and scarce resources. Moreover, these trajectories tend to be complex and characterized by uncertain medical processes. In this paper, we will structure patient trajectories using clinical pathways and aggregate them in a discrete-event simulation model. This model enables the health manager to evaluate and improve important performance indicators, both for the patient and the hospital, by conducting a detailed sensitivity analysis. Two case studies, performed at large hospitals in Antwerp and Leuven (Belgium), will be introduced and briefly discussed in order to illustrate the generic nature of the model.Capacity management; Case studies; Discrete-event simulation; Health care operations; Processes; Structure; Simulation; Model; Performance; Indicators; Sensitivity; Studies; Hospitals; Belgium; Order;

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Integrating Balanced Scorecard and Simulation Modelling to Improve Emergency Department Performance in Irish Hospitals

In the healthcare sector, there is a requirement for innovative solutions in managing the high levels of complexity and uncertainty within Emergency Departments (EDs). Simulation modeling is currently seen as a competent means of analyzing EDs, which allows changes effects to be understood and predicted more easily. The Balanced Scorecard (BSC), well-known performance management concept, has become a steering method in approaching new improvement cycles. This paper presents a methodology that integrates BSC and simulation modeling to improve the performance of ED in a University Hospital in North Dublin. BSC design began with understanding patient\u27s needs, ED activities, as well as training and development programs. Then a detailed simulation model was developed and integrated with the BSC to provide a comprehensive decision support system. This integrated model can be used for evaluation of various decisions in emergency area. The developed integrated model is also a tool for improvement

The Impact of Threat Levels at the Casualty Collection Point on Military Medical Evacuation System Performance

One of the primary duties of the Military Health System is to provide effective and efficient medical evacuation (MEDEVAC) to injured battlefield personnel. To accomplish this, military medical planners seek to develop high-quality dispatching policies that dictate how deployed MEDEVAC assets are utilized throughout combat operations. This thesis seeks to determine dispatching policies that improve the performance of the MEDEVAC system. A discounted, infinite-horizon continuous-time Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The model incorporates problem features that are not considered under the current dispatching policy (e.g., myopic policy), which tasks the closest-available MEDEVAC unit to service an incoming request. More specifically, the MDP model explicitly accounts for admission control, precedence level of calls, different asset types (e.g., Army versus Air Force helicopters), and threat level at casualty collection points. An approximate dynamic programming (ADP) algorithm is developed within an approximate policy iteration algorithmic framework that leverages kernel regression to approximate the state value function. The ADP algorithm is used to develop high-quality solutions for large scale problems that cannot be solved to optimality due to the curse of dimensionality. We develop a notional scenario based on combat operations in southern Afghanistan to investigate model performance, which is measured in terms of casualty survivability. The results indicate that significant improvement in MEDEVAC system performance can be obtained by utilizing either the MDP or ADP generated policies. These results inform the development and implementation of tactics, techniques and procedures for the military medical planning community

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model

Efficiency evaluation for pooling resources in health care

Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centers, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper we examine service and patient group characteristics to study the conditions where a centralized model is more efficient, and conversely, where a decentralized model is more efficient. This relationship is examined analytically with a queuing model to determine themost influential factors and then with simulation to fine-tune the results. The tradeoffs between economies of scale and economies of focus measured by these models are used to derive general management guidelines