Resource allocation in health care processes (original) (raw)
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Resource allocation in health care processes: A case study
RePEc: Research Papers in Economics, 2013
This paper utilizes queuing models to analyze health care processes. We extend previous queuing models to allow for i) heterogeneous resources, ii) resource allocation to various tasks, and iii) teams (complementary resources). We model a process of one clinical unit. We use the model to analyze how resource allocation affects both process performance and utilization of resources. This approach emphasizes how allocation of resources to tasks affects process performance. We illustrate how the model can be used to analyze how variations in resources affect process performance and for example how ICT affects process performance.
The healthcare sector is a fast growing segment of GNP in almost every economy. No wonder that we witnessed a tremendous increase in research both medical research to improve medical practice but also research to improve management practices. Patient flow management is an example. Patient flow represents the ability of the healthcare system to serve patients quickly, reliably and efficiently as they move through stages of care. Unfortunately patients still experience delays and waiting lists. A queueing model offers an excellent tool to analyze and to improve the performance of healthcare systems. The purpose of this contribution is to discuss differences with the modeling of manufacturing systems and to focus on modeling issues in patient flow. Next, we discuss two specific topics: first, the impact of interrupts and absences on waiting lists and delays, and second, the modeling of batches in healthcare operations.
Simulation analysis of resource flexibility on healthcare processes
Journal of Multidisciplinary Healthcare, 2016
Purpose: This paper uses discrete event simulation to explore the best resource flexibility scenario and examine the effect of implementing resource flexibility on different stages of patient treatment process. Specifically we investigate the effect of resource flexibility on patient waiting time and throughput in an orthopedic care process. We further seek to explore on how implementation of resource flexibility on patient treatment processes affects patient access to healthcare services. We focus on two resources, namely, orthopedic surgeon and operating room. Methods: The observational approach was used to collect process data. The developed model was validated by comparing the simulation output with actual patient data collected from the studied orthopedic care process. We developed different scenarios to identify the best resource flexibility scenario and explore the effect of resource flexibility on patient waiting time, throughput, and future changes in demand. The developed scenarios focused on creating flexibility on service capacity of this care process by altering the amount of additional human resource capacity at different stages of patient care process and extending the use of operating room capacity. Results: The study found that resource flexibility can improve responsiveness to patient demand in the treatment process. Testing different scenarios showed that the introduction of resource flexibility reduces patient waiting time and improves throughput. The simulation results show that patient access to health services can be improved by implementing resource flexibility at different stages of the patient treatment process. Conclusion: This study contributes to the current health care literature by explaining how implementing resource flexibility at different stages of patient care processes can improve ability to respond to increasing patients demands. This study was limited to a single patient process; studies focusing on additional processes are recommended.
Modeling Hospital Resources with Process Oriented Simulation
2008
A hospital environment is a complex system that requires appropriate allocation of human and material resources in order to optimize its effectiveness and efficiency. This paper utilizes an AWESIM simulation model to investigate patients' flow in a hospital, and how the resources are utilized to respond to the health care system. Results of the study provide insight to the management as to how the patient, doctors, paramedics and other non-clinical resources compete to respond to the health care needs. As an example of how to reduce patients' queue time, the administrator may use the response surface results showing how to combine resources to improve resource deployment and enhance operational efficiency. Additionally, to be truly competitive in the healthcare market, hospital administrators must use a mix of strategies to respond to structural changes in the healthcare industry.
Queueing theory techniques and its real applications to health care systems – Outpatient visits
International Journal of Healthcare Management, 2019
Many organizations such as banks, airlines, health care systems, telecommunications companies and security departments routinely use queuing theory models to help determine capacity levels needed to experienced demands in a more efficient way. Although queuing models have been used in hospitals and other health care systems, its applications in this field has not been widely and extensively utilized. Given the perverseness of delays under health care and due to the fact that many facilities are trying to meet the increasing demands with tightly constrained resources, then queuing theory models can be very useful in identifying other opportunities for service improvement. In this article, we build a foundation into the investigation of queuing phenomenon through the review of some applicability of such techniques. Our emphasis is on intuitive understanding of queuing modelling and solution techniques that are useful in applications. We conclude this report by applying its insights and findings to real time data collected from one of the main health facilities in Botswana.
Hospital capacity management based on the queueing theory
International Journal of Productivity and Performance Management, 2018
Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications The outcomes suggest that operational managers should improve ...
Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.
Queues are characterized structures fashioned to maintain order and create a hold on time, money and human contribution towards development and efficient performance of any system. Queues are experienced in our everyday activities. Queue causes inconvenience to individuals (patients) and economic costs to firms and organizations. Patients wait for minutes, hours, days or months to receive medical service-waiting before, during or after being served. Queuing theory is a mathematical approach to the study of waiting in lines/queues. This research presents the results of a study that evaluates the effectiveness of a queuing model in identifying the major bottleneck in healthcare operations. It uses chi-square and Erlang's queuing to analyse data collected from the University of Benin Health Center and the Faith Mediplex center over one month period. Results showed that queue characteristics existing at the healthcare centers during the situation analysis was a single server multiple queue model. However, after the study was done involving staff at the understudy healthcare centers, it was discovered that queuing is mainly found in record unit and doctor consultation waiting lobby. We finally came to a conclusion and made the some recommendations on how best healthcare centers can maximize the benefits of queuing model for good and effective operations.
Queueing Models in Healthcare with applications to a General Hospital in Zimbabwe
2018
DOI: 10.21276/sjpms.2018.5.2.12 Abstract: In healthcare sector, quality services come with a compromise of devoting more resources e.g. labour force, waiting space, efficient laboratory equipment, etc. Few workforce result in prolonged and sluggish queues which are life threatening especially to accident ill patients. Waiting on a queue is not usually interesting, but reduction in this waiting time usually requires planning and extra investments. Still, emergency departments and intensive care units are among the most intricate and expensive of all medicinal resources, and hospital authorities are mandated to meet the demand for intensive care services with suitable capability. This study seeks to address the congestion of patients flow in acute departments (Emergence department and Intensive care unit ward) from our local hospitals by applying analytical queueing models to the situation. However, our models in this paper only address the waiting queues and waiting space as main cha...