Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis

Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis

Modeling and simulating hospital operations in a 3D environment

The use of dashboards to aid hospital decision makers in managerial and clinical decisions is well documented in the literature, though few broach the challenging subject of combining cost measurement with user satisfaction and building layout optimization. This paper presents an innovative dashboard in a 3D environment, providing decision makers with simulation capabilities using agent based simulation, allowing examination of their facility and the impact of policy, process and layout changes on patients and finances. An example is presented for an Emergency Department, wherein the presented dashboard revealed that the costs of constructing additional triage rooms would produce no benefit to patients; rather, a change in the process would be more beneficial compared with the existing situation. It is concluded that the developed dashboard allows users to make comparisons between multiple scenarios and visualize data in an intuitive format, allowing for decision makers to optimize their facility and operations

The impact of the hospital environment:Understanding the experience of the patient journey

A hospital visit is often an anxious and uncertain event for patients and their relatives. Patients are often concerned about a diagnosis and/or the treatment of their disease in an outpatient or inpatient setting. Knowledge regarding the influence of these settings on patients is essential for facilitating the quality of health care. It is expected that an understanding of the experience of patients will allow designers and decision-makers in hospitals to positively influence the well-being of patients. The aim of this thesis was to gain an improved understanding about a more holistic experience and well-being of patients at specific focal points of the entire patient journey from the arrival, to the diagnosis, and to the actual treatment in a hospital. For example, results showed that patients sometimes experience difficulties in finding their way to an outpatient clinic, that images of nature during a CT scan can reduce anxiety, and that (the opportunity of) interaction with other patients is a pleasant distraction or, on the contrary, an invasion of their own privacy. This thesis emphasizes the relations between the hospital environment and the psychosocial and physical well-being of patients. The results show that it is of great importance to listen carefully to patients’ experiences and needs when designing a hospital as many of the results showed individual differences with patients that emphasize that one size does not fit all. The well-being of patients in future hospitals can be improved by aligning the hospital environment with individual patient characteristics, needs, and preferences

Optimising hospital designs and processes to improve efficiency and enhance the user experience

The health sector is facing increasing pressure to provide effective, efficient, and affordable care to the population it serves. The National Health Service (NHS) of the United Kingdom (UK) has regularly faced scrutiny with NHS England being issued a number of challenges in recent years to improve operational efficiency, reduce wasted space, and cut expenditure. The most recent challenge issued to NHS England has seen a requirement to save £5bn per annum by 2020, while reducing wasted space from 4.4% to 2.5% across the NHS estate. Similarly, satisfaction in the health service is also under scrutiny as staff retention and patient experiences are used in determining the performance of facilities. [Continues.

Social Determinants of Late Presentation to HIV Care

Background: In recent years, increased attention has shifted toward evaluating social determinants of health, and understanding how community, environment, and system factors affect health outcomes. HIV policies and guidelines emphasize the importance of earlier HIV diagnosis and presentation for care. This study evaluated the role of individual and community-level factors in late presentation to HIV care. Methods: HIV-infected patients newly initiating outpatient HIV medical care at an academic medical center between 2005-2010 were included. Patients\u27 self-reported addresses at their first clinic visit were geocoded using geographic information systems software to the appropriate United States census block group. Using data from the U.S. Census Bureau’s 2005-2009 American Community Survey, community-level data was recorded for each patient\u27s census block group. Poisson regression was used to evaluate associations between individual- and community-level factors with late presentation for HIV care, defined as an initial CD4 count /mm3. Results: Among 609 patients, 341 patients (56%) had an initial CD4 count /mm3. At a community level, late presentation was significantly associated with the proportion of African Americans in a census block group (RR=1.47; 95%CI=1.19-1.81); with proportion living in poverty, lack of fuel, and lack of vehicle demonstrating borderline statistical significance. At an individual level, older patients were more likely (1.12; 1.06-1.19), while white females were less likely (0.45; 0.24-0.84) to present with a CD4 count /mm3. Conclusion: Both individual and community-level characteristics were associated with late presentation for HIV medical care. Research and interventions to promote earlier HIV diagnosis and care entry should include geographical information and social determinants of health to define priority populations

Choosing Number And Scheduling Priority Of Warm-Hand Offs: A Des Model

Background: The integration of behavioral health care into primary care is being promoted as a means to treat more people with behavioral health problems where they are most likely to be seen. Clinics with traditional behavioral health services may open slots among scheduled appointments to see these warm-hand off (WHO) patients identified by primary care providers (PCPs). The effects of giving priority for behavioral health appointments to either scheduled or WHO patients and of the number of appointments left open for WHO patients are investigated in this project. Methods: A discrete event simulation model was built of a moderately integrated clinic. WHO patients arrive randomly, on average 4 per day per PCP, and wait to see behavioral health providers (BHPs) who also see scheduled patients. Simulations of four clinic sizes, with PCP to BHP ratios of 1:1, were run. Effects of queue discipline (priority is given to scheduled or WHO patients) and the number of open WHO slots (3 or 5) are analyzed. Outcomes include the percent of scheduled patients served, the percent of WHO patients served, and the percent of BHP utilization. Results: In clinics with 1 PCP and 1 BHP, for 3 and 5 open slots respectively, giving priority to WHO patients resulted in 80.6% and 81.0% of WHO patients served and 84.4% and 86.6% of scheduled patients served, however, giving priority to scheduled patients resulted in 97.8% and 98.1% of scheduled patients served, but 32.0% and 47.9% of WHO patients served. A similar pattern was seen for larger clinics, though the percent of WHO patients served increased for both 3 and 5 open slots with clinic size. Having 3 or 5 open slots led to few differences when WHO patients were given priority, but when scheduled patients were given priority, choosing 5 open slots rather than 3 open slots, increased the percent of WHO patients served by 15-20 percentage points across the clinic sizes. In either queue discipline, changing from 3 to 5 open slots reduced the percent of BHP utilization by approximately 8 percentage points for all clinic sizes. When WHO patients were given priority, the average wait time for scheduled patients increased from approximately 2-5 minutes to 13-19 minutes across clinic sizes. Conclusion: These results might suggest to some clinics attempting to integrate primary care and traditional behavioral health services to choose to give WHO patients priority. However, it is recognized that there are costs associated with not seeing both scheduled and WHO patients, and clinics making this decision will have to weigh these tradeoffs. The analysis of these results provides one framework to assist in choosing between different arrangements for integration

Developing Health System Surge Capacity: Community Efforts in Jeopardy

Examines six communities' efforts to build surge healthcare capacities to respond to terrorist attacks, epidemics, and natural and manmade disasters; the needed components and funding; and the effects of the restrictions and decline in federal funds

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages