Patient Arrival Rate Research Articles

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area.

Differentiated health insurance policy for coordinating healthcare referral in hierarchical healthcare systems with an internet diagnosis platform

The integration of the Internet with traditional medical services is poised to transform health insurance policies. This study aims to explore the coordinating role of differentiated health insurance policies within the context of Internet healthcare. A four-stage sequential game decision-making model is developed within a queuing framework to address scenarios involving online patient referrals and misdiagnoses. The model begins by analyzing the equilibrium arrival strategy of patients, followed by the determination of optimal service capacity strategies for a nonprofit community health center (CHC) and optimal pricing strategies for a for-profit general hospital (GH). Additionally, the model describes an optimal differentiated subsidy strategy for the government aimed at minimizing total social costs. Analysis reveals that under certain conditions, an increase in the service price at GH relative to CHC can lead to a higher influx of online patients visiting GH in person. Furthermore, when the number of online patients exceeds a specific threshold, it not only prompts the government to increase the disparity in health insurance subsidies between the two hospital tiers but also encourages GH to reduce its service prices and offer free services to online patients. Numerical experiments explore the effects of government budgets, sharing ratios, and other variables on the system's equilibrium state, providing several managerial insights. Notably, when patients' misdiagnosis costs are partially covered, increasing GH's misdiagnosis cost-sharing ratio not only enhances the patient arrival rate but also enhances GH's profitability.

Payment schemes for resource reallocation in hierarchical medical system: Fee-for-capacity versus performance payment

This study explores resource reallocation, or capacity sinking, in a hierarchical medical system that includes a healthcare funder, a comprehensive hospital, a community hospital, and patients with varying perceptions of different hospital levels. To help the funder better coordinate the process and induce a comprehensive hospital to willingly sink its high-quality medical resources to a community hospital, we design and evaluate two payment schemes: Fee-For-Capacity (FFC) scheme and Performance Payment (PP) scheme. In the FFC scheme, the funder pays a unit capacity sinking price to the comprehensive hospital. In the PP scheme, the sinking price is paid only if the community hospital increases the rate of patient visits. By considering different parties’ decision-making behaviors, we develop a sequential game model within a queuing framework to determine equilibrium results in terms of the patient arrival rate at each hospital, the community hospital’s capacity planning, the comprehensive hospital’s capacity sinking rate, and the funder’s capacity sinking price. Our study indicates that the FFC scheme outperforms the PP scheme in terms of social welfare, patient utility, and waiting time when the budget is low or the funder’s concern about patient utility is relatively high or low. However, both schemes are suboptimal when the budget and the funder’s concern degree are medium. We find that a hybrid payment scheme can alleviate the drawbacks of FFC and PP and help achieve system optimum.

Low and Delayed Patient Arrival Rates during COVID-19 Pandemic in Acute Coronary Syndrome Patients: A Study from Tertiary Cardiac Center.

The footfall and admission rates post-COVID-19 scenario have come down in the case of patients with cardiac problems with larger delays leading to complications as per several studies. Studies have primarily focused on the disruptions due to lockdown but not much study has been done to understand how it has affected the lifestyle of the patients and changed the mentality leading to lower patient arrivals. The current study deals with understanding how the patient arrival pattern has changed and what are the factors affecting the same during COVID-19 times. Analysis of the data for patients with acute coronary syndrome from April to June for pre (2019)- and post (2020)-COVID-19 times admitted to a government cardiology and cardiac care hospital is studied for patient-based and accessibility-based parameters. A significant reduction in admissions (4230-880) with higher arrival times from the onset of symptom (80% rise) although the overall mean distance traveled reduced (63.8-47.4 km) leading to greater health risks. Furthermore, problems due to physical inactivity, diabetes, smoking, and drinking have also risen by about 3%-5% in each case. The need for better health-care system connectivity and the need for online platforms-based consultation systems, especially in times of such a pandemic have been highlighted. The results from this study will be helpful in addressing the issues related to delayed care for heart patients, thereby helping in reducing the mortality rate and improving overall health.

Modeling Hospital Operating Theater Services: A System Dynamics Approach

Background: A hospital’s operating theater service system is a large-scale, complicated system that must be carefully managed to offer the best possible results for its patients. Unlike other industries such as manufacturing and logistics, system dynamics (SD) methodologies are not extensively applied in hospital operating theaters. This study deals with the future development and possible future scenarios for hospital operating rooms in Bangladesh. Methods: Due to demographic dynamics and demographic processes, increased pressures on hospital care are expected in Bangladesh. The SD model anticipates possible future scenarios, reconciles service capacities and the variability of patient demand, and reduces patient congestion and waiting times in the hospital area. This study introduces a causal loop diagram to show a causal link between the hospital operating theater system variables. It also introduces a stock flow diagram to understand the dynamic behavior of the system. Results: The model validation testing reports that in extreme conditions, such as a 50% reduction in the patient arrival rate, the model is valid and runs as usual. Conclusions: This first work of SD modeling for hospital operating theater systems can help healthcare managers, decision makers, or researchers of any responsibility level make better predictions in order to reduce patient waiting times and backlogs and make appropriate decisions.

Process Modeling a Radiation Oncology Clinic Workflow From Therapeutic Simulation to Treatment: Identifying Impending Strain and Possible Treatment Delays

The administration of safe, high-quality radiation therapy requires the systematic completion of a series of steps from computed tomography simulation, physician contouring, dosimetric treatment planning, pretreatment quality assurance, plan verification, and, ultimately, treatment delivery. Nevertheless, due consideration to the cumulative time required to complete each of these steps is often not given sufficient attention when determining patient start date. We set out to understand the systemic dynamics as to how varying patient arrival rate can affect treatment turnaround times using Monte Carlo simulations. We developed a process model workflow for a single physician, single linear accelerator clinic that simulated arrival rates and processing times for patients undergoing radiation treatment using the AnyLogic Simulation Modeling software (AnyLogic 8 University edition, v8.7.9). We varied the new patient arrival rate from 1 to 10 patients per week to understand the effect of treatment turnaround times from simulation to treatment. We used processing-time estimates determined in prior focus studies for each of the required steps. Altering the number of patients simulated from 1 per week to 10 per week resulted in a corresponding increase in average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patients from simulation to treatment ranged from 6 to 12 days. To compare individual distributions, we used the Kolmogorov-Smirnov statistical test.We found that altering the arrival rate from 4 patients per week to 5 patients per week resulted in a statistically significant change in the distributions of processing times (P=.03). The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery while ensuring quality and safety.

Design mechanism to coordinate a hierarchical healthcare system: Patient subsidy vs. capacity investment

Motivated by the problems of imbalance between the supply of and demand for the different levels of hospitals in a hierarchical healthcare system, we propose two contract mechanisms: patient subsidy and capacity investment. Under the patient subsidy scheme, each patient who chooses the primary hospital receives a subsidy from the funder; whereas, under the capacity investment scheme, the primary hospital itself receives investment compensation for expanding service capacity. By employing a game-theoretic queueing model, we determine the optimal decisions (i.e., the patient arrival rate at each hospital, the funder subsidy rate, the primary hospital's capacity allocation rate, and the funder's compensation rate) and characterize the equilibrium outcomes (i.e., social welfare, operating profit, patient utility, and waiting time) for both contract mechanisms. We find that the funder's budget and the size of the patient population play a critical role in scheme performance. The funder must secure a sufficient budget to ensure the effectiveness of either scheme. When the healthcare market is small, the patient subsidy scheme is effective in mitigating the issue of an imbalanced hierarchical system; however, when the market is large and growing, the capacity investment scheme effectively encourages capacity expansion to accommodate the growing demand. Therefore, it is suggested that the funder makes policy decisions based on both the existing and anticipated healthcare system (i.e., market size and development trends), the root issues affecting the system hierarchy, and the associated costs. Additionally, we find that hospital profitability conflicts with total social welfare under some circumstances, and that the funder must therefore strive to align the interests of hospitals with society in order to ensure a successful implementation of the contract scheme.

Process modeling radiation oncology clinic workflow from therapeutic simulation to treatment: Identifying impending strain and possible treatment delays.

39 Background: The administration of safe, high-quality radiotherapy requires the systematic completion of a series of steps from CT simulation, physician contouring, dosimetric treatment planning, pre-treatment quality assurance, plan verification, and ultimately treatment delivery. Nevertheless, due consideration to the cumulative time required to complete each of these steps is often not given sufficient attention when determining patient start date. On one hand, the nature of cancer therapy relies on timely treatment delivery. On the other hand, overly ambitious treatment turnaround times can lead to staff burnout, and result in medical errors. We sought to better understand how changes in patient volume could impact turnaround time through a simulation-based study. Methods: We developed a process model workflow for a single physician, single linear-accelerator clinic that simulated arrival rates and processing times for patients undergoing radiation treatment using AnyLogic Simulation Modeling software (AnyLogic 8 University edition, v8.7.9). We varied the new patient arrival rate from 1 to 10 patients per week to understand the impact treatment turnaround times from simulation to treatment. We utilized processing time estimates for each of the required steps as was determined in a departmental focus group study. We assumed an eight hour workday, and that contouring, treatment planning, verification, and treatment delivery followed the following distributions, respectively (in hours): triangular (2, 4, 16); triangular (8, 16, 60); triangular (0.25, 0.5, 1). Results: Altering the number of patients simulated per week from 1-10 patients resulted in a corresponding increase average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patient from simulation to treatment ranged from 6-14 days. To compare individual distributions, we utilized the Kolmogorov-Smirnov (K-S) statistical test. We found that altering the arrival rate from 4 patients per week to 5 patients per week resulted in a statistically significant change in the distributions of processing times (p = 0.03) and resulted in a corresponding increase in the maximum patient processing time from approximately 6 days to 12 days, or from approximately 1 week to 2 weeks. Conclusions: The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. However, sustained increases in volume could require increased staffing to ensure timely treatment delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery.

Enhancing staffing methods and improving the admission process of a psychiatric hospital using simulation

ABSTRACT This study considers how patients with severe mental illness are referred to a psychiatric hospital from outpatient settings, community mental health centers, or general hospitals. Due to the complexities of many of the patients, the referral process has multiple steps and was experiencing significant delays that both hindered patient care and drove up system costs. The combination of a multistage patient assessment process and the need to observe patients prior to transfer create unique system constraints and objectives. An investigation of potential root causes of admissions process delay determined that insufficient capacity in the Mental Health Disorder Identification (MHDI) process was the primary cause. Discrete event simulation was used to analyze eight different capacity scenarios for MHDI resulting in a recommendation to increase daily MHDI capacity and to provide backup capacity to cover staff vacation days, the latter being one of the more important findings. The simulation model is also used to assess what capacity improvements are required to keep PM times low for up to a 25% increase in the patient arrival rate.

Simulation-based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital.

The emergency department (ED) is a gateway to hospitals and is in the centre of hospital management efforts. It is often saturated by a continuous flow of patients, which causes excessive patient waiting time. This study integrates simulations with optimisation to design planning decision support for an ED. We considered all the processes of the ED, from triage to bed assignment. This study's main objective was to determine the optimal number of doctors, nurses, and beds required to schedule patients with different acuity levels to minimise both the total patient waiting time and the patient average length of stay and balance the resource utilisation rates. The problem is also characterised by multiple uncertainties, such as the patient arrival rate and service times in each stage of the process. We first propose a stochastic mixed-integer programing model that is solved using the sample average approximation approach. The resulting resource sizing is then evaluated using a discrete-event simulation model by comparing different patient scheduling rules. Numerical experiments highlight the performance of the proposed approach using data from a Tunisian ED hospital.

Simulated annealing approach for outpatient scheduling in a haemodialysis unit

National Renal Registry Malaysia has reported that the dialysis treatment demand among chronic kidney and end-stage kidney disease patients rises yearly. However, available haemodialysis (HD) units have limited facilities to meet the current and increasing demand. This leads to congestion, long waiting times, and an increase in the duration of treatment (DOT) among HD patients during their treatment sessions. Two essential factors in providing optimal treatment plans are outpatient scheduling and nurse assignment. Therefore, the objectives of this study are to minimise patients' total DOT, including the waiting time for pre-dialysis and post-dialysis sessions, which also includes determining the amount of patient flow in an HD unit. Regarding the first objective, we include simulated annealing (SA) into our simple heuristics (SH) in the patient scheduling optimisation model. Here, the initial solution obtained from the method can be improved. The backtracking heuristic (BH) is then applied to the nurse assignment problem, where at least two nurses are needed for each dialysis patient. The results show that the solutions obtained for outpatient scheduling by SA are efficient and have significantly reduced the computational time compared with the SH, even when considering more patients on the waiting list. As for total DOT, we obtain the optimum value compared to the average DOT values for both 3-hour and 4-hour sessions. Besides, a discrete-event simulation (DES) experiment of patient flow in an HD unit was performed by gradual variations in patient arrival rates, λ, to avoid congestion in the system. DES has the potential to accommodate emergency patients that seek HD treatment without causing much disruption to the system.

Discrete-event models for the simulation of computed tomography sectors according to hospital structural/organizational changes and expected patient arrival rates.

To analyze the types of computed tomography (CT) scanners most suitable for different hospital sizes and 'scenarios' (exam rates and structural/organizational changes), using discrete-event simulation models. CT exams were divided into stages, measured during on-site surveys at CT services in small and average size private hospitals. Ten devices in nine health units, five cities and two states of Brazil were studied to this end, and the following data were collected: Time spent in each stage for each type of exam; average monthly number of exams performed and general characteristics of exams. Three arrival rates were defined (103, 154 and 206 patients/day), representing expected demand for the studied units. From these parameters, six scenarios were simulated, consisting of changes in personnel and hospital structure (e.g., 'adding a changing room') in a base scenario (one CT, one changing room, no nursing assistance, arrival rate 1). It was possible to identify a scenario most useful for very large demands, such as large emergency hospitals in big cities, (a CT, nursing assistance and three changing rooms added to the base scenario). Another identified scenario was more adequate for small demands (adding a changing room to the base scenario). Administrative/organizational measures are a very important factor in defining productivity in a hospital imaging sector. The focus of these measures should be on detecting bottlenecks and improving processes, regardless of the type of equipment used.

Optimisation of Patient Flow and Scheduling in an Outpatient Haemodialysis Clinic

The demand for renal replacement therapy (RRT) from the growing number of patients suffering from chronic kidney disease (CKD) and end stage renal disease (ESRD) in Nigeria is reported to be on the rise. However, dialysis clinics are few with limited facilities to meet the increasing demand leading to congestion, long waiting time and increased length of stay (LOS) in dialysis clinics. This paper presents an optimisation model for scheduling patient flow in an outpatient haemodialysis clinic. The objective is to minimize patient LOS using Genetic Algorithm (GA), implemented in Python programming language with Spyder Integrated Development Environment (IDE). The model was tested using data obtained from a haemodialysis clinic, in Lagos, Nigeria. The model generated optimum LOS values (193.01, 275.02 and 390.01) minutes compared to the mean LOS values at the haemodialysis clinic (235.50, 296.62 and 424.50) minutes for the 3-hour, 4-hour and 6-hour dialysis sessions. Furthermore, a simulation experiment of patient flow in a typical haemodialysis clinic was performed by gradual variations in patient arrival rates, λ. Simulation results at (λ=0.1,0.2,0.3,0.4) revealed mean LOS (minutes) as (312.85 ± 73.45, 348.18 ± 84.89, 342.18 ± 81.30, 305, 28 ± 63.67) respectively. The optimisation model was effective in reducing patient LOS.

ICU Capacity Management During the COVID-19 Pandemic Using a Stochastic Process Simulation

Introduction: The current COVID-19 pandemic leads to a massive influx of patients to the ICU. Epidemic models focus on the demand for care capacity. Here, we present a model for the number of COVID-19 and non-COVID-19 patients that can be served for a given ICU capacity. The results may give direction to expansion decisions during the pandemic. Methods: We adapted a stochastic patient flow simulation model, originally designed to assess performance metrics for ICU design decisions, to support capacity management decisions for hospitals that are trying to assess the impact of a sustained increase in ICU demand for COVID-19 patients. We also account for the impact on such decisions for the ability to provide acceptable service levels to other ICU patients. Results: We report potential COVID-19 patient flow rates for a range of potential COVID-19 patient arrival rates and ICU capacity and estimate non-COVID-19 unplanned patient ICU capacity, based on data from the Amsterdam University Medical Centres, location AMC. We provide a link to web-based code for other hospitals to use.

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example.

Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

Optimization of Healthy Service by Implementing the System Queues on Social Insurance Administration Organization (BPJS) in Dr. H. Marzoeki Mahdi Hospital in Bogor

Patient queue buildup on Social Insurance Administration Organization (BPJS) in the Dr. H. Marzoeki Mahdi hospital (RSMM) in Bogor is a problem that often occurs. This is caused by the patient's arrival rate exceeds the service facilities provided. To reduce the buildup of queues and improve services, queueing M/M/S model is applied in BPJS patient. M/M/S model is used so that the Ministry can optimize the management system as well as reduce queues at the BPJS patients patient service (TPP) in Bogor RSMM. On-site system queues on the TPP can be done at a time when the number of staff service (counter) at least 5 officers of service so that in addition to be able to reduce the chance of a busy period of previous service officer can also optimize on-site services, but for the wait and cost expenditures costs of facilities not significant therefore need to add 1 more Ministry officials became 6 officer's service in order to improve the facilities of the Ministry on patients as well as patients BPJS TPP not too long to wait in the process the queue.

The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory.

Queuing theory can and has been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper, we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and 'treatment' rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision-making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool, reducing variation should be considered.

Seismic Risk Assessment of an Emergency Department of a Chilean Hospital Using a Patient-Oriented Performance Model

After an earthquake, hospital emergency departments need to provide continuous health care services to respond to the eventual sudden increase in injured people. The service performance of an emergency department is influenced by internal factors, such as physical damage and staff availability, and external factors, such as an increased patient arrival rate and disruptions in its supply chain. This research presents a quantification methodology for the performance of the emergency department. The novelty of the proposed approach lies in the explicit integration of the inelastic structural and nonstructural response of the building and damage with its loss of functionality, downtime, and emergency patient treatment rate. A discrete event simulation model is used to model the flow of patients within the different units of the emergency department. The seismic risk is expressed as return periods of exceeding different levels of patient waiting times. Results show that 1,000 and 30,000 accumulated waiting hours correspond to return periods of 100 and 1,000 years, respectively. It is concluded that this model may contribute to improving the risk management of critical emergency department infrastructure.

Patient arrival pattern and waiting time analysis for a tertiary hospital radiology department

Background: High patient load and ineffective appointment scheduling has been linked to hospital overcrowding. This article investigated the appointment scheduling method, patient waiting times and overall treatment times. These figures were subsequently compared against patient arrival and examination times. Method: In total, 768 adult ambulant patients were part of a real-time observation that was conducted in a tertiary hospital's radiology department in Nigeria. The scheduling method, arrival time, treatment time and waiting time for each patient was noted. The analytical software, MATLAB, was used for data analysis. Results: A single block appointment time of 7.30 was given to all patients. For any given day, mean patient arrival per hour (arrival rate) was 7 ± 5.4 patients per hour. Patient arrival rate distribution showed a pattern that resulted in the identification of three segments with stable arrival rates: 7:00–10:00, 10:00-13:00 and 13:00-16:00 with arrival rates of 12, 6 and 2 patients per hour, respectively. The mean waiting time was 116.2 minutes.

A simulation study of bed allocation to reduce blocking probability in emergency departments: A case study in China

Improving the bed allocation policy can alleviate the blocking, a situation where admitted patients cannot be transferred from the Emergency Departments (the upstream) to inpatient departments (the downstream) due to unavailability of beds. We aim to find a bed allocation policy that reduces the blocking probability and improves bed utilisation considering the interaction between the neighboring departments, as well as the priorities of different departments. This concept and the proposed model are tested in a real-life case at a hospital in China. A two-stage bed allocation simulation model is developed to evaluate the effect of bed assignment on the blocking probability and bed utilisation. Results show that the blocking probabilities can be reduced by 1.32%–8.98% and 1.74%–4.16% under different priority assignment cases and total number of beds. Sensitivity analyses are performed to examine the effect of varying patient arrival rate on the performance of bed allocation policy.