Design and comparison of scheduling strategy for teleconsultation

2021 ◽  
pp. 1-15
Author(s):  
Yan Qiao ◽  
Lun Ran ◽  
Jinlin Li ◽  
Yunkai Zhai
Keyword(s):  
Waiting Time ◽  
Operations Management ◽  
System Performance ◽  
Discrete Event ◽  
Arrival Rate ◽  
Disease Diagnosis ◽  
Scheduling Strategy ◽  
Static Strategy ◽  
Current Resource ◽  
The Impact

BACKGROUND: Telemedicine is playing an increasingly more important role in disease diagnosis and treatment. The market of telemedicine application is continuously promoted, thus bringing some issues on telemedicine operations management. OBJECTIVE: We aimed to compare the teleconsultation scheduling performance of newly designed proactive strategy and existing static strategy and explore the decision-making under different conditions. METHODS: We developed a discrete-event simulation model based on practical investigation to describe the existing static scheduling strategy of teleconsultation. The static strategy model was verified by comparing it with the historical data. Then a new proactive strategy was proposed, whose average waiting time, variance of waiting time and completed numbers were compared with the static strategy. RESULTS: The analysis indicated that the proactive strategy performed better than static under the current resource allocation. Furthermore, we explored the impact on the system of both strategies varying arrival rate and experts’ shift time. CONCLUSIONS: Under different shift times and arrival rates, the managers of telemedicine center should select different strategy. The experts’ shift time had a significant impact on all system performance indicators. Therefore, if managers wanted to improve the system performance to a greater extent, they needed to reduce the shift time as much as possible.

Performance analysis and improvement of a typical telephone response system of VA hospitals

2015 ◽  
Vol 35 (8) ◽  
pp. 1098-1124 ◽  
Author(s):  
Jing Shi ◽  
Ergin Erdem ◽  
Yidong Peng ◽  
Peter Woodbridge ◽  
Christopher Masek
Keyword(s):  
Waiting Time ◽  
Resource Sharing ◽  
System Performance ◽  
Current System ◽  
Discrete Event ◽  
Cost Effective ◽  
Content Type ◽  
Scarce Resources ◽  
Response System ◽  
Va Hospitals

Purpose – Telephone response system is the frontline of hospital operations. The purpose of this paper is to analyze a representative telephone response system of Veterans Affairs (VA) hospitals, address the existing inefficiency issues such as long call waiting time, and improve system resilience to changes. Design/methodology/approach – Resource sharing schemes are proposed to improve the system performance in answering calls related to appointment booking and medication renewal. Discrete event simulation is adopted to model the current system and the resource sharing schemes. Findings – The resource sharing schemes dramatically improve system performance reflected by the decrease of call waiting time and queue, as well as the extreme high utilization of agents in a key unit. Compared with the less desired alternative of hiring additional employees to mitigate the performance issues, the resource sharing schemes perform at par or even better. Sharing more resource during the peak hours can further balance the agent workload. Practical implications – The resource sharing schemes could alleviate staffing shortage, long waiting time, and high-abandonment rate in the bottle-beck unit of the system, and lead to better utilization of scarce resources on the hospital floor. The concept reflects localized centralization efforts in traditionally highly decentralized telephone operations in hospital systems. Originality/value – This research provides a structured approach to analyze the operations of a VA telephone response system. The developed simulation model is validated, and this provides a valuable tool for management to analyze the complicated telephone operations of the telephone systems of other VA and non-VA hospitals. Resource sharing constitutes a cost-effective solution for improving system performance and resilience.

Evaluating intensive care unit admission and discharge policies using a discrete event simulation model

SIMULATION ◽  
2020 ◽  
Vol 96 (6) ◽  
pp. 501-518 ◽  
Author(s):  
Imran Hasan ◽  
Esmaeil Bahalkeh ◽  
Yuehwern Yih
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Discrete Event ◽  
Wait Time ◽  
Average Waiting Time ◽  
Event Simulation ◽  
The Impact ◽  
Patient Mix

The efficient utilization and management of a scarce resource such as the intensive care unit (ICU) is critical to the smooth functioning of a hospital. This study investigates the impact of a set of operational policies on ICU behavior and performance. Specifically, the implemented policies are (a) wait time thresholds on how long patients can wait for an ICU bed, (b) the time windows during which patient discharges and transfers take place, and (c) different patient mix combinations. The average waiting time of patients for ICU beds and the admission ratio, the ratio of admitted patients to total ICU bed requests, are the performance measures under consideration. Using discrete event simulation, followed by analysis of variance and post hoc tests (Tukey multiple comparison), it is shown that increasing discharge windows has a statistically significant impact on the total number of admissions and average patient wait times. Moreover, average waiting time increased when wait time thresholds increased, especially when the number of emergency surgeries in the mix increased. In addition, larger proportions of elective surgery patients in the patient mix population can lead to significantly reduced ICU performance.

scholarly journals Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals

2021 ◽  
Author(s):  
Alberto De Santis ◽  
Tommaso Giovannelli ◽  
Stefano Lucidi ◽  
Mauro Messedaglia ◽  
Massimo Roma
Keyword(s):  
Emergency Department ◽  
Optimization Problem ◽  
Discrete Event ◽  
Arrival Rate ◽  
Black Box ◽  
Arrival Process ◽  
Accurate Estimation ◽  
Piecewise Constant ◽  
Piecewise Constant Approximation ◽  
The Impact

AbstractModeling the arrival process to an Emergency Department (ED) is the first step of all studies dealing with the patient flow within the ED. Many of them focus on the increasing phenomenon of ED overcrowding, which is afflicting hospitals all over the world. Since Discrete Event Simulation models are often adopted to assess solutions for reducing the impact of this problem, proper nonstationary processes are taken into account to reproduce time–dependent arrivals. Accordingly, an accurate estimation of the unknown arrival rate is required to guarantee the reliability of results. In this work, an integer nonlinear black–box optimization problem is solved to determine the best piecewise constant approximation of the time-varying arrival rate function, by finding the optimal partition of the 24 h into a suitable number of not equally spaced intervals. The black-box constraints of the optimization problem make the feasible solutions satisfy proper statistical hypotheses; these ensure the validity of the nonhomogeneous Poisson assumption about the arrival process, commonly adopted in the literature, and prevent mixing overdispersed data for model estimation. The cost function of the optimization problem includes a fit error term for the solution accuracy and a penalty term to select an adequate degree of regularity of the optimal solution. To show the effectiveness of this methodology, real data from one of the largest Italian hospital EDs are used.

scholarly journals Improving workflow control in radiotherapy using discrete-event simulation

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Bruno Vieira ◽  
Derya Demirtas ◽  
Jeroen B. van de Kamer ◽  
Erwin W. Hans ◽  
Wim van Harten
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Time ◽  
Waiting Times ◽  
Discrete Event ◽  
Hybrid Strategy ◽  
Event Simulation ◽  
Baseline Case ◽  
Pull Strategy ◽  
Pre Treatment ◽  
The Impact

Abstract Background In radiotherapy, minimizing the time between referral and start of treatment (waiting time) is important to possibly mitigate tumor growth and avoid psychological distress in cancer patients. Radiotherapy pre-treatment workflow is driven by the scheduling of the first irradiation session, which is usually set right after consultation (pull strategy) or can alternatively be set after the pre-treatment workflow has been completed (push strategy). The objective of this study is to assess the impact of using pull and push strategies and explore alternative interventions for improving timeliness in radiotherapy. Methods Discrete-event simulation is used to model the patient flow of a large radiotherapy department of a Dutch hospital. A staff survey, interviews with managers, and historical data from 2017 are used to generate model inputs, in which fluctuations in patient inflow and resource availability are considered. Results A hybrid (40% pull / 60% push) strategy representing the current practice (baseline case) leads to 12% lower average waiting times and 48% fewer first appointment rebooks when compared to a full pull strategy, which in turn leads to 41% fewer patients breaching the waiting time targets. An additional scenario analysis performed on the baseline case showed that spreading consultation slots evenly throughout the week can provide a 21% reduction in waiting times. Conclusions A 100% pull strategy allows for more patients starting treatment within the waiting time targets than a hybrid strategy, in spite of slightly longer waiting times and more first appointment rebooks. Our algorithm can be used by radiotherapy policy makers to identify the optimal balance between push and pull strategies to ensure timely treatments while providing patient-centered care adapted to their specific conditions.

Search Among Queues Under Quality Differentiation

2019 ◽  
Vol 65 (8) ◽  
pp. 3605-3623 ◽  
Author(s):  
Luyi Yang ◽  
Laurens G. Debo ◽  
Varun Gupta
Keyword(s):  
Waiting Time ◽  
Operations Management ◽  
Service Providers ◽  
Mean Field ◽  
Search Behavior ◽  
Arrival Rate ◽  
Search Costs ◽  
Stopping Rules ◽  
Mean Field Model ◽  
Equilibrium Search

Customers looking for service providers often face search frictions and have to trade off quality and availability. To understand customers’ search behavior when they are confronted with a large collection of vertically differentiated, congested service providers, we build a model in which arriving customers conduct a costly sequential search to resolve uncertainty about service providers’ quality and queue length and select one to join by optimal stopping rules. Customers search, in part, because of variations in waiting time across service providers, which, in turn, is determined by the search behavior of customers. Thus, an equilibrium emerges. We characterize customers’ equilibrium search/join behavior in a mean field model as the number of service providers grows large. We find that reducing either the search cost or customer arrival rate may increase the average waiting time in the system as customers substitute toward high-quality service providers. Moreover, with lower search costs, the improved quality obtained by customers may not make up for the prolonged wait, therefore degrading the average search reward and, more importantly, decreasing customer welfare; when customers search, their welfare can even be lower than if they are not allowed to search at all. This paper was accepted by Gad Allon, operations management.

scholarly journals A challenge for healthcare system resilience after an earthquake: The crowdedness of a first-aid hospital by non-urgent patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249522
Author(s):  
You-Xuan Lin ◽  
Chi-Hao Lin ◽  
Chih-Hao Lin
Keyword(s):  
Healthcare System ◽  
Waiting Time ◽  
Emergency Care ◽  
Patient Flow ◽  
Discrete Event ◽  
Severe Case ◽  
First Aid ◽  
Response Measure ◽  
Preparedness Planning ◽  
The Impact

After a violent earthquake, the supply of medical services may fall short of the rising demand, leading to overcrowding in hospitals, and, consequently, a collapse in the healthcare system. This paper takes the emergency care system in Taiwan as the research context, where first-aid hospitals are ranked to three levels, advanced, intermediate, and general, and, currently, emphasizes on a general emergency responsibility hospital. Having limited capacity and capability, a general emergency responsibility hospital treats minor and moderate injuries, from which the majority of earthquake-induced casualties suffer. The purpose of this study is to analyze the impact of this group of earthquake-induced non-urgent patients on the performance of a hospital. A patient flow model was built to represent patients’ paths throughout emergency care. Based on the model, discrete event simulation was applied to simulate patients’ trajectories and states of a hospital under four seismic scenarios, where patient visits are 1.4, 1.6, 1.9, and 2.3 times the normal number. A healthcare performance index, Crowdedness Index (CI), is proposed to measure crowdedness on a daily basis, which is defined as the ratio of the average waiting time for treatment to the recommended maximal waiting time. Results of simulations rendered the establishment of empirical equations, describing the relation between the maximum CIs and the patient growth ratios. In the most severe case in this study, the maximum CI exceeds 92 and it takes 10 days to recover from the quality drop. This highlights the problem a general emergency responsibility hospital may encounter if no emergency response measure is implemented. Findings are provided pertaining to the predication of a recovery curve and the alarming level of patient increase, which are supportive information for preparedness planning as well as response measure formulation to improve resilience.

scholarly journals The Impact of Longer Consultation Time: A Simulation-Based Approach

2020 ◽  
Vol 11 (05) ◽  
pp. 857-864
Author(s):  
Abdulrahman M. Jabour
Keyword(s):  
Waiting Time ◽  
Quality Care ◽  
Working Hours ◽  
Discrete Events ◽  
Simulation Based ◽  
Patient Waiting Time ◽  
Free Environment ◽  
The Impact ◽  
Consultation Time ◽  
Patient Waiting

Abstract Background Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients' order of arrival than those with a shorter consultation time. Conclusion By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients' order of arrival than those with a shorter consultation time.

scholarly journals IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model

2021 ◽  
Vol 13 (8) ◽  
pp. 195
Author(s):  
Akash Gupta ◽  
Adnan Al-Anbuky
Keyword(s):  
Hip Fracture ◽  
Real Time ◽  
System Performance ◽  
Digital Health ◽  
Time Behavior ◽  
Functional Requirements ◽  
Rehabilitation Process ◽  
Rehabilitation Model ◽  
The Impact ◽  
Movement Monitoring

Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.

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