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A New Optimized Queueing Model with Compensation and Buffer
Abstract
This paper aims at designing a queueing model for clinics to reduce the waiting time for patients and increase the working efficiency for physicians. The current queueing model may cause time conflicts between patients with and without appointments especially when whom with the appointment comes late. Many hospitals tolerate the latecomers that may elicit dissatisfaction of other patients. In this research, we developed a new queueing model applying the strategy of dynamic punishment by moving back a certain percentage length of the existing queue for latecomers with appointments. Comparison of our new model with three existing ones demonstrated that ours is superior to others especially when handling the full load situation. The standard deviation of the new model is around 10% less than the other three models, indicating that fewer patients will fall into long-time waiting zone. In sum, our new model displays a better performance in case of full load situation by reducing the number of long-waiting-time patients and making the differences among waiting times smaller.
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Background:
Waiting is common in health care, delays intervention, and has negative effects on satisfaction with services.
Objectives:
To evaluate effects of a modified walk-in system, where patients were invited consecutively from the waiting list to attend the clinic on a walk-in basis, on waiting times, services, and work environment.
Study design:
Parallel-group trial.
Methods:
In all, 1286 consecutive patients in need of shoe insoles were randomized to waiting lists for modified walk-in ( n = 655) or a scheduled appointment ( n = 631). Seven staff members also participated.
Results:
The median indirect waiting time to first appointment was 40 days shorter for modified walk-in (135 days) than for scheduled appointment (175 days; p < 0.001); 17% of those randomized to modified walk-in did not attend the clinic compared to 6% for scheduled appointment ( p < 0.001). Mean direct waiting time in the waiting room was 9.9 min longer for modified walk-in than for scheduled appointment ( p < 0.001). Patients attending modified walk-in or a scheduled appointment reported similar levels of satisfaction with services. Staff reported more support from co-workers with modified walk-in than with scheduled appointment ( p = 0.041).
Conclusion:
The modified walk-in can reduce indirect waiting times without any substantial worsening of direct waiting times, service quality, or work environment. Studies are needed to investigate why many patients drop out from modified walk-in. Clinical relevance A modified walk-in system can cut the queues and create more timely interventions by reducing indirect waiting times. This system can therefore be recommended in secondary-care prosthetic and orthotic clinics to reduce patients' suffering from their health condition.
Motivated by the rising popularity of electronic appointment booking systems, we develop appointment scheduling models that take into account the patient preferences regarding when they would like to be seen. The service provider dynamically decides which appointment days to make available for the patients. Patients arriving with appointment requests may choose one of the days offered to them or leave without an appointment. Patients with scheduled appointments may cancel or not show up for the service. The service provider collects a "revenue" from each patient who shows up and incurs a "service cost" that depends on the number of scheduled appointments. The objective is to maximize the expected net "profit" per day. We begin by developing a static model that does not consider the current state of the scheduled appointments. We give a characterization of the optimal policy under the static model and bound its optimality gap. Building on the static model, we develop a dynamic model that considers the current state of the scheduled appointments, and we propose a heuristic solution procedure. In our computational experiments, we test the performance of our models under the patient preferences estimated through a discrete choice experiment that we conduct in a large community health center. Our computational experiments reveal that the policies we propose perform well under a variety of conditions.
We introduce MASON, a fast, easily extendable, discrete- event multi-agent simulation toolkit in Java. MASON was designed to serve as the basis for a wide range of multi- agent simulation tasks ranging from swarm robotics to ma- chine learning to social complexity environments. MASON carefully delineates between model and visualization, al- lowing models to be dynamically detached from or attached to visualizers, and to change platforms mid-run. We de- scribe the MASON system, its motivation, and its basic ar- chitectural design. We then discuss five applications of MA- SON we have built over the past year to suggest its breadth of utility.
Purpose
– Delay is an important issue for service providers. Indeed, previous studies have widely shown the negative effect of waiting time on consumer service satisfaction. However, being satisfied with the service seems to be insufficient for customers to remain loyal. Creating customer loyalty is even more crucial than just satisfying them. The paper aims to investigate how customers weigh up their service satisfaction and waiting time satisfaction in order to determine whether they will remain loyal or not.
Design/methodology/approach
– A survey was conducted in the Belgian health care industry. The final sample includes 946 respondents. Regression analyses were performed and the Baron and Kenny method used to test moderator and mediator impacts of variables.
Findings
– The results confirm that waiting time satisfaction is not only a service satisfaction determinant, but it also moderates the satisfaction‐loyalty relationship. Moreover, determinants of customer waiting time satisfaction include the perceived waiting time, the satisfaction with information provided in case of delays, and the satisfaction with the waiting environment. In addition, it is shown that waiting time satisfaction is a complete mediating variable in the perceived waiting time and service satisfaction link.
Originality/value
– The paper suggests several implications about the waiting time impacts on service satisfaction and customer loyalty. They show the importance of this variable in the service process and explain how to improve it.
Long waiting times for registration to see a doctor is problematic in China, especially in tertiary hospitals. To address this issue, a web-based appointment system was developed for the Xijing hospital. The aim of this study was to investigate the efficacy of the web-based appointment system in the registration service for outpatients.
Data from the web-based appointment system in Xijing hospital from January to December 2010 were collected using a stratified random sampling method, from which participants were randomly selected for a telephone interview asking for detailed information on using the system. Patients who registered through registration windows were randomly selected as a comparison group, and completed a questionnaire on-site.
A total of 5641 patients using the online booking service were available for data analysis. Of them, 500 were randomly selected, and 369 (73.8%) completed a telephone interview. Of the 500 patients using the usual queuing method who were randomly selected for inclusion in the study, responses were obtained from 463, a response rate of 92.6%. Between the two registration methods, there were significant differences in age, degree of satisfaction, and total waiting time (P<0.001). However, gender, urban residence, and valid waiting time showed no significant differences (P>0.05). Being ignorant of online registration, not trusting the internet, and a lack of ability to use a computer were three main reasons given for not using the web-based appointment system. The overall proportion of non-attendance was 14.4% for those using the web-based appointment system, and the non-attendance rate was significantly different among different hospital departments, day of the week, and time of the day (P<0.001).
Compared to the usual queuing method, the web-based appointment system could significantly increase patient's satisfaction with registration and reduce total waiting time effectively. However, further improvements are needed for broad use of the system.
This paper is focused on the factors causing long patient waiting time/clinic overtime in outpatient clinics and how to mitigate them using discrete event simulation. A two-week period of data collection is conducted in an outpatient clinic of a Singapore government hospital. Detailed time study from patient arrival to patient departure is conducted, and the possible factors causing long patient waiting time/clinic overtime are discussed. A discrete simulation model is constructed to illustrate how to improve the clinic performance by mitigating the detected factors. Simulation and implementation results show that significant improvement is achieved if the factors are well addressed.
The purpose of our study was to determine how time spent with the physician might be related to patient dissatisfaction with their waiting time. During a 2-month period, patients in our internal medicine resident continuity clinic completed a survey assessing their satisfaction with their waiting time and their estimates of their waiting time and time spent with the resident physician. For patients with long waiting times (more than 15 min in the waiting room or more than 10 min in the exam room), patient dissatisfaction with waiting time was associated with a shorter physician visit (48% were dissatisfied if the physician spent less than 15 min vs. 18% if the physician spent more than 15 min with them, p = .03). These data suggest that physicians can mediate the negative effects of long waiting times by spending more time with their patients. Future studies on patient satisfaction should consider this interaction.
Emergency department (ED) crowding represents an international crisis that may affect the quality and access of health care. We conducted a comprehensive PubMed search to identify articles that (1) studied causes, effects, or solutions of ED crowding; (2) described data collection and analysis methodology; (3) occurred in a general ED setting; and (4) focused on everyday crowding. Two independent reviewers identified the relevant articles by consensus. We applied a 5-level quality assessment tool to grade the methodology of each study. From 4,271 abstracts and 188 full-text articles, the reviewers identified 93 articles meeting the inclusion criteria. A total of 33 articles studied causes, 27 articles studied effects, and 40 articles studied solutions of ED crowding. Commonly studied causes of crowding included nonurgent visits, "frequent-flyer" patients, influenza season, inadequate staffing, inpatient boarding, and hospital bed shortages. Commonly studied effects of crowding included patient mortality, transport delays, treatment delays, ambulance diversion, patient elopement, and financial effect. Commonly studied solutions of crowding included additional personnel, observation units, hospital bed access, nonurgent referrals, ambulance diversion, destination control, crowding measures, and queuing theory. The results illustrated the complex, multifaceted characteristics of the ED crowding problem. Additional high-quality studies may provide valuable contributions toward better understanding and alleviating the daily crisis. This structured overview of the literature may help to identify future directions for the crowding research agenda.
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