Browsing by Author "Yang, K. K."

Now showing 1 - 5 of 5

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Altering the environment to improve appointment system performance
(Informs, 2019-06) Çayırlı, Tuğba; Yang, K. K.; Business Administration; ÇAYIRLI, Tuğba
Current research on clinic performance is focused primarily on appointment scheduling rather than shaping the clinical environments. The goal of this study is to investigate the impact of environmental factors on the total cost performance of a clinic, measured as a weighted sum of patients' wait times and physician's idle time and overtime. The environmental factors investigated include the variability of service times, the probabilities of no-shows and walk-ins, the number of appointments per session, and the cost ratio of physician's time to patients' time. The effects of these factors are evaluated using a near-optimal rule that already adjusts the patients' appointment times to minimize the negative effects of these factors so that their residual or true effects on total cost performance can be isolated. As a result, this study provides useful insights for healthcare practitioners in prioritizing their efforts in managing the different sources of variability to further improve the clinic performance beyond the use of an optimal or near-optimal appointment rule. Additional experiments are conducted on the effects of patient and physician unpunctuality, which have been studied to a lesser extent in prior literature.
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Managing clinic variability with same-day scheduling, intervention for no-shows, and seasonal capacity adjustments
(Taylor & Francis, 2020-01-02) Yang, K. K.; Çayırlı, Tuğba; Business Administration; ÇAYIRLI, Tuğba
This study investigates demand and capacity strategies for managing clinic variability. These include (i) same-day scheduling to control random walk-ins, (ii) no-show intervention, where the clinic calls advance-booked patients a day before to identify and release cancelled slots to same-day patients, and (iii) adjustments to daily number of appointments for advance-booked patients to match seasonal variations in same-day demand. These strategies are tested over the individual-block/fixed-interval (IBFI) and the Dome appointment rules. Our results show that choosing the appropriate refinements in the order of appointment rules, same-day scheduling, no-show intervention, and capacity adjustment provides maximum improvement. The total cost benefit of demand strategies (i) and (ii) is 7 to 21%, whereas the benefit of capacity strategy (iii) is as high as 6%. Our study affirms the universality of the Dome rule to perform well when combined with the demand and capacity strategies across different environments.
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Predicting the performance of queues–A data analytic approach
(Elsevier, 2016) Yang, K. K.; Çayırlı, Tuğba; Low, J. M.W.; Business Administration; ÇAYIRLI, Tuğba
Existing models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, combining computer simulation to generate the data and an advanced non-linear regression technique called the Alternating Conditional Expectation (ACE) to construct a set of easy-to-use equations to predict the performance of queues with a scheduled start and end time. Our results show that the equations can accurately predict the queue performance as a function of the number of servers, mean arrival load, session length and service time variability. To further facilitate its use in practice, the equations are developed into an open-source online tool accessible at http://singlequeuesystemstool.com/. The proposed procedure of data analytics can be used to model other more complex systems.
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A universal appointment rule in the presence of no-shows and walk-ins
(Wiley, 2012-07) Çayırlı, Tuğba; Yang, K. K.; Quek, S. A.; Business Administration; ÇAYIRLI, Tuğba
This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no-shows, walk-ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no-shows and walk-ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no-shows and walk-ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open-source decision-support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.
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A universal appointment rule with patient classification for service times, no-shows and walk-ins
(Informs, 2014) Çayırlı, Tuğba; Yang, K. K.; Business Administration; ÇAYIRLI, Tuğba
This study evaluates patient classification for scheduling and sequencing appointments for patients differentiated by their mean and standard deviation of service times, no-show, and walk-in probabilities. Alternative appointment systems are tested through simulation using a universal Dome rule and some of the best traditional appointment rules in the literature. Our findings show that the universal Dome rule performs better in terms of reducing the total cost of patient’s waiting time, doctor’s idle time, and overtime, and its performance improves further with the right sequencing of patient groups. Although it is a challenge to find the best sequence, we propose a heuristic rule that successfully identifies the best sequence with an accuracy level of 98% for the universal Dome rule. Sensitivity analyses further confirm that our findings are valid even when assumptions on patient punctuality and service time distributions are relaxed. To facilitate the use of our proposed appointment system, an open source online tool is developed to support practitioners in designing their appointment schedules for real clinics.