Person: ÇAYIRLI, Tuğba
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ÇAYIRLI
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ArticlePublication Metadata only An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins(Springer Nature, 2019-06) Çayırlı, Tuğba; Dursun, P.; Güneş, E. D.; Business Administration; ÇAYIRLI, TuğbaThis study analyzes two decision levels in appointment system design in the context of clinics that face seasonal demand for scheduled and walk-in patients. The macro-level problem addresses access rules dealing with capacity allocation decisions in terms of how many slots to reserve for walk-ins and scheduled patients given fixed daily capacity for the clinic session. The micro-level problem addresses scheduling rules determining the specific time slots for scheduled arrivals. A fully-integrated simulation model is developed where daily demand actualized at the macro level becomes an input to the micro model that simulates the in-clinic dynamics, such as the arrivals of walk-ins and scheduled patients, as well as stochastic service times. The proposed integrated approach is shown to improve decision-making by considering patient lead times (i.e., indirect wait), direct wait times, and clinic overtime as relevant measures of performance. The traditional methods for evaluating appointment system performance are extended to incorporate multiple trade-offs. This allows combining both direct wait and indirect wait that are generally addressed separately due to time scale differences (minutes vs. days). The results confirm the benefits of addressing both decision levels in appointment system design simultaneously. We investigate how environmental factors affect the performance and the choice of appointment systems. The most critical environmental factors emerge as the demand load, seasonality level, and percentage of walk-ins, listed in the decreasing order of importance.ArticlePublication Metadata only Predicting the performance of queues–A data analytic approach(Elsevier, 2016) Yang, K. K.; Çayırlı, Tuğba; Low, J. M.W.; Business Administration; ÇAYIRLI, TuğbaExisting 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.EditorialPublication Metadata only The 39th international conference of the EURO working group on operational research applied to health services: ORAHS 2013 special issue(Springer Science+Business Media, 2015-09) Çayırlı, Tuğba; Gunal, M. M.; Gunes, E.; Ormeci, L.; Business Administration; ÇAYIRLI, TuğbaThe healthcare sector is facing major challenges worldwide in terms of higher demands for efficiency, quality and equity. Operational Research (OR) techniques offer valuable tools for improving the design and delivery of healthcare services (e.g., inpatient and outpatient care, admission processes, emergency services, home healthcare, etc.), as well as improving the decision-making processes and implementation of public health policies.ArticlePublication Metadata only Assessment of patient classification in appointment system design(Production and Operations Management Society, 2008-05) Çayırlı, Tuğba; Veral, E.; Rosen, H.; Business Administration; ÇAYIRLI, TuğbaThis paper investigates two approaches to patient classification: using patient classification only for sequencing patient appointments at the time of booking and using patient classification for both sequencing and appointment interval adjustment. In the latter approach, appointment intervals are adjusted to match the consultation time characteristics of different patient classes. Our simulation results indicate that new appointment systems that utilize interval adjustment for patient class are successful in improving doctors' idle time, doctors' overtime and patients' waiting times without any trade-offs. Best performing appointment systems are identified for different clinic environments characterized characterized by walk-ins, no-shows, the percentage of new patients, and the ratio of the mean consultation time of new patients to the mean consultation time of return patients. As a result, practical guidelines are developed for managers who are responsible for designing appointment systems.ArticlePublication Metadata only Altering the environment to improve appointment system performance(Informs, 2019-06) Çayırlı, Tuğba; Yang, K. K.; Business Administration; ÇAYIRLI, TuğbaCurrent 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.ArticlePublication Metadata only Outpatient appointment scheduling in presence of seasonal walk-ins(Springer Nature, 2014-04) Çayırlı, Tuğba; Güneş, E. D.; Business Administration; ÇAYIRLI, TuğbaThis study investigates appointment systems (AS), as combinations of access rules and appointment-scheduling rules, explicitly designed for dealing with walk-in seasonality. In terms of 'access rules', strategies are tested for adjusting capacity through intra-week, or monthly seasonality of walk-ins, or their combined effects. In terms of 'appointment rules', strategies are tested to determine which particular slots to double-book or leave open in cases where seasonal walk-in rates exceed or fall short of the overall yearly rate. In that regard, this study integrates capacity and appointment decisions, which are usually addressed in an isolated manner in previous studies. Simulation optimization is used to derive heuristic solutions to the appointment-scheduling problem, and the findings are compared in terms of in-clinic measures of patient wait time, physician idle time and overtime. The goal is to provide practical guidelines for healthcare practitioners on how to best design their AS when seasonal walk-ins exist.ArticlePublication Metadata only 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, TuğbaThis 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.ArticlePublication Metadata only FASStR: A framework for ensuring high-quality operational metrics in health care(Managed Care & Healthcare Communications, LLC, 2020-06) Torabi, E.; Çayırlı, Tuğba; Froehle, C. M.; Klassen, K. J.; Magazine, M.; White, D. L.; Ward, M. J.; Business Administration; ÇAYIRLI, TuğbaOBJECTIVES: Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible. STUDY DESIGN: Retrospective analysis. METHODS: Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research. RESULTS: We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators. CONCLUSIONS: The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.ArticlePublication Metadata only 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, TuğbaThis 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.ArticlePublication Metadata only 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, TuğbaThis 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.