Simulation of Appointment Scheduling Policies: a Study in a Bariatric Clinic.

PURPOSE: Appointment scheduling systems traditionally book patients at fixed intervals, without taking into account the complexity factors of the health system. This paper analyzes several appointment scheduling policies of the literature and proposes the most suitable to a bariatric surgery clinic, considering the following complexity factors: (i) stochastic service times, (ii) patient unpunctuality, (iii) service interruptions, and (iv) patient no-shows. MATERIALS AND METHODS: We conducted the study using data collected in a bariatric surgery clinic located in Rio de Janeiro, Brazil. The dataset presented 1468 appointments from June 29, 2015, to June 29, 2016. We comparatively evaluate the main literature policies through a discrete event simulation (DES). RESULTS: The proposed policy (IICR) provides a 30% increase in attendance and allows a decrease in the total cost, maintaining the level of service in terms of average waiting time. CONCLUSION: IICR was successfully implemented, and the practical results were very close to the simulated ones.

Predicting Patient No-show Behavior: a Study in a Bariatric Clinic.

PURPOSE: No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic. MATERIALS AND METHODS: We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty. RESULTS: The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%. CONCLUSION: Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient's no-show probability.