Improving an outpatient clinic utilization using decision analysis-based patient scheduling

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

“…On the other hand, predictors identi ed as non-signi cant in our work displayed similar behavior in previous studies; more speci cally, gender [2,27]; schooling [28]; cancer [15]; day [2,16,23,25]; shift of the appointment [14,16]; age [2,11]; and number of consultations scheduled in previous year [29].…”

“…In our model, there are two possible explanations for this variable's behavior: rst, most appointments made for the year 2017 (58%) are for patients from other cities for which free transportation is provided by their municipalities; second, patients lack other options of specialized radiology services in their hometowns. Other studies also positively related the no-show probability with the lead time between scheduling a medical appointment and getting it [2,7,11].…”

“…There is consensus in the literature regarding the non-random nature of no-shows, and research has been published proposing statistical analyses of factors that may impact no-show rates [6,7]. Previous studies have shown that factors associated with no-show include patients' sociodemographic conditions such as age [4]; gender [7,8]; distance between home and healthcare unit [9,10]; race [11]; marital status [12,13]; level of schooling [14]; appointment characteristics -time [2], day of the week [9], and month [15]; elapsed time between appointment request and actual consultation [2]; and patient's clinical diagnosis [16].…”

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

“…Prior work has shown that factors can include gender, age [40,41], social factors [42], patient perceptions of medical providers, direct patient costs, distance to the clinic, a lack of a personal relationship with the physicians [6], adherence to physician visits [43], the perception of long waiting times [44], the delay in scheduling appointments [45], the long lead time from scheduling Research Article date to appointment date [46], and the provider's specialty [6]. Others estimated the no-show rate based on factors, such as purpose of visit, day of the week, and age using data mining techniques [47,48] or logistic regression modeling [49,50] and developed a predictive model for the probability of no-shows for groups of patients that shared common attributes, often in an attempt to minimize costs rather than predicting errors.…”

Grid Patient Appointment Template Design to Improve Scheduling Effectiveness

“…To help alleviate negative effects from these factors, a number of methods have been used to determine possible solutions, such as Monte Carlo simulation [3], stochastic modeling [4,5], dynamic/deterministic optimization models [6,7], and testing different scenarios for a clinical setting through simulation [8,9]. Solutions that have been studied to improve outpatient delivery systems include scheduling of resources [10,11], studying forecasting models [12], evaluating patients before visits [13], the best sequence for scheduling patients [14], predicting patient show or no-show [15], and adjusting appointment times [16]. One of the solutions that is studied by many researchers is redesigning patient appointment templates, also known as appointment rules.…”