Predicting no-show appointments in a pediatric hospital in Chile using machine learning

Abstract

The Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I and II errors based on cost-effectiveness criteria. 20.4% of the 395,963 appointments considered presented no-shows, with ophthalmology showing the highest rate among specialties at 29.1%. Patients in the most deprived socioeconomic group according to their insurance type and commune of residence and those in their second infancy had the highest no-show rate. The history of non-attendance is strongly related to future no-shows. An 8-week experimental design measured a decrease in no-shows of 10.3 percentage points when using our reminder strategy compared to a control group. Among the variables analyzed, those related to patients’ historical behavior, the reservation delay from the creation of the appointment, and variables that can be associated with the most disadvantaged socioeconomic group, are the most relevant to predict a no-show. Moreover, the introduction of new cost-effective metrics significantly impacts the validity of our prediction models. Using a prototype to call patients with the highest risk of no-shows resulted in a noticeable decrease in the overall no-show rate.

An artificial intelligence-based model to reduce the no-show rate in outpatient clinics of an academic hospital

“…Dashtban et al [24] considered eight variable groups for prediction of non-attendance, including demographic data, patient history, appointment characteristics, time variables, patient appointment history, socioeconomic data of the patient, weather conditions and admission history. A study by Dunstan et al [25] from Chile concluded that factors pertaining to a socioeconomically disadvantaged background were the most relevant to predict noshows. The predictive value of variables linked to low socioeconomic status have also been observed in other studies, although there seems to be a country-speci c component [6, 26], which further strengthen the need for models highly adjusted to the local patient population.…”

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

“…Factors such as reminders, available appointment times, wait times, patient education, and procedure quality affect patients' likelihood of attending their appointments [8,9]. At the same time, economic conditions, health insurance, and transportation also cause patients to miss their appointments [10,11]. Health systems develop effective strategies by taking these factors into account to increase patients' "show-up" situations and reduce "no-show" situations [8].…”