Predicting and Reducing Patient Waiting Times in Dental Clinics Using Machine Learning: A Case Study from Türkiye

Abstract

Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Türkiye. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.

Long waiting times in polyclinics are a critical factor affecting patient satisfaction and the efficient use of healthcare personnel and resources. This study applied machine learning (ML) algorithms to predict and reduce patient waiting times in a dental clinic in Türkiye. The daily data collected from the clinic included variables such as patient satisfaction, appointment patients, Walk-in patients, number of doctors and nurses, and dental technicians on duty. Six ML algorithms were tested: Decision Trees (DT), Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Kernel Regression (KR), and Neural Networks (NN). Among these, the GPR model achieved the best performance, accurately predicting patient waiting times with an R2 value of 0.936 and RMSE of 0.075. This study highlights the potential of ML methods to enhance operational efficiency in healthcare management.