Analysis of Outpatient Visit Pattern and Waiting Time Prediction for Selected Public Health Clinics in Selangor

Abstract

Public clinics are the preferred choice for health treatment worldwide. This preference results in many outpatients seeking remedies at public clinics and is the leading factor in patients’ long waiting time. The definition for waiting time is when a patient has to remain at the clinic for treatment. The waiting time starts with the registration process until consultation with the physician. Lengthy waiting time is the leading cause of patient dissatisfaction. The discontent is especially so when there is no convenient waiting facility. The objective of this study is to analyze outpatient visits patterns to public outpatient clinics. The recognized patterns will form the basis to recommend which public outpatient clinics are the best to visit at a specific time to minimize waiting time. The waiting time is also predicted based on the arrival time. Fifteen public outpatient clinics in Selangor that use an electronic medical record system provided this study’s data. The data analysis shows a correlation between patient waiting time, day, the month of visit, patient age, and consultation time. Past research shows that classification of machine learning methods can predict the waiting time. This study has demonstrated that Linear Discriminant Analysis creates the best classification model for Puchong and Batu 9 Cheras datasets. Support Vector Machine (SVM) is the best classifier for the Anika dataset.