Ahmad Hamdan Abdullah Fahim, Abu Bakar Azuraliza
Pathology Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Kuala Lumpur, Malaysia.
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Malays J Med Sci. 2023 Oct;30(5):169-180. doi: 10.21315/mjms2023.30.5.14. Epub 2023 Oct 30.
A no-show appointment occurs when a patient does not attend a previously booked appointment. This situation can cause other problems, such as discontinuity of patient treatments as well as a waste of both human and financial resources. One of the latest approaches to address this issue is predicting no-shows using machine learning techniques. This study aims to propose a predictive analytical approach for developing a patient no-show appointment model in Hospital Kuala Lumpur (HKL) using machine learning algorithms.
This study uses outpatient data from the HKL's Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP).
The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65.
The predictive model could be used to formulate intervention steps to reduce no-shows, improving patient care quality.
当患者未参加之前预约的门诊时，就会出现爽约情况。这种情况会引发其他问题，比如患者治疗的中断以及人力和财力资源的浪费。解决这一问题的最新方法之一是使用机器学习技术预测爽约情况。本研究旨在提出一种预测分析方法，利用机器学习算法在吉隆坡医院（HKL）开发患者爽约预约模型。
本研究使用了HKL患者管理系统（SPP）2019年全年的门诊数据。最终数据集包含246,943条预约记录，有13个属性用于描述性分析和预测性分析。预测性分析使用了七种机器学习算法，即逻辑回归（LR）、决策树（DT）、k近邻（k-NN）、朴素贝叶斯（NB）、随机森林（RF）、梯度提升（GB）和多层感知器（MLP）。
描述性分析表明，爽约率为28%，预约月份和患者性别等属性似乎会影响患者爽约的可能性。对预测模型的评估发现，GB模型的准确率最高，为78%，F1分数为0.76，曲线下面积（AUC）值为0.65。
该预测模型可用于制定干预措施以减少爽约情况，提高患者护理质量。
