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An ML Model to Predict the Number of OPD Appointments for a Given Future Time Period

  • Unique Paper ID: 172550
  • Volume: 11
  • Issue: 9
  • PageNo: 101-106
  • Abstract:
  • Efficient management of outpatient waiting time is essential for enhancing hospital workflows and patient satisfaction. This study investigates the prediction of outpatient waiting times in a tertiary care hospital in Madhya Pradesh, India, utilizing machine learning (ML) algorithms. Four ML models were developed and evaluated: Linear Regression (LR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and K-Nearest Neighbors (KNN). Among these, the GBDT model demonstrated superior predictive accuracy with a Mean Absolute Error (MAE) of 10.5 minutes and an R-squared value of 0.92. These findings have significant implications for improving resource allocation and minimizing patient waiting times in Indian hospitals. Background: In the Indian healthcare landscape, outpatient departments (OPDs) form a critical entry point for patients seeking medical attention. With a burgeoning population and limited healthcare infrastructure, Indian hospitals, especially in states like Madhya Pradesh, face significant challenges in managing patient flow and ensuring timely treatment. Overcrowding in OPDs leads to increased waiting times, adversely impacting patient satisfaction and hospital efficiency. The integration of data-driven approaches, such as machine learning (ML), offers a promising solution to address these issues. ML models can analyze historical patient data to predict waiting times, enabling hospitals to allocate resources dynamically and streamline workflows. While similar studies have been conducted in other countries, the unique demographic, socio-economic, and healthcare challenges in India necessitate localized research and solutions. This study focuses on applying ML techniques to predict outpatient waiting times in a tertiary care hospital in Madhya Pradesh, providing actionable insights for healthcare administrators. Methods: First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. Results: The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Conclusions: Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 9
  • PageNo: 101-106

An ML Model to Predict the Number of OPD Appointments for a Given Future Time Period

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