Patient Admission Projection Using Facebook Prophet

Abstract

Healthcare facilities face the continual challenge of managing patient admissions efficiently while maintaining high standards of care. Accurate forecasting of patient admissions is crucial for optimizing resource allocation, ensuring adequate staffing, and enhancing overall operational efficiency. To address this challenge, this research paper explores the application of Facebook Prophet, an advanced time series forecasting tool, in predicting patient admissions within healthcare facilities. Facebook Prophet, a specialized time series forecasting model developed by Facebook's Core Data Science team, is then employed to model and predict patient admissions. Facebook Prophet is particularly well-suited for handling time series data characterized by seasonality, trends, and special events. The model's flexibility and ease of use make it an ideal choice for healthcare providers seeking to leverage advanced analytics techniques for predictive purposes. In the implementation of Facebook Prophet, the model is meticulously trained and validated using the pre-processed historical admission data. The model's ability to capture complex temporal patterns, including seasonality and special events such as holidays or outbreaks, is thoroughly evaluated. By incorporating these patterns into the forecasting process, the model can provide accurate predictions of future patient admissions. By accurately predicting patient admissions, healthcare facilities can optimize resource allocation, streamline operations, and ensure adequate staffing levels to meet patient demand. Additionally, the model enables healthcare providers to proactively plan for special events or seasonal fluctuations in admission rates, further enhancing operational efficiency. The application of Facebook Prophet holds significant promise for improving patient care delivery and operational outcomes in healthcare facilities.