Advancing Healthcare Resource Allocation through Multi-Step Ahead Time Series Forecasting

Vijaya Chaitanya Palanki

Abstract: Improving efficiency in healthcare resource allocation is crucial for high quality patient care while managing costs. This paper dives into healthcare resource allocation with the application of multi-step ahead time series forecasting techniques. We investigate various advanced forecasting techniques, their application to multiple healthcare scenarios, and the challenges in implementing these techniques in complex healthcare systems. Our study focuses on the distinct characteristics of healthcare data, emphasizing the critical role of uncertainty quantification and the need to incorporate domain expertise into forecasting models. We outline a framework to help select the most suitable forecasting techniques tailored to specific challenges in healthcare resource allocation. Additionally, we explore how these improved forecasting methods can enhance resource utilization and lead to better patient outcomes.

Keywords: Multi-step ahead forecasting, healthcare resource allocation, time series analysis, predictive modeling, uncertainty quantification, machine learning in healthcare