A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre

TL;DR

This paper presents a hybrid data-driven and simulation framework using machine learning to predict and optimize inpatient length of stay, aiming to improve hospital efficiency and patient outcomes.

Contribution

It introduces a novel hybrid approach combining machine learning and simulation for predicting and managing patient length of stay in hospitals.

Findings

Model accurately predicts patient LoS upon admission.

Hybrid approach reduces patient length of stay in real healthcare settings.

Key factors influencing LoS identified for better resource allocation.

Abstract

Patient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve efficiency and reduce costs while enhancing patient outcomes and hospital capacity within the patient journey. By seamlessly merging data-driven techniques with simulation methodologies, the study proposes an all-encompassing framework for the optimization of patient flow. Using a comprehensive dataset of 2.3 million de-identified patient records, we analyzed demographics, diagnoses, treatments, services, costs, and charges with machine learning models (Decision Tree, Logistic Regression, Random Forest, Adaboost, LightGBM) and Python tools (Spark, AWS clusters, dimensionality reduction). Our model predicts patient length of stay (LoS) upon admission using supervised learning algorithms. This hybrid approach enables the identification of key factors influencing LoS, offering a robust framework for hospitals to streamline patient flow and resource utilization. The research focuses on patient flow, corroborating the efficacy of the approach, illustrating decreased patient length of stay within a real healthcare environment. The findings underscore the potential of hybrid data-driven models in transforming hospital management practices. This innovative methodology provides generally flexible decision-making, training, and patient flow enhancement; such a system could have huge implications for healthcare administration and overall satisfaction with healthcare.