Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study.

ABSTRACT:

Background

Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being.

Objective

This methodological study aimed to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; "documentation of nursing activities"), to predict the future workload requirements using machine learning

SUBMITTER: McMahon M 

PROVIDER: S-EPMC12440230 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature