Exploring the Relationship Between Workload and Quality of Patient Care in Long-Term Facilities

Posted: Jul 16, 2022

• Jasmine Reed
• Jason Powell
• Jayden Adams

Abstract

This research investigates the complex relationship between nursing workload and quality of patient care in long-term care facilities through an innovative computational framework that combines traditional healthcare metrics with novel data-driven approaches. Unlike previous studies that primarily rely on linear regression models and standardized quality indicators, our methodology employs machine learning techniques, including gradient boosting and neural networks, to capture non-linear relationships and interaction effects between workload variables and care quality outcomes. We collected comprehensive data from 45 long-term care facilities over 18 months, including electronic health records, staffing patterns, patient outcomes, and real-time workload assessments through wearable sensors. Our analysis reveals several counterintuitive findings: moderate increases in certain types of workload metrics correlate with improved patient outcomes up to a threshold point, beyond which quality rapidly deteriorates. Furthermore, we identified specific workload combinations that optimize care quality while maintaining staff well-being. The research introduces a predictive model that can forecast care quality degradation with 87