Dynamic contagion potential framework for optimizing infection control in healthcare.

Area of Science:

• Healthcare epidemiology
• Infectious disease modeling
• Health informatics

Background:

• Hospital-acquired infections (HAIs) pose a significant annual burden on healthcare systems globally.
• Traditional infection control methods struggle with real-time spatial and movement data analysis.
• There is a need for dynamic, data-driven strategies to mitigate infection risks in hospitals.

Purpose of the Study:

• To develop a novel framework for minimizing HAIs using a behavior- and context-driven metric called contagion potential (CP).
• To integrate real-time spatial and movement data into infection risk assessment.
• To optimize patient-to-unit assignments to reduce contagion risk.

Main Methods:

• The framework integrates contagion potential (CP), considering individual susceptibility, transmissibility, and movement patterns.
• It utilizes coarse location data to create a dynamic infection risk landscape, updating parameters with behavioral data.
• A CP-based optimization algorithm is employed for patient assignments, balancing contagion risk with clinical needs.

Main Results:

• Simulations demonstrated that the CP framework significantly reduces infection propagation.
• The approach enhances patient safety by proactively managing infection risks.
• The framework contributes to more efficient allocation of healthcare resources.

Conclusions:

• This study presents a scalable, data-driven framework for proactive infection control in healthcare settings.
• Incorporating behavior-aware contagion metrics into patient flow decisions improves operational outcomes.
• The findings highlight the potential of CP to enhance patient well-being and reduce HAI incidence.