Predictive estimations of health systems resilience using machine learning.

Area of Science:

• Public Health
• Health Systems Research
• Machine Learning Applications

Background:

• Operationalizing resilience is crucial for public health systems' adaptive capacity during crises.
• Assessing health system resilience requires robust frameworks and predictive capabilities.
• Existing methods may not fully capture the dynamic nature of health system responses to stressors.

Purpose of the Study:

• To develop and apply a Machine Learning (ML)-based approach for assessing public health system resilience.
• To predict the responses of health systems to various stressors using historical data.
• To identify key indicators that influence health system resilience.

Main Methods:

• Utilized historical data from Brazilian capitals, aligned with WHO's six dimensions of resilient health systems.
• Developed a comprehensive dataset through rigorous data collection and preprocessing.
• Applied various ML algorithms, including regression models and decision trees, for predictive analysis.

Main Results:

• Identified significant correlations between outpatient care, healthcare workforce availability, and system resilience.
• Demonstrated that expanding outpatient care capacity enhances overall health system resilience.
• ML models successfully predicted health system responses to stressors, highlighting key influencing factors.

Conclusions:

• Machine Learning offers a powerful tool for predictive modeling in public health resilience assessment.
• Findings inform strategic decision-making, intervention targeting, and resource allocation for strengthening health systems.
• This research provides a valuable framework for public health managers to evaluate and enhance system resilience against emerging challenges.