Analyzing the Efficacy of Machine Learning Augmented Case Management Systems in Streamlining Public Health Crisis Interventions

Abstract

The COVID-19 pandemic highlighted critical gaps in public health infrastructure, notably the inefficiencies in case management during crisis interventions. This paper evaluates the role of machine learning (ML)-augmented case management systems in optimizing response workflows in public health crises, focusing on research and developments up to. We systematically analyze how ML techniques have enhanced data triage, resource allocation, and intervention targeting, using empirical studies and models developed. The findings suggest that ML integration significantly improves efficiency, though ethical concerns and model biases remain significant obstacles. The paper concludes by suggesting future research directions to address scalability and fairness in ML-augmented systems.