Machine Learning and Causal Inference for Performance Optimization in Hospital Management

Effective decision-making in hospitals requires more than intuition; it demands data-driven insights grounded in causal relationships. Traditional performance management approaches fail to capture the complex interplay of variables that influence hospital efficiency, patient outcomes, and resource allocation. To address this, we integrate machine learning, knowledge graphs, and statistical causal inference to develop decision models that enhance hospital performance. Our high-performance machine reading system extracts causal insights from healthcare, business, and social science literature, identifying key variables and structuring them into a knowledge graph-based reasoning system that uncovers hidden relationships between factors such as staffing levels, patient recovery rates, and resource utilization. These relationships are further validated through statistical causal inference techniques, constructing synthetic counterfactuals to simulate decision scenarios and determine the most effective actions for hospitals based on performance objectives. We began by curating and annotating a dataset of 39 academic papers to train the machine learning model, identifying hypotheses and causal structures to enhance the system’s ability to extract meaningful insights. Additionally, we conducted market research on Causal AI adoption, analyzing vendors, patents, and commercial gaps to inform a technology transfer strategy for real-world implementation. By combining causal inference techniques with AI-driven decision modeling, this research provides actionable, scenario-specific recommendations for hospital management while also contributing to the broader application of Causal AI in enterprise decision-making.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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