Driving measurable impact.

Applied Mathematical Optimizations, Machine Learning and Artificial Intelligence technology.

“What has happened in the past?”

Descriptive analytics uses Artificial Intelligence to process and summarize historical data, helping hospitals understand trends, patterns, and operational performance. It powers dashboards and reports that show average length of stay by department, historical operating room block utilization, nurse shift coverage, and critical care alerts. It lays the groundwork by identifying areas of inefficiency where optimization can later be applied.

“What will happen?”

Predictive analytics uses Artificial Intelligence and Machine Learning to forecast future events by analyzing past patterns. In a hospital, it predicts patients’ likelihood to experience extended stays, operating room delays, or risk of clinical deterioration. These insights inform staffing, discharge planning, and escalation protocols. While primarily focused on forecasting, predictive models often serve as critical inputs for optimization engines—feeding them data to model better decisions.

“What should we do?”

Prescriptive analytics combines predictive insights with applied mathematical optimization to recommend the best course of action. In hospital operations, it suggests how to reduce length of stay, optimize operating room block allocation, or generate efficient nurse schedules. Optimization algorithms evaluate countless scenarios and constraints to deliver practical, data-driven solutions that maximize outcomes with the resources available.

“What can we create or automate?”

Generative AI uses large language and foundation models to produce new, human-like content from data. In hospitals, it generates proactive alert narratives for at-risk patients, offers clear explanations of the model’s reasoning, and enables clinicians to interact with predictions using natural, conversational language.

“How can we plan effectively?”

H2O solutions leverage both robust optimization and mixed-integer optimization. Robust optimization strengthens patient-care strategies against uncertainty, while mixed-integer optimization streamlines the complex mix of yes or no decisions and resource allocations.