Clinical Decision Support Visualization for Trauma Mortality Risk

The patent transforms static risk scoring methods into dynamic, real-time computational models that continuously update mortality risk scores as new patient data becomes available. The system processes incoming EHR data streams and recalculates risk scores dynamically, allowing the scoring mechanism to adapt to changing patient conditions automatically rather than relying on static calculations and clinician intuition.

The patent replaces manual, instinct-based clinical judgment with an automated computational system that uses machine learning algorithms and predictive models. The mechanical process of clinicians manually assessing patient condition and applying scoring criteria is substituted with an automated information processing system that computes risk scores objectively and consistently, reducing reliance on human instinct and experience variability.

The patent extracts and isolates the most critical risk factors and data elements from the comprehensive patient dataset, presenting only the most relevant information to care teams. The system identifies and extracts key predictors of mortality risk, displaying them in a prioritized format that highlights what matters most for decision-making, rather than presenting the entire raw dataset which would cause cognitive overload.

The patent introduces an intermediary computational layer between the comprehensive patient data and the care team. This intermediary system processes, analyzes, and translates raw data into meaningful risk scores and actionable insights, acting as a mediator that bridges the gap between data complexity and clinical usability. The care team interacts with the simplified output rather than directly with the complex underlying data.