AI Clinical Deterioration Prediction System

Core Logic

Powered by GPT-4, the orchestrator coordinates all monitoring agents, aggregates their risk contributions into a unified deterioration probability, prioritizes patients by acuity, manages alert thresholds to prevent fatigue, and orchestrates rapid response activation. It synthesizes multi-agent findings into actionable recommendations with confidence levels and predicted time-to-event windows.

Core Logic

A specialized time-series ML model performs continuous trend analysis on vital sign streams, detecting patterns invisible to threshold-based alarms. It identifies trajectory changes (rising heart rate combined with dropping blood pressure), calculates rate of change, predicts future values, and contributes risk scores based on hemodynamic deterioration patterns over 6-24 hour windows.

Core Logic

Claude 3.5 Sonnet analyzes laboratory results with clinical context, tracking trends in inflammatory markers (WBC, lactate, procalcitonin), organ function indicators (creatinine, INR), and critical values. It correlates lab patterns with clinical trajectories, identifies concerning trends before values breach critical thresholds, and provides interpretive findings with organ dysfunction assessment.

Core Logic

GPT-4 Turbo performs natural language processing on nursing notes and physician documentation, extracting clinical sentiment, identifying concerning phrases, and detecting subjective observations that indicate early deterioration. It captures the 'nursing intuition' embedded in documentation and quantifies these soft signals into risk contributions.

Core Logic

Claude 3 Opus tracks medication administration and correlates it with clinical response. It identifies concerning patterns like failure to respond to fluid boluses, escalating vasopressor requirements, or repeated PRN medication needs. Poor medication response contributes to deterioration risk and triggers recommendations for escalation of care.

Core Logic

A specialized ML sepsis model continuously monitors SIRS criteria (temperature, heart rate, respiratory rate, WBC), calculates qSOFA scores, and predicts sepsis trajectory. It identifies patients progressing toward sepsis hours before they meet full criteria, triggering early sepsis bundle recommendations (cultures, antibiotics, fluids) to prevent progression to septic shock.

Core Logic

A specialized cardiac ML model analyzes heart rate variability, troponin trending, blood pressure patterns, and ECG changes to identify patients at risk for cardiac events. It detects patterns suggesting demand ischemia (tachycardia with hypotension), monitors for arrhythmia precursors, and recommends cardiology consultation and repeat cardiac markers when risk elevates.

Core Logic

A specialized respiratory ML model tracks oxygen saturation trends, escalating oxygen requirements, respiratory rate patterns, and work of breathing indicators. It identifies patients trending toward respiratory failure, correlates with ABG results when available, and triggers early ICU evaluation and pulmonology consultation before emergent intubation becomes necessary.