Accurately scoring and stratifying patients based on their health risks is essential in preventative care, triage, and chronic disease management. Traditional models often oversimplify risks or fail to capture hidden comorbidities and data interactions.

The Challenge:

• Clinical data is often incomplete, high-dimensional, and imbalanced

• Traditional scoring models use linear rules or rigid thresholds

• Hidden patterns across labs, imaging, behavior, or genetics are missed

• Risk flags come too late, limiting proactive intervention

Quantum-Inspired Solution

Multidimensional Risk Mapping

HessQ transforms patient data into a QUBO-optimized graph to find nonlinear relationships among medical variables.

Continuously re-evaluates patient risk as new data (labs, vitals, notes) arrives — instead of relying on static scores.

Flags emerging risks across patient cohorts — before traditional systems would raise concern.

Dynamic Patient Stratification

High-Risk Identification at Scale

The Results:

• +30% improvement in early identification of high-risk patients

• Faster interventions (based on predictive stratification)

• Reduced care burden through optimized care plans

• Increased trust in data-driven triage decisions