Predictive Patient Risk Analytics Engineering

We engineer robust data pipelines that ingest and harmonize structured EHR data, claims history, and real-time monitoring streams (e.g., vitals, wearables) into a unified patient representation, ensuring models have a complete, longitudinal view.

We develop and rigorously validate machine learning models (e.g., XGBoost, survival analysis, deep learning) to generate individual patient risk scores for sepsis, readmission, and clinical deterioration, with performance metrics (AUC, sensitivity) benchmarked against clinical standards.

All model development utilizes automated, HIPAA-compliant de-identification pipelines. We implement synthetic data generation and privacy-preserving techniques like differential privacy to protect PHI while solving data scarcity.

We deploy risk scores and alerts directly into clinician workflows via secure HL7/FHIR APIs and SMART on FHIR apps, ensuring insights are available at the point of care within existing systems like Epic or Cerner without disruption.

We implement automated monitoring for model drift, fairness, and performance degradation in production. Our systems include full audit trails and are designed to support compliance with FDA SaMD guidelines and the EU AI Act.

We integrate explainability frameworks (SHAP, LIME) to provide clinicians with interpretable reasons behind each risk prediction, fostering trust and enabling informed, evidence-based intervention decisions.