CareIQ — Clinical Risk Stratification & Readmission Predictor
The Problem & Bottlenecks
System Bottlenecks & Technical Friction
Unplanned 30-day hospital readmissions create severe clinical and financial burdens. Traditional discharge planning relies primarily on static protocol checklists rather than individualized patient risk stratification. High-risk patients frequently receive standard discharge instructions without timely post-discharge follow-up. Clinical teams need real-time, explainable risk scoring embedded directly into EHR discharge workflows so care coordinators can prioritize interventions.
Our Engineering Approach
Architecture Design & Implementation
CareIQ is a predictive ML platform generating real-time 30-day readmission risk scores at the point of discharge. An XGBoost classifier is trained on 2.1M de-identified records incorporating 47 clinical and social determinant variables. Integrated SHAP (SHapley Additive exPlanations) values provide clinical explainability by showing clinicians the top contributing factors for each score. AWS SageMaker hosts the low-latency inference endpoint, while automated workflows trigger structured check-ins for high-risk cohorts.
Technical Architecture
System Subsystem Breakdown.
XGBoost classifier trained on 2.1M patient records with 47 clinical + SDOH features
SHAP explainability layer providing per-prediction factor attribution for clinical transparency
AWS SageMaker real-time inference endpoint with p99 latency < 180ms
FastAPI score ingestion API integrated into EHR discharge workflows
Twilio Programmable Voice for automated multi-touch post-discharge outreach calls
PostgreSQL patient tracking database with 90-day rolling readmission outcome data
React nursing dashboard with risk stratification queue, call logging, and escalation workflows
Docker-based model retraining pipeline running monthly on new discharge outcome data
Engagement Roadmap
Engineering Delivery Schedule.
Patient data extraction, de-identification, feature engineering across 47 clinical + SDOH variables
XGBoost model training, hyperparameter tuning, SHAP explainability integration, validation on holdout set
SageMaker deployment, EHR discharge API integration at 3 hospital sites
Twilio outreach automation, call script design, nursing dashboard build
Parallel validation with control group, outcome tracking, go-live and 30-day result measurement
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