Predictive Analytics & ML

Reducing Avoidable ED Visits via Risk Tiering & Visit Recommendations

Client: Healthcare services company

Situation

Avoidable Emergency Department visits were a key cost and care-quality issue. Care managers needed a consistent way to identify rising-risk patients and intervene earlier.

Task

Tier patient risk and recommend care intensity including visit frequency using clinical and non-medical context, operationalized for care manager workflows.

Action

Built risk tiering and visit-frequency recommendation models using classification and regression including Random Forest. Combined diagnostic codes with utilization and treatment signals, enriched with census and public data for contextual risk factors. Embedded outputs into call-center care manager workflows to steer patients toward primary care and virtual visits when appropriate.

Results

• More frequent engagement and proactive routing toward primary and virtual care
• Aligned interventions with program goal of reducing avoidable ED visits
• Operationalized risk scores directly into care manager workflows