V3Qtor AG/Healthcare/Value-based care
Community Health AI
At-risk populations identified by risk stratification, interventions deployed and tracked for effectiveness, social determinants and community resources mapped against need.
$12.4Mannual savings per 100k members
- Practice
- Decision & Risk Sciences
- Sector
- Healthcare, value-based care
- Systems involved
- AI Governance
- Where the number comes from
- The number is an engagement target1.
What it changes in the P&L
Total cost of care per member. The intervention goes where the next claim would have been.
How the first weeks go
- Fit the data you haveDistributions, regressions, time series: we start with what the system records and say where the data is too thin to decide.
- Put a confidence interval on the decisionA rate, a risk score, a treatment match: each comes with its uncertainty and with the rule that says when a person decides instead.
- Calibrate on a rhythmBack-tests run weekly, drift is reported before anyone asks, and the model version is logged with every output.
In an engagement
A payer running a population-health programme stratified 100,000 members and targeted interventions where they returned most. The modeled saving was $12.4 million a year per 100,000 members1.
What stays
Community Health AI stays in the ward’s rhythm once it runs. The weekly review reads from it and its evidence files into the audit trail. Hand-over comes when the number has held for a quarter.
Talk to us about Community Health AI.
Tell us where it would run and what it would have to change. We will come back with what we would look at first, what the first quarter produces, and what it would take.
Notes and sources
- 1Illustrative. Anonymised engagement scenarios; figures indicate modeled or targeted impact and are not audited outcomes.