Value-based care, built on production data.
Population health, risk adjustment, and clinical/claims data exchange, inside a regulated environment, with a multi-agent AI platform doing real operational work, not a pilot sitting next to production.
What value-based care BI actually reports on.
Representative measures this practice builds BI and data pipelines around, not a specific client's reported numbers.
Medical Loss Ratio, the share of premium spent on care and quality vs. overhead.
Risk Adjustment Factor and Hierarchical Condition Category capture accuracy.
HEDIS-style quality measure and care-gap closure tracking.
Cost per member per month, benchmarked across populations.
Shared-savings and population health outcomes across attributed lives.
Satisfaction and clinical outcome scores tied back to care delivery.
Encounter-to-claims match accuracy and billing/coding integrity.
Social Determinants of Health signal integrated into risk and outreach models.
Standards and systems this connects to.
The industry-standard formats and vendor systems most healthcare data actually arrives in.
What's been built, at the pattern level.
- Patient identity resolution, probabilistic and deterministic matching and deduplication across multiple EMR sources, feeding a Master Patient Index.
- Multi-agent AI platform, eleven specialized personas with retrieval-augmented generation, doing real operational work in platform development and data operations.
- Governed LLM predictive modeling, patient-outcome prediction with a full deployment, monitoring, and governance lifecycle inside a regulated environment.
- Enterprise data lake and warehouse, ingesting CMS submissions and multi-vendor file exchange through automated, event-driven pipelines.
- DAMA DMBOK-aligned data governance, policy management, stewardship workflows, lineage, and a business glossary built into the platform, not bolted on.