Autonomous AI discovery built for healthcare.
Continuous AI discovery across your claims, member, clinical, and prescriber data. Every finding ships with the underlying queries, the cohort definition, and the cost of the run. The cohort-level questions that never make it past the monthly executive deck, answered before the next reporting cycle.
Built for payers, providers, life sciences, and population health teams at health plans, PBMs, health systems, pharma, MedTech, ACOs, and value-based care organizations.
The cohort-level questions that never make it past the monthly executive deck, running overnight against your data.
Your team owns the network, the formulary, the contracts, the care pathways. The agent runs unattended against your claims, member, clinical, and prescriber data — and ships findings into a queue your team triages. Every claim carries the underlying queries and the data-validated number, not the dashboard-rolled-up one.
See how the agent reads cohort × cost × outcomeAggregate metrics look fine. The cohort underneath is where the cost actually moved.
Three benchmarks every healthcare commercial and operational leader recognizes. Each points to the same gap: your data already holds the signals — cohort drift, cost concentrations, cross-system leakage — but they only surface when somebody asks the right question. Most months, nobody does.
Three finding shapes the agent surfaces, paired with the recommendation it ships.
Three structural shapes the agent finds across payer, provider, life sciences, and population health data: a single-cohort drift the aggregate concealed, a cross-system pattern the dashboard couldn't see, and an aggregate mirage that averaged two opposing shifts into a flat line. Same auditable trail every time — severity, the underlying queries, the cohort definition, the cost of the run. The shapes below are representative of what the engine surfaces; specific numbers come from the customer's own data.
Point the agent at your claims and member data for a week. Triage what it finds Monday morning.
Same engine, different findings for every team.
Healthcare data is rarely owned by one team. The agent runs against the same claims, member, clinical, and prescriber data — and tunes its discoveries to the team reading the output. Four teams, one engine, plus the cross-cutting capability that ties them together.
For health plans, PBMs, and TPAs — the medical-economics review, decomposed.
Built for actuarial, network, pharmacy, member analytics, fraud & abuse, and risk adjustment teams. The agent reads claims, member, pharmacy, and network data together and surfaces the cohorts dragging the aggregate. The same engine that finds utilization spikes in a provider cohort after a network change finds pharmacy class drift in one segment, or coding gaps against the panel benchmark. The deep dive lives in how the agent decides what to investigate.
- Utilization spikes concentrated in one provider cohort after a network or formulary change
- Pharmacy spend drift — therapeutic classes moving in one segment without volume change
- HCC coding gaps — provider groups under-coding vs. the panel benchmark
- FWA signal — claim patterns inconsistent with procedure-norm distributions
- Risk adjustment cohort accuracy against actuarial baseline
Built for healthcare teams who want a cohort analyst running overnight, not three dashboards to reconcile.
Three things every finding from DecisionBox carries. None of them changes how your team works in your claims system, your EHR, your data platform, or whatever data tools and BI you already standardized on.
Sees what nobody had time to query for
The agent picks which cohorts, providers, segments, and measures to investigate from your claims, member, clinical, and prescriber data. Findings ship into a queue your team triages in the morning. The cohort-level question stops sitting in the data waiting for the next QBR.
Every number you'd defend at the executive review
The underlying queries the agent ran, the cohort definition, the data window, the cost of the run, the validation checks. Your team re-runs anything, extends anything, overrides anything. No black boxes, no dashboard-rolled-up numbers the data can't validate, no "trust the AI."
Reads your data, not just the claims system and the EHR
Claims plus member enrollment, EHR-derived, pharmacy, referral, contract, and quality data — read together. The patterns the claims system can't see (because it only sees adjudication) and the patterns the EHR can't see (because it only sees the encounter) surface when your data is read end-to-end. Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Open source, AGPL v3. The agent meets you on the stack you already run.