Behavioral Health · Databricks Lakebase
CareIQ: The Operating Model for Your Care Network, Built on Databricks Lakebase
Dashboards tell you what happened. CareIQ runs a live model of your operations and acts on it.
Every autism and ABA organization is sitting on a goldmine of data: scheduling, claims, authorizations, session notes, retention, satisfaction. The problem was never a shortage of data. It’s that the data only ever describes the business: it never touches it. Insight lands in a dashboard, the dashboard lands too late, and even when it’s right, someone still has to go do something in another system entirely. By the time a report tells you authorized hours are going unused or a center is about to miss its target, the hours have already expired.
CareIQ closes that gap. It’s a care operations OS for behavioral health, built on Databricks Lakebase, that keeps a live, transactional model of your care network - patients, providers, authorizations, sessions, claims - as it changes. You don’t just ask it questions. You run your operation on it: monitor the network, understand why a number moved, decide the next best action, and take that action, all in one governed place.
Monitor. Predict. Decide. Act.
The difference is the last word: Act
Analytics tools, Genie included, stop at the answer. They show you the dip and leave the doing to you, in some other system, some other day. CareIQ doesn’t, because it’s built on a transactional layer it can write to.
Ask “Where are authorized hours going unused?” and you get a decision package: the chart, the root cause, a confidence score, and citations back to the live data. But then there’s a button - Flag auth for care-team review, propose a schedule rebalance - and clicking it writes the decision straight back into the operating model as an auditable transaction. The pending-actions panel updates instantly. Seconds later, that same action is analytics-ready in your Lakehouse under the same governance.
That closed loop - insight into action, in one system, with one audit trail - is the entire difference between a BI tool and an operating model. It’s the thing a dashboard structurally cannot do.
Lakebase is the twin
The name is literal. Lakebase, Databricks’ managed Postgres, holds a live digital twin of your operation: the current, transactional state of every patient, provider, authorization, session, and claim, read and written at single-digit-millisecond latency. It’s the operational system of record.
Around it:
- The Lakehouse is the memory and intelligence. History, trends, and ML models, kept continuously in sync with the live twin and governed by the same Unity Catalog. Every session logged in the twin is instantly analytics-ready: no nightly export, no separate pipeline.
- ML predictions land in the operation. No-show risk, authorization-utilization forecasts, and denial predictions are computed in the Lakehouse and synced back into Lakebase so the app reads them at OLTP latency, right next to the row they describe. The prediction lives where the work happens.
- The app is the control room. One place to watch the network, see why it moved, and act on it.
Ask a question. Get a decision. Take the action.
CareIQ is conversational, but it isn’t a chatbot that hands you a paragraph and shrugs. Ask what a leader asks:
Which center is most at risk of missing its target? What are the top three decisions I need to make this week? Which denial reasons are costing us most this month?
And every response is a decision package, not just a number:
- The right chart, auto-selected from your question.
- Live KPI tiles for billed revenue, utilization, and cancellation rate, read from the live twin, current to the moment.
- Root-cause analysis that explains why something changed, not just what.
- The next best action you can execute in one click, with a confidence score and citations to the underlying data.
Conversation is one way in, not the whole product. Free-text questions can be answered by a Databricks-served model grounded in live queries, and a Genie space can serve the analytical long-tail, but these are interfaces on top of the operating model, not the thing itself.
One model. Seven experts.
A clinical director’s questions look nothing like a finance lead’s. So the control room ships with seven purpose-built personas, each tuned to a real team:
- Executive / Owner: portfolio health, P&L, growth, payer mix.
- Finance: revenue cycle, claims, denials, AR, reimbursement.
- Operations: scheduling, billable utilization, cancellations, capacity.
- Intake: waitlist, referrals, insurance verification, conversion.
- Recruiting: hiring BCBAs and RBTs, time-to-fill, turnover, ramp.
- Care Team: treatment-plan execution, goal mastery, session quality.
- Clinical Director: supervision, caseloads, authorization utilization, outcomes, CSAT.
Switch persona and the whole experience reshapes: questions, KPIs, language, and the actions each team can take. Seven experts, one live model underneath.
One platform, not three vendors
The conventional way to build this is Postgres on RDS, Fivetran to move the data, and a separate warehouse to analyze it: three vendors, three governance models, and hours of latency between the system that runs the business and the analytics meant to inform it. For healthcare, that’s also three places PHI can leak.
CareIQ collapses into one governed platform. It runs as a native Databricks App, with Lakebase for live operational state, Lakehouse for history and ML, and Unity Catalog governing both OLTP and OLAP: one audit trail, one access model, PHI never leaving the platform. There’s nothing to copy out and nothing new to lock down. The same governance that protects your analytics protects every question asked and every action taken.
Why this fits behavioral health
Behavioral health runs on thin margins and complex operations. Revenue depends on authorization and clean claims. Capacity depends on the workforce of BCBAs and RBTs that’s hard to hire and easy to lose. Quality depends on consistent treatment-plan execution across sites that each behave a little differently. And the moment to act is always now: authorized hours expire, a weekly report is already too late.
That’s exactly the gap CareIQ is built for. It connects those signals into one live, governed operating model and makes them not just answerable but actionable in plain language, so the person closest to a problem can see it, understand it, and act on it without waiting on an analyst or a batch job. A regional director rebalances a drifting schedule, an intake lead unblocks a stalled referral, a finance lead flags a compounding denial reason, each from the same live source of truth, each written back to the same operating model.
The bottom line
Behavioral health organizations don’t fail for lack of effort. They leak margin and capacity through decisions made a few days too late and through the gap between knowing and doing. CareIQ closes both: a live operating model of your care network, and the ability to act on it in the moment, on one governed platform.
Stop reading dashboards. Start running your operation.
Built on Databricks Lakebase, Lakehouse, and Unity Catalog. Want to see the closed loop on your data? Let’s talk.
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