Public-data-first
Start with what's legally unambiguous, CMS, state survey data, and prove value before touching anything sensitive.
Healthcare AI fails when it overclaims. We build the opposite way: public data first, transparent scores, explicit guardrails, systems a compliance officer can defend to a surveyor.
a compliance build scores skilled-nursing facilities for psychotropic-medication compliance risk using CMS Care Compare data, benchmarking, citation history (F758/F605), fines, and quarterly trends, with a fully transparent score breakdown. Built deliberately PHI-free: no EHR integration, no resident data, no overclaiming what lagged public data can show.
Every healthcare system we build states plainly what it is and is not: a prioritization heuristic is labeled as one, never as a clinical judgment. AI outputs are drafts with audit trails, deterministic fallbacks, and human sign-off. That discipline is why the work survives scrutiny.
In skilled nursing and long-term care, a missed medication signal isn't a bug ticket, it's a patient. The bar is clinical, and so is the discipline behind it.
Start with what's legally unambiguous, CMS, state survey data, and prove value before touching anything sensitive.
If a number drives a decision, its inputs and weights are inspectable. Black boxes don't survive regulated environments.
Model calls logged and audited, drafts clearly labeled, humans in the loop where stakes demand it.
Systems that fit how nurses and administrators actually work a shift, not how a demo imagines they do.
Medication and regulatory obligations aren't an afterthought here, they're the reason the system exists.
Guardrails, audit trails, and human escalation designed in from the first commit, not patched in later.
LTC teams carry real liability on thin staffing. AI here has to cut risk and workload at the same time, which only happens when reliability and compliance are built in, not sprinkled on.
Yes. We build with PHI handling, access control, and audit trails as first-class requirements.
The opposite is the point. We design to remove steps, not add screens.
A scoped pilot against real workflows in weeks, with the evaluation harness in place from day one.
We'll show you what compliant, defensible AI looks like on your own public data.
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