The evidence
A live pilot measures one building on one good month. Before we ever touch a real ward, we run hundreds of de-identified synthetic patients — generated by our own engine and validated against the openly published 1-million-patient synthetic set — straight through the OS. The output is a gap assessment: what the system would have caught, prioritized, with the action beside it.
Synthetic patients carry no real identities — so we can demonstrate the OS end-to-end without exposing a single person's data.
The same cohort runs anywhere, every time. A finding you can re-run is a finding you can trust — and hand to a reviewer.
Hundreds of trajectories across conditions and settings — breadth a single-site pilot structurally cannot give you.
The patient generator
Our generator engine builds clinically coherent synthetic encounters, calibrated and validated against Synthea's openly published 1-million synthetic-patient set — the same lineage of data peer-reviewed and used across the research community. We then map those encounters into the OS data fabric and let the bundles reason over them exactly as they would over a live hospital.
See the literatureSample output
An excerpt of what the OS returns after a synthetic-cohort run: findings the fabric surfaced, ranked by priority, each with the suggested action and the bundle that owns it.
Finding. Foley catheters held beyond a documented indication in 18% of qualifying synthetic encounters — the leading modifiable CAUTI driver in the cohort.
Finding. One or more 3-hour bundle elements (lactate, cultures, antibiotics) completed outside target in 23% of modeled sepsis presentations.
Finding. Admitted patients boarded > 4 hours in 31% of ED-to-inpatient transitions — concentrated at the discharge-lounge hand-off.
Finding. Admission med-rec unverified at the ordering step in 14% of encounters — an adverse-drug-event exposure.
Finding. 9% of modeled shift configurations fell below the computed unit safety floor — the conditions that precede error and turnover.
Outcomes & Opportunities
Once a year, clinicians from our member facilities sit down and peer-review the evidence base behind the engine — challenging the assumptions, checking them against the floor, and recalibrating the digital twin that the gap assessments run on. The model isn't a black box we defend; it's a shared instrument the field keeps honest.
We name the meeting the way the product names everything: not "incident review." Outcomes and opportunities.
Member-facility clinicians challenge and validate the evidence base — annually, on the record.
Their input tunes the model the synthetic cohorts and gap assessments run against.
The evidence base earns its authority from the people who practice it.
When the guidance changes
When a governing body releases new guidance, we fold it into the model — and the next time you open your gap assessment, it shows you exactly what's now out of compliance, the action to close it, and the deadline you're working against. Keeping current stops being a project you staff and becomes a state the system maintains.
Owl owns thisBeyond our own walls
Most clinical AI is trained and validated on the same handful of real datasets — MIMIC above all, a single academic medical center's critical-care records. It's invaluable, but when everyone benchmarks on one hospital's data, the field inherits one hospital's blind spots.
Our engine offers something complementary: a reproducible, privacy-free synthetic control population that any prospective study can hold constant — to baseline an intervention, stress-test a model, or compare across sites without moving a byte of PHI. Synthetic cohorts already have a peer-reviewed precedent as research and benchmarking instruments; we make one purpose-built for operational and coordination questions.
Hold a known synthetic population fixed while you vary the intervention.
An alternative to everyone validating on the same single-center records.
No IRB tangle to exchange a synthetic cohort — collaboration without exposure.
The literature
We didn't invent the idea of synthetic clinical data — we built a purpose-tuned engine on a peer-reviewed foundation. A selection of references on the generation, validation, and research use of synthetic patient data is below; the full Digital Pilot white paper carries the complete reference list, the reduction- and baseline-rate sources, and the full metric appendices — download the PDF ↓.
References support the generation, validation, and research use of synthetic patient data generally. They do not constitute an endorsement of Nightingale OS. The complete citation list and metric appendices appear in the full Digital Pilot white paper (PDF) ↓. Every projected figure is modeled, not observed.
Run your own cohort
We'll generate a gap assessment on a synthetic cohort, then on yours — and show our work at every step.
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