NANO DDI
NANO Health Deep Data Insights
The data already exists. The question is whether anyone can act on it
Hospitals and payers are not short of data — they are short of answers they can act on before the moment passes. The records exist; they sit in separate systems, in different formats, describing the same patient in ways nothing reconciles.
NANO Deep Data Insights is the layer that makes that data answer questions. It is built on NANO BRAIN, trained on hundreds of millions of processed claims and approvals — so the models start from what has already happened at scale rather than from a blank page. Getting this right takes clinicians, data scientists and experience designers working together, which is how it was built.
Improve operational efficiency &
performance
performance
Sifting through data at this volume is precisely what people cannot do and software can. Managers get to see where capacity is actually going, where productivity is lost and which resources are underused — in time to do something about it.
Aiding clinical decision support
Bringing clinical data together produces a fuller view of the patient than any single system holds. Decisions get made against the whole record rather than the part that happened to be open, which is what improves both individual outcomes and population health.
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Enabling population health
management
Combining clinical decision support with patient self-management lets providers act before a condition escalates. Predictive analytics across a population identifies who is at risk while intervention is still cheap and still works.
Aggregating and analysing health data at population scale shows how socio-economic, behavioural, genetic and clinical factors actually correlate — which is the difference between preventive care that is targeted and preventive care that is broadcast.
Empowering consumers, Enhancing patient
care
Patients are more engaged in their own care than they have ever been. Meeting that means reaching them when they need a service rather than only when they are already unwell — which requires knowing which patients those are.
Machine learning removes the routine work that consumes a clinical team: eliminating repetitive administrative tasks and scheduling intelligently gives time back to physicians and patients.
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Related products
Products in the suite that work alongside this one.
More about this product
How this works with the rest of the suite
- NANO BRAIN
BRAIN is the model layer this is built on, which is why the analytics start from scale rather than from a pilot.
- NANO CDSS
Bringing clinical data together produces the fuller view decision support needs to be worth acting on.
- NANO PLICS
Patient-level cost is one of the richest datasets a hospital owns, and it repays being queried rather than only reported.
- NANO Reports
Analytics finds the question; reporting is how the answer reaches the people who did not run the query.
Frequently asked questions
If we already have the data, what is missing?
- Answers, in time. Hospitals and payers are not short of data — the records exist, but they sit in separate systems, in different formats, describing the same patient in ways nothing reconciles. The shortage is of something that can be acted on before the moment passes.
Why does it matter what the models were trained on?
- Because a model trained from a blank page has to learn what is normal before it can say what is not. Building on NANO BRAIN — trained on hundreds of millions of processed claims and approvals — means the models start from what has already happened at scale.
What does it change operationally?
- Sifting through data at this volume is precisely what people cannot do and software can. Managers get to see where capacity is actually going, where productivity is lost and which resources are underused, in time to do something about it rather than in a retrospective.
What does it change clinically?
- Bringing clinical data together produces a fuller view of the patient than any single system holds, so decisions are made against the whole record rather than the part that happened to be open. That improves both the individual outcome and the population picture built from many of them.
Who has to be involved to build this well?
- Clinicians, data scientists and experience designers together, which is how it was built. Analytics assembled by any one of those three alone tends to be statistically sound and clinically unused, or clinically sensible and impossible to operate.