Data and AI for healthcare
Healthcare data is fragmented by design — clinical, administrative and operational systems each answer to different owners. Useful analytics almost always means reconciling records that were never meant to be joined.
Clients in this sector: MaxHealth
What usually gets in the way
- Clinical records, scheduling and billing held in separate systems with inconsistent patient identity
- Free-text clinical notes carrying information the structured fields do not
- Coding standards that change, and historical data coded under superseded versions
- Privacy rules that restrict who may see identifiable records, and where they may be processed
- Operational decisions needed daily, from data that arrives weekly
What we build
- Patient and encounter data integration with a defensible identity-resolution approach
- Capacity, scheduling and resource-utilisation forecasting
- Structured extraction from clinical free text
- Quality and outcome reporting pipelines that clinicians will actually trust
- De-identification and access tiering so analysts work without seeing identifiable data
How we handle the constraints
We assume identifiable health information cannot leave your environment, and design for de-identified or aggregated analysis wherever the question allows it. Access tiering is part of the architecture, not a policy document.
Ways to work with us
Fixed-scope build
Defined deliverable · weeks to months
The problem is well understood and you want it solved without opening a hiring req.
Embedded senior team
Ongoing · alongside your people
You have a capable team that is short on senior capacity, not on direction.
Advisory and roadmap
Assessment · sequenced plan
There are several plausible directions and the cost of picking the wrong one is high.
Build and run
Delivery · then ongoing operation
The system matters but does not justify hiring a permanent team to maintain it.
Common questions
Do you need access to identifiable patient data?
Usually not. Most questions can be answered on de-identified or aggregated data, and we scope for that first. Where identifiable data is genuinely required, work happens inside your environment under your controls.
Can you work with our existing systems rather than replacing them?
That is the normal case. We integrate with the record systems you already run — replacing a clinical system to enable analytics is almost never the right trade.
Working on something in healthcare?
Describe the problem in a couple of sentences. If we're not the right people for it, we'll tell you who is.