Post-discharge monitoring, and the data plumbing behind it.
Nasken AI is building two things: remote patient monitoring for the weeks after a patient goes home, and a toolkit that gets clinical data into FHIR so systems can actually exchange it.
NVIDIA Inception member
Post-discharge check-in
Heart Rate
SpO₂
Resp. Rate
Temp
Recovery tracked at home, reviewed by the care team.
Recognised & Affiliated With
What we're building
Two problems, taken seriously.
We're an early-stage team, so we're building two things properly rather than six things thinly. Both are in active development and neither is shipping yet.
01
Post-discharge remote patient monitoring
The weeks after a patient leaves hospital are where recovery is won or lost, and where clinical visibility is thinnest. We're building monitoring that works from the patient's home, starting with diabetic foot ulcers — where a wound that goes unwatched is the difference between a dressing change and an amputation.
In development
- At-home wound assessment from patient-captured images
- Structured recovery tracking between clinic visits
- Review and escalation workflow for the care team
02
FHIR health-data interoperability toolkit
Most clinical data in Indian hospitals and labs lives in CSVs, spreadsheets, and legacy formats that no other system can read. We're building the toolkit that turns it into standards-conformant FHIR, with the mapping and validation work that step actually requires.
Five components
- Legacy-to-FHIR mapping
- Terminology mapping
- Conformance validation
- Data-quality reporting
- Human review interface