Health data and device platforms
IoT sensor platforms and integration with the systems already holding the record — where the hard part is never the sensor, but making its readings agree with the patient.
What a device platform has to handle
IoT device platforms and fleets
Provisioning, configuration, firmware updates and health monitoring for device estates large enough that individual attention is not possible.
Real-time event ingestion
High-volume ingestion with ordering and deduplication, designed for devices that lose connectivity and reconnect with a backlog.
Integration with clinical systems
Readings delivered into the systems clinicians already use, because a separate dashboard adds a place to look rather than information.
Patient identity resolution
Matching a device reading to the right patient across clinical, administrative and device data — the part that consumes most of the project.
Data retention and residency
Retention periods and storage location decided per data category and enforced, which in health data is a legal requirement rather than a preference.
Dashboards for clinical teams
Presented at the level a clinical team can act on, with alerting thresholds set by them and tunable without a release.
Don’t see your challenge here? Talk to us about your project
Identity resolution is where the work actually goes
Reading a sensor is a solved problem. Establishing that this reading belongs to this patient, in this episode of care, when the clinical system, the administrative system and the device fleet each identify people differently, is not.
We treat identity resolution as the first design task rather than an integration detail, because every downstream use — alerting, reporting, research — inherits whatever accuracy is achieved there.
- Identity first
- Matching designed before ingestion is built.
- Backlog tolerant
- Reconnecting devices deliver history safely.
- Into existing systems
- Readings reach the tools clinicians already use.
- Residency enforced
- Storage location controlled per data category.
- Tunable alerting
- Thresholds changed by clinicians, not by a release.
- Fleet manageable
- Firmware and health monitored across the estate.
A data-driven platform built on measurement rather than self-report
Invaryant's data-driven platform is built on the premise that health data is more useful when it is collected continuously than when it is remembered at an appointment. The platform work is ingestion, identity and presentation — making a stream of measurements into something a clinician can act on.
Common questions
Can you work with devices we have already bought?
Usually yes, provided they expose data in some documented way. Where a device is closed, we are straightforward about the limits rather than promising an integration that would depend on reverse engineering.
Where is the data stored?
Wherever your regulatory position requires — including in-country hosting where health data residency rules demand it. That constraint is established at the start, because it shapes the whole architecture.
How do you avoid alert fatigue?
By making thresholds clinically owned and adjustable without a release, and by measuring alert volume and dismissal rates from the outset. An alerting system that nobody reads is worse than none, and it is usually a tuning problem rather than a design failure.
Let's talk about your healthcare platform
Tell us where the friction sits in the pathway today and what the regulatory position requires. We will come back with an honest read on the integration work, the identity problem underneath it, and what a realistic first phase looks like.
