Remote patient monitoring with predictive alerts
Widal designed and shipped a remote patient monitoring platform with predictive deterioration alerts. The hard part was not the model, it was the noise: device dropouts, mistyped readings, sensor drift. We tuned the alerting against real device behavior and integrated the alerts directly into the clinical inbox the operator already used.
The number that moved
Streaming
Per-device ingestion and normalization.
Useful alerts in a noisy device environment.
- 01
Ingest streams from a heterogeneous fleet of RPM devices, with realistic dropout and noise characteristics.
- 02
Surface meaningful clinical change without burying the clinical team in low-signal alerts.
- 03
Integrate alerts into the existing clinical inbox, not yet another screen the team has to learn.
- 04
Hold to a high uptime target while clinical operations rely on the alert path.
A platform that respects the inbox.
- 01
Built a streaming ingestion pipeline with per-device normalization, dropout detection, and state reconciliation.
- 02
Designed a deterioration alert model tuned against real device noise, with explicit thresholds for different patient cohorts.
- 03
Integrated alerts into the operator's existing clinical inbox with prioritization, deduplication, and snooze rules co-designed with clinical ops.
- 04
Layered observability across the device fleet: per-device health, signal quality, and alert outcome telemetry.
- 05
Architected the alert path for high availability, with redundant ingestion and reconciliation on recovery.
Alerts the clinical team actually trusts.
- 01
Held a high uptime target on the alerting path since launch.
- 02
Reduced low-signal alert volume to the clinical inbox vs prior tooling.
- 03
Brought RPM alerts and existing chart context into the same review workflow the team already used.
- 04
Caught deterioration signals earlier in pilots across multiple condition cohorts.
Streaming
Per-device ingestion and normalization
Inbox-native
Alerts in the clinician's existing workflow
HIPAA
BAA-aligned, audit-logged
A two-week architecture read, then a forward deployed pod for the build. We work the way the operator already measures.
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