Anomaly Detection Alerts for a Connected Glucose Monitoring Platform
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Problem
Alert fatigue from static thresholds
A regional connected glucose-monitor maker serving around 20,000 patients relied on static threshold alarms that fired either too often or too late. Patients ignored repetitive low-priority alerts, care teams had no way to spot unusual overnight patterns, and support logs showed alert fatigue as the top reason patients disabled notifications.
Solution
Personalised anomaly detection layer
We developed an anomaly detection layer that learns each patient's individual glucose rhythm and flags deviations — unusual overnight drift, accelerating trends, sensor dropouts — earlier than fixed thresholds. Flags are tiered by severity and surfaced in the companion app and clinician portal, running alongside the device's regulatory-cleared alarms rather than replacing them.
Measurable Impact
What changed after launch
Non-actionable alert volume per patient reduced by 44% after personalised baselines went live
83% of flagged overnight anomalies preceded a threshold alarm by 25 minutes or more
Share of patients keeping notifications enabled rose from 61% to 87% within 4 months
Clinician review time per flagged patient cut from 9 minutes to 4 in the care portal
Tech & Tools Used
What powered the build
Ready to Build your Medical Devices Business with Anomaly Detection
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