Continuous Patient Deterioration Monitoring

Clinical context

Audits of rapid response calls, unplanned ICU transfers, and cardiac arrests showed early signals in vital signs hours before escalation — hard to spot from once-every-4-hours observations. Monitoring and charting were fragmented, and clinicians were already overloaded with alarms.

Solution overview

Wearables streamed heart rate, respiratory rate, oxygen saturation, and posture to an on‑prem edge gateway. Data then flowed into an AI microservice that enriched streams with EHR context (age, comorbidities, recent labs) and fed a temporal model trained to predict deterioration events. Outputs were risk trajectories and time-to-high-risk indicators embedded in the EHR — not binary alarm directives.

Pipeline

Clinical impact (reported)

• Detected a large fraction of deterioration events earlier than traditional approaches, often creating an 8–24 hour intervention window for high-acuity outcomes.
• Enabled proactive interventions (earlier labs, earlier reviews, earlier transfers) rather than late rapid response escalation.
• Reduced uncontrolled decompensations while keeping clinicians in control of decisions.

Workflow and adoption

• Charge nurses used ward-level heatmaps during shift huddles to reprioritize rounds.
• Staged deployment: retrospective validation → shadow mode → limited pilot → hospital-wide rollout.
• Operational monitoring focused on false positives/negatives and alert volume per bed-day.

Technical highlights

• High-frequency streaming ingestion with cleaning and secure forwarding at the edge.
• Temporal deep learning for early warning and interpretable risk trajectories.
• EHR-native integration via FHIR write-back so outputs behave like standard clinical observations.