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Reference Implementation · Healthcare · 16–22 week reference implementation

AI Readmission Risk Scoring for Multi-Hospital Health Systems

Reference implementation: how NDN Care Predict addresses 30-day readmission risk identification and care coordinator prioritisation

Material reduction
30-day Readmissions
High (>90%)
Risk Detection Sensitivity
2–3× more
Coordinator Throughput
16–22 wks
Integration Timeline
Reference Implementation — This scenario is modeled against realistic industry constraints to demonstrate how the product architecture addresses this class of problem. It is not a historical client engagement.

Reference Scenario

Scenario: Reference Scenario — Regional Multi-Hospital Health System
Industry: Healthcare
Scale: Scenario scale: 8–15 hospitals, 1.5M–3M patient encounters/year

The Challenge

CMS excess readmission penalties accumulate when high-risk patients are discharged without timely intervention. Traditional risk scoring tools (e.g. LACE index) capture a fraction of patients who ultimately readmit, leaving care coordinators to work through manual chart reviews with no way to prioritise. The result: many high-risk discharges happen before any intervention is possible.

Our Solution

NDN Care Predict connects to existing EMR systems via Epic FHIR APIs and scores every inpatient every 4 hours across 200+ real-time signals — vital trends, lab trajectories, medication fill patterns, social determinants, and care team notes. High-risk alerts surface directly inside nursing and case management workflows with specific intervention recommendations. The model is calibrated on each health system's own historical data before go-live.

Modeled Outcomes

Material reduction
30-day Readmissions
Modeled from intervention rate improvement when high-risk patients are identified 24–48 hours earlier in the episode
High (>90%)
Risk Detection Sensitivity
Model architecture targets >90% sensitivity — substantially higher than LACE baseline — through multi-signal real-time scoring
2–3× more
Coordinator Throughput
AI-prioritised worklists reduce time spent on manual chart review, enabling coordinators to cover more patients per shift
16–22 wks
Integration Timeline
Phased rollout via FHIR APIs; timeline scales with number of hospitals and EMR configuration complexity

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