Predictive Analytics Platform That Reduced Readmissions by 34%
A regional hospital group needed to identify at-risk patients before discharge. We built a real-time data intelligence platform that analyses clinical data, surfaces risk scores for care teams, and triggers automated interventions — saving £2.1M annually.
The Challenge
Reactive Care in a System That Needed to Be Proactive
Our client — a regional hospital group with 3 sites and 12,000 annual admissions — was facing mounting pressure over readmission rates. The clinical team knew which patient types were high-risk, but had no systematic way to identify and intervene before patients deteriorated at home.
Readmission rates 18% above national average, triggering regulatory scrutiny and financial penalties
Clinical data siloed across 4 separate systems — no unified patient view for care teams
Risk assessments done manually at discharge, missing patients who deteriorated post-discharge
Care coordinators spending 3+ hours per day pulling data from multiple systems
No early warning system — interventions happening reactively after readmission, not proactively
"We knew our readmission rates were a problem, but we didn't have the tools to do anything about it systematically. LyraeAI gave us the ability to see risk before it became a crisis — and that's changed how we deliver care."
Our Approach
The Solution
Unified Data Platform
We built a HIPAA-compliant data integration layer that unified patient records from the EHR, lab systems, pharmacy, and discharge planning tools into a single, real-time patient view.
Predictive Risk Model
We trained a machine learning model on 3 years of historical patient data to predict 30-day readmission risk at discharge — incorporating 47 clinical and social determinants of health.
Real-Time Risk Scoring
The model runs continuously, updating patient risk scores as new clinical data arrives — alerting care coordinators when a patient's risk profile changes significantly post-discharge.
Care Team Dashboard
A purpose-built dashboard surfaces high-risk patients, recommended interventions, and care gaps — designed with clinical staff to fit naturally into existing workflows.
Automated Intervention Triggers
High-risk patients automatically receive follow-up calls, medication reminders, and telehealth check-ins — coordinated by the platform without manual scheduling.
Outcomes Tracking & Model Refinement
A continuous feedback loop tracks intervention outcomes and feeds results back into the model — improving prediction accuracy over time as the system learns from each case.
Outcomes
The Results
Readmission rate reduced by 34% — from 18% above to 10% below the national average
12,000+ patients now monitored continuously with real-time risk scoring
91% alert accuracy — care teams trust the system and act on its recommendations
Care coordinators save 2.5 hours per day — redirected to direct patient care
Estimated £2.1M annual saving in avoided readmission penalties and costs
Model now being expanded to predict sepsis risk and medication non-adherence
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Services Used