Clinical Accuracy
AI-assisted diagnosis validation serves as a second pair of eyes for every consultation. It cross-references symptoms with millions of patterns to suggest potential oversights, reducing diagnostic errors by up to 30%.
Operational Forecasting
Predict patient inflow and resource utilization days in advance. Our algorithms analyze seasonal trends and local health data to help you staff appropriately and manage bed availability.
Financial & Inventory AI
Smart Pharmacy modules (Expiry prediction) and Revenue Cycle Optimization. The system identifies potential claim denials before they happen, ensuring a healthier bottom line.
Pre-Trained Clinical Models
Deploy validated algorithms instantly. Our models are trained on over 50 million de-identified patient records.
Sepsis Early Warning
Detects physiological patterns 6 hours before clinical onset.
30-Day Readmission Risk
Identifies high-risk discharge candidates for intervention.
Appointment No-Show
Optimizes scheduling by predicting likely cancellations.
Coding & Billing Automation
Suggests ICD-10 and CPT codes based on clinical notes.
Deterioration Index
Real-time scoring of patient vitals in ICU/Ward settings.
Supply Chain Optimization
Predicts surgical inventory needs based on OR schedule.
From Raw Data to
Lifesaving Action.
H24s™ doesn't just display data; it digests it. Our proprietary ETL pipeline normalizes inputs from any source (EHR, Labs, IoT) into a unified FHIR-based data lake ready for inference.
Ingestion
Connectors for Epic, Cerner, HL7 v2 feeds, and wearables.
Normalization
Mapping local codes to SNOMED-CT and LOINC standards.
Inference
Real-time processing against active ML models.
Intervention
Alerts sent directly to clinician mobile devices or nursing stations.
Reducing ER Wait Times by 40%
"H24s™ predicted our patient surges with uncanny accuracy, allowing us to adjust staffing proactively. It didn't just save money; it saved burnout."
Technically Speaking
How do you handle algorithmic bias?
We rigorously test all models against diverse datasets across demographics. Our 'Fairness Monitor' flags any drift in model performance across specific patient cohorts.
Is patient data used to train your global models?
No. Your instance is private. We only use federated learning techniques where effectively 'weights' are shared, but never raw patient record data.
Can we bring our own models?
Yes. H24s™ MLOps platform supports deploying your custom TensorFlow or PyTorch models alongside ours within the same inference pipeline.