AI That Clinicians Actually Trust
Most healthcare AI projects die in pilot. Not because the model was bad, but because it didn't fit the clinical workflow. We build AI systems that give doctors useful answers at the right moment — and show their reasoning so clinicians can verify before acting.
What Are Healthcare AI Solutions?
Healthcare AI solutions apply machine learning, natural language processing, and computer vision to clinical, operational, and research problems in medicine. The goal isn't to replace clinicians — it's to give them better information faster. That means clinical decision support at the point of care, predictive models that flag deteriorating patients 6 hours before a code, and NLP that turns 2 hours of chart review into 5 minutes.
- Clinical decision support that integrates directly into EHR workflows
- Predictive models trained and validated on real clinical datasets
- Explainable AI that shows clinicians why a recommendation was made
- FDA regulatory pathway support for AI/ML medical devices (SaMD)
Healthcare AI Systems We Build
From clinical decision support to operational optimization — AI that solves real problems in healthcare.
Clinical Decision Support (CDS)
Real-time alerts and recommendations embedded in the EHR workflow. Drug interaction warnings, diagnostic suggestions, and treatment protocol guidance — triggered at the right moment, not buried in a separate application.
Medical NLP & Document Processing
Extract structured data from clinical notes, pathology reports, and discharge summaries. Our NLP models handle medical abbreviations, negation detection, and temporal reasoning with 94%+ accuracy on clinical text.
Predictive Analytics & Risk Scoring
Predict hospital readmissions, sepsis onset, patient no-shows, and length of stay. Models are trained on your data and validated against established clinical benchmarks. One hospital reduced sepsis mortality by 18% using our early warning system.
Medical Imaging AI
Computer vision models for radiology (chest X-ray, CT), pathology (histopathology slide analysis), and dermatology (skin lesion classification). We handle DICOM integration, model validation, and the FDA 510(k) or De Novo submission process.
Operational AI & Resource Optimization
Predict patient volume, optimize staff scheduling, forecast supply needs, and reduce wait times. Our OR scheduling optimizer increased surgical throughput by 15% at one academic medical center without adding rooms or staff.
AI Model Monitoring & Governance
Production monitoring for model drift, bias detection, and performance degradation. Automated retraining pipelines with clinical validation gates. Full audit trails that satisfy FDA post-market surveillance requirements.
Healthcare AI in Practice
AI delivers the most value when it's solving a specific clinical or operational problem. Here's where we've seen the biggest impact.
Early Sepsis Detection
A predictive model that analyzes vital signs, lab results, and nursing notes in real-time to flag sepsis risk 4-6 hours before clinical recognition. Integrated into the EHR as a passive alert — fires only when risk exceeds 85%, keeping alert fatigue low.
Automated Prior Authorization
NLP system that reads prior authorization requests, extracts clinical justification from the chart, and auto-populates payer forms. Reduced auth processing time from 45 minutes to 8 minutes per request at a 200-provider medical group.
Radiology Triage & Prioritization
AI that scans incoming imaging studies and prioritizes the worklist based on detected abnormalities. Critical findings (pneumothorax, PE, large vessel occlusion) get moved to the top of the radiologist's queue within seconds of image acquisition.
Population Health Risk Stratification
Identify high-risk patients across your population using claims data, clinical records, and social determinants. Assign risk scores and recommended interventions. One ACO reduced ER visits by 22% by proactively engaging their top 5% risk tier.
How We Build Healthcare AI
Healthcare AI has a unique challenge: the model is only half the problem. Clinical integration, validation, and regulatory compliance are the other half.
Clinical Problem Definition
We work with your clinical team to define the specific problem, the decision point where AI adds value, and the success metrics. A model that predicts something interesting but doesn't change a clinical action is a research project, not a product.
Data Assessment & Preparation
Audit your available data sources (EHR, claims, imaging, labs) for quality, completeness, and bias. Build the training dataset with clinical SME oversight. Handle de-identification, data linkage, and IRB requirements if needed.
Model Development & Clinical Validation
Train, tune, and validate models using clinically appropriate metrics (sensitivity/specificity, NNT, calibration). We run retrospective validation on historical data and prospective silent-mode testing before any clinical deployment.
EHR Integration & Workflow Design
Build the integration layer — CDS Hooks for real-time alerts, SMART on FHIR apps for interactive tools, or batch scoring for population health. Design the clinical workflow so the AI output reaches the right person at the right time.
Regulatory Submission (If Required)
For AI systems that qualify as Software as a Medical Device (SaMD), we prepare the regulatory submission package — predicate device analysis, algorithm description, validation study results, and post-market monitoring plan for FDA 510(k) or De Novo.
Production Monitoring & Continuous Improvement
Deploy with full monitoring: model performance dashboards, drift detection alerts, clinician feedback loops, and automated retraining pipelines. Healthcare data distributions change — your model needs to change with them.
Ready to get started? Let's discuss your project.
Healthcare AI Engagement Models
AI projects range from focused proof-of-concepts to full production deployments. Here's how we structure engagements.
AI Proof of Concept
Validate a clinical AI hypothesis with your data. Get a working prototype and performance metrics in 8-12 weeks.
Custom pricing based on your requirements
- Clinical problem scoping with your team
- Data assessment and preparation
- Model development and validation
- Performance report with clinical metrics
- Go/no-go recommendation for production
Production AI Deployment
Take a validated model from prototype to production with full EHR integration, monitoring, and clinical workflow support.
Custom pricing based on your requirements
- Production-grade model optimization
- EHR integration (CDS Hooks, SMART on FHIR)
- Clinical workflow design and testing
- Model monitoring and drift detection
- Clinician training and adoption support
- FDA regulatory support (if applicable)
AI Strategy & Assessment
Not sure where AI fits in your organization? We assess your data, workflows, and opportunities to build a prioritized AI roadmap.
Custom pricing based on your requirements
- Data maturity and readiness assessment
- Clinical workflow opportunity analysis
- AI use case prioritization framework
- Technical architecture recommendations
- 12-month implementation roadmap
Healthcare AI Solutions Questions Answered
Quick answers to the questions we hear most often.
It depends on the intended use. If your AI makes clinical recommendations that a provider might act on without independent verification — like flagging a malignant lesion on an image — it likely qualifies as a Software as a Medical Device (SaMD) and needs FDA clearance. CDS tools that merely present information for provider review may qualify for the CDS exemption under the 21st Century Cures Act. We help you determine the regulatory pathway during the scoping phase.
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Ready to Get Started with Healthcare AI Solutions?
Let's discuss your project and discover how we can help you achieve your business goals with our expert team.