Overview
What is AI care-gap identification?
Care-gap identification uses population-health AI to analyze clinical, claims, and engagement data and surface patients with open gaps — an overdue screening, an uncontrolled condition, a missed follow-up — before those gaps become acute, costly events.
Rather than reacting when patients present in crisis, care teams receive prioritized, actionable lists of who needs outreach and why — improving preventive care, chronic-disease management, and performance on quality measures tied to value-based contracts.
What the system analyzes
The Challenge
Preventable gaps become expensive crises
Without proactive identification, at-risk patients fall through the cracks \u2014 driving avoidable admissions, worse outcomes, and missed quality targets in value-based care.
Reactive Care
Patients are addressed in crisis rather than before deterioration.
Hidden Risk
At-risk patients are hard to spot across fragmented data.
Quality-Score Pressure
Open gaps undermine HEDIS and value-based performance.
Limited Outreach Capacity
Teams can't manually review whole panels for gaps.
Education AI Solutions
How Solnix closes care gaps
Risk Stratification
Rank patients by clinical risk across the population.
Gap Detection
Identify overdue screenings, labs, and follow-ups.
Chronic-Condition Monitoring
Flag uncontrolled diabetes, hypertension, and more.
Outreach Prioritization
Give care teams ranked, actionable outreach lists.
Quality-Measure Tracking
Track HEDIS and value-based measure performance.
Outcome Monitoring
Measure whether interventions closed the gap.
End-to-End Implementation
How Solnix implements care-gap AI
A phased rollout from data integration through validated risk and gap models surfaced in care-team workflows \u2014 measured on gap closure and quality scores.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Key Capabilities
Key capabilities
Population Risk Models
Stratify risk across the panel.
Benefits
Gap Detection
Find overdue and missed care.
Benefits
Chronic-Disease Monitoring
Track condition control over time.
Benefits
Outreach Worklists
Prioritized, actionable lists.
Benefits
Quality Analytics
HEDIS and value-based tracking.
Benefits
Outcome Tracking
Measure intervention impact.
Benefits
Who Benefits
Outcomes for care teams, leadership, and patients
For Care Teams
Know exactly who to reach and why.
For Population-Health Leaders
Better quality and value performance.
For Patients
Proactive, preventive care.
Education Segments
Explore related healthcare AI
Patient Triage & Navigation
Turn gap detection into proactive outreach.
Revenue Cycle Intelligence
Connect quality performance to value-based revenue.
Clinical Documentation AI
Richer documentation improves gap detection.
Clinical Trial Patient Matching
Reuse population screening for research.
Enterprise Automation
Automate outreach and scheduling.
Agent Ecosystem
Build outreach and follow-up agents.
Methodology
How Solnix builds for healthcare
01, Clinical Workflow Assessment
01, Clinical Workflow Assessment
We shadow real clinical and administrative workflows to design AI around how teams actually work.
02, HIPAA-Compliant Architecture
02, HIPAA-Compliant Architecture
All PHI is processed within HIPAA-compliant infrastructure under signed BAAs, with encryption and access logging.
03, EHR & System Integration
03, EHR & System Integration
We integrate with Epic, Cerner, and Athenahealth via FHIR R4 / HL7 so AI lives inside existing workflows.
04, Clinical Validation
04, Clinical Validation
Systems are validated against retrospective data with clinician oversight before production.
05, Continuous Monitoring
05, Continuous Monitoring
Post-deployment monitoring tracks performance and outcomes; drift triggers retraining.
FAQ
Questions from population-health and quality leaders
Get Started
Reach Patients Before The Crisis.
Talk to a Solnix specialist about a care-gap pilot scoped to your population, quality measures, and EHR.
Talk to a Population-Health AI Specialist \u2192