The Challenge
Systemic pressures limiting care quality and discovery speed
Administrative burden, slow prior auth, and inefficient trial enrollment compound into billions in waste annually.
EHR Documentation Burden
Physicians spend 2 hours on EHR documentation for every 1 hour of direct patient care. Administrative burden is the #1 driver of physician burnout, contributing to a projected shortage of 86,000 physicians by 2036.
Prior Authorization Delays
Prior auth denials and delays result in treatment abandonment for 93% of physicians. Insurers receive millions of manual PA requests annually, a process that averages 2 weeks and costs $11 per transaction.
Clinical Trial Inefficiency
80% of clinical trials fail to enroll on time. Patient matching to eligibility criteria is manual, slow, and misses 60–70% of eligible patients due to fragmented EHR data.
Diagnostic Bottlenecks
Radiologist and pathologist shortages create growing queues. The global shortage of radiologists is projected to reach 500,000 by 2030.
Education AI Solutions
Eight AI systems transforming healthcare & life sciences
Clinical Documentation AI
Ambient AI scribes that generate structured SOAP notes, billing codes, and after-visit summaries, reducing documentation time by 60%+.
Prior Authorization Automation
AI systems that extract clinical evidence from EHRs and auto-generate PA requests with supporting documentation, achieving 89% automation rates.
Clinical Trial Patient Matching
NLP over EHR data to identify eligible trial participants in real-time, improving enrollment rates and accelerating time-to-enrollment by 4×.
Drug Discovery Screening
AI for target identification, molecular property prediction, ADMET screening, and synthesis planning, compressing preclinical timelines from years to months.
Patient Triage & Navigation
Conversational AI for symptom triage, appointment routing, pre-visit intake, and care navigation, reducing unnecessary ED visits.
Revenue Cycle Intelligence
AI-powered claim scrubbing, denial prediction, coding optimization, and AR prioritization, improving net collection rate.
Care Gap Identification
Population health AI that surfaces at-risk patients for preventive interventions, closing care gaps in chronic disease management.
Pharmacovigilance
AI monitoring of adverse event reports, social media, and EHR data to identify drug safety signals faster than traditional surveillance systems.
End-to-End Implementation
End-to-end AI implementation for healthcare & life sciences
Solnix delivers the full lifecycle, from opportunity mapping through production deployment and continuous improvement. The same proven methodology powers every healthcare & life sciences engagement, tailored to your systems, data, and regulatory environment.
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.
Methodology
How Solnix Builds for Healthcare
01, Clinical Workflow Assessment
01, Clinical Workflow Assessment
We shadow clinical and administrative workflows to identify the highest-leverage AI interventions, prioritizing impact on patient outcomes, clinician time, and revenue cycle efficiency.
02, HIPAA-Compliant Architecture
02, HIPAA-Compliant Architecture
All systems are architected within HIPAA-compliant infrastructure with BAAs in place. PHI is processed within accredited environments; de-identification and synthetic data strategies are applied where possible.
03, EHR & Payer Integration
03, EHR & Payer Integration
We integrate with Epic, Cerner, Athenahealth, and major payer portals via FHIR R4 and HL7 interfaces. Our systems surface AI insights within existing clinical workflows.
04, Clinical Validation
04, Clinical Validation
AI systems are validated against retrospective clinical data before deployment. We work with clinical informaticists and CMOs to establish safety thresholds, failure modes, and escalation protocols.
05, Continuous Monitoring
05, Continuous Monitoring
Post-deployment monitoring tracks model performance, clinical outcomes, and edge cases. Drift detection triggers retraining cycles. We provide clinical dashboards for ongoing oversight.
FAQ
Questions from CMOs, CIOs, and research leaders
Get Started
AI That Serves Patients and Researchers
Whether you're a health system, payer, pharma company, or digital health startup. Solnix builds the AI infrastructure that moves healthcare forward.
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