Clinical AI that works in regulated environments.
HIPAA-compliant AI and data platforms for hospitals, health systems, payers, and life sciences. We design and build production ML systems that integrate with EHR workflows, meet OCR and HITRUST expectations, and deliver measurable clinical and operational outcomes.
Healthcare data is among the most complex, sensitive, and high-stakes in any industry. Organizations that succeed with AI in clinical settings share a common foundation: clean, governed data pipelines; privacy-preserving architecture from the start; and ML models validated against real-world clinical workflows rather than clean benchmark datasets.
Yearling Solutions brings engineers who have built data platforms and ML systems for hospitals, payers, and life sciences companies. We work with your clinical informatics, IT, and compliance teams to build AI solutions that pass legal and compliance review, integrate with Epic and Cerner environments, and scale beyond a single pilot.
Standards & Regulatory Context
The compliance landscape that shapes AI and data programs in healthcare.
HIPAA Privacy & Security Rule
De-identification standards (Safe Harbor, Expert Determination), minimum necessary principle, and PHI handling in data pipelines and ML training workflows.
HITRUST CSF
Data platform and ML infrastructure controls mapped to HITRUST r2 and i1 requirements for covered entities and business associates.
FDA AI/ML SaMD Guidance
Software as a Medical Device considerations for clinical decision support tools and AI-assisted diagnostics.
HL7 FHIR & Interoperability
FHIR R4 API architecture, CMS interoperability rules, and data exchange standards for clinical AI applications.
ONC Information Blocking
Interoperability requirements and information blocking prohibitions that govern data access for analytics and AI systems.
21st Century Cures Act
Patient data access obligations and the implications for health data platform design and patient-facing AI features.
What We're Seeing
The data and AI challenges driving conversations with healthcare technology leaders today.
AI projects stalled in pilot indefinitely
Many healthcare organizations have completed proofs of concept for AI use cases that never move to production. The gap is almost always governance, integration complexity, or compliance uncertainty rather than the model itself.
Fragmented data across systems and sites
Epic, Cerner, claims systems, lab systems, and medical devices each hold critical data in different formats. ML models trained on siloed data produce siloed insights that clinicians cannot act on.
PHI leaking into AI development workflows
Teams using general-purpose AI tools and cloud services without reviewing data handling practices create compliance exposure. A documented, controlled AI development environment is a HIPAA requirement, not a best practice.
Clinical staff rejection of AI recommendations
Models that produce recommendations without explainability or that were not validated with input from the clinicians who will use them face adoption barriers that no dashboard can solve.
How We Help
End-to-end AI and data engineering for healthcare organizations, built for compliance and clinical fit.
HIPAA-Compliant Data Platform Design
- PHI-aware data architecture with de-identification and access controls
- FHIR-native data lake design for clinical and claims data
- Audit logging, data lineage, and compliance reporting pipelines
- BAA-scoped cloud environment design (AWS, Azure, GCP)
Clinical AI & NLP
- Clinical NLP for unstructured note processing and coding automation
- Predictive models for readmission, deterioration, and care gaps
- LLM-based clinical documentation assistance with PHI controls
- Model validation frameworks aligned to clinical workflow and safety
Interoperability & Data Integration
- EHR integration with Epic, Cerner, and Oracle Health APIs
- FHIR R4 API development and HL7 v2 pipeline modernization
- Claims and clinical data normalization for analytics and ML
- Real-time and batch ingestion from ADT, lab, and pharmacy feeds
Analytics & Governance
- Population health and quality measure analytics platforms
- Self-service BI for clinical operations and care management teams
- Data catalog and metadata management for regulated data assets
- MLOps pipelines with model monitoring and retraining workflows
Perfect For
Healthcare and life sciences organizations building production AI and data capabilities.
Health systems building a clinical data warehouse to power quality reporting and AI initiatives
Payers building predictive models for care management and utilization review
Hospital IT teams modernizing HL7 v2 interfaces to FHIR for analytics and patient access
Digital health companies needing HIPAA-compliant AI infrastructure for their platform
Life sciences organizations building real-world evidence platforms from claims and EHR data
Revenue cycle teams automating coding and denial management with NLP
Proof in Healthcare
Real engagements with measurable outcomes.
LLM benchmarking framework accelerates enterprise AI adoption
Structured evaluation framework for large language models that cuts model selection time significantly. The same rigorous evaluation approach we apply to clinical AI tools where accuracy and safety are non-negotiable.
Read case studyData EngineeringEnterprise data retrieval system improves analyst productivity
Semantic search and retrieval architecture built on unstructured data. The same pattern powers clinical note retrieval and documentation assistance for healthcare teams.
Read case studyRegulated IndustryRegional bank reduces compliance documentation time by 50% with YearlingIQ
Evidence automation across overlapping regulatory frameworks. The same governance automation approach we apply to HIPAA and HITRUST reporting for healthcare data platforms.
Read case studyPair Data with Compliance
YearlingIQ for Healthcare Compliance
Pair your AI and data platform with automated compliance evidence collection for HIPAA, HITRUST, and FDA frameworks.
Ready to build production AI for your healthcare organization?
Talk with engineers who have built HIPAA-compliant data platforms and clinical ML systems for hospitals, payers, and life sciences.