Advisory Work. Regulatory Platform. Together.
Precoh provides expert consulting on clinical AI readiness and health data infrastructure — and the RegOS platform that turns that work into structured, defensible regulatory execution.
Two capabilities. One integrated outcome.
Most organizations need both: the expert advisory work to get AI-ready, and the regulatory infrastructure to evaluate, approve, and scale what they build. Precoh delivers both — and the two are designed to compound.
Clinical AI & Health Data Readiness
Expert consulting across the full AI lifecycle — from data infrastructure and use-case design through validation, governance, and real-world evidence. We work alongside your clinical, data, and compliance teams to build AI that is trustworthy before it ever reaches a patient.
- Platform assessment & AI readiness scoring
- Use-case development & clinical phenotyping
- AI scale simulations across your patient population
- Medical informatics engineering (OMOP, FHIR)
- AI governance & health equity frameworks
- Real-world evidence & regulatory submissions
Regulatory Execution Operating System
RegOS transforms fragmented regulatory requirements into structured, reproducible, auditable workflows. Every AI model passes through a five-domain evaluation pipeline — producing tamper-evident decisions and submission-ready evidence packages.
- Structured intake & risk classification
- Multi-domain validation engine (5–6 domains)
- Weighted composite risk scoring
- SaMD decision framework — Approved / Conditional / Not Approved
- Sealed evidence packages & audit trail
- FDA SaMD & EU MDR aligned
Together: Precoh's advisory work builds clinical and data readiness — and RegOS ensures that every AI model that comes out of that work can be evaluated, approved, and scaled with confidence. Ad hoc validation replaced with structured, reproducible pipelines. Innovation without overloading clinicians or compliance teams.
Full-lifecycle coverage, from data readiness to regulatory submission
Whether you're just beginning to explore AI's potential or ready to scale a validated model across millions of patients, Precoh meets you where you are.
Platform Assessment & Data Readiness
Before any model is built, we audit your data infrastructure, clinical workflows, governance policies, and integration landscape — defining what AI is actually achievable and what must be fixed first. This is the foundation that makes everything else possible.
- EHR data quality and completeness evaluation
- FHIR/HL7 integration gap analysis
- AI readiness scoring across clinical domains
- Prioritized use-case opportunity mapping
- Technical debt and remediation roadmap
RegOS connection: Readiness assessments feed directly into RegOS intake — data governance domain scores reflect your actual infrastructure maturity, not a generic checklist.
Use-Case Development
We translate clinical problems into precision AI solutions — designing, validating, and packaging use cases from sepsis prediction to readmission modeling — with clinician-in-the-loop design principles at every stage.
- Clinical problem framing and stakeholder alignment
- Computable phenotype design and validation
- Feature engineering from structured + unstructured EHR data
- Model selection, training, and external validation
- Clinical workflow integration design
RegOS connection: Every use case we develop goes through RegOS validation before deployment — ensuring clinical evidence, bias checks, and governance documentation are in place before a model reaches patients.
AI Scale Simulations
Our AI Sandbox lets healthcare organizations rigorously stress-test models across the full clinical population — before a single change is made to production systems. Simulation-first validation is the fastest path to clinical trust.
- Retrospective validation across millions of patient records
- Demographic and geographic subgroup performance analysis
- Equity audits across race, ethnicity, sex, and SDoH
- Model drift simulation over time windows
- Clinical impact quantification (LOS, readmissions, mortality)
RegOS connection: Simulation results — subgroup performance, equity delta, drift projections — map directly to RegOS's Clinical Validation and Fairness & Bias domain scores.
Medical Informatics Engineering
We build the data foundations that make AI trustworthy — FHIR-aligned pipelines, OMOP-mapped data warehouses, and computable phenotype libraries validated by clinicians and data scientists.
- OMOP CDM implementation and ETL pipeline development
- Computable phenotype library design and validation
- Clinical NLP for structured extraction from notes
- Longitudinal patient data architecture
- Research-ready data mart construction
RegOS connection: OMOP-standardized, FHIR-aligned pipelines are the input infrastructure that RegOS's Data Governance domain requires to score at the highest levels.
AI Governance & Health Equity
Responsible AI in healthcare requires more than technical accuracy. We implement governance frameworks that satisfy regulators, protect patients, and ensure models perform equitably across all populations — before and after deployment.
- FDA AI/ML Software as a Medical Device (SaMD) compliance
- Model cards and algorithmic impact assessments
- Health equity audits and bias mitigation strategies
- Continuous monitoring and drift alerting systems
- Stakeholder explainability documentation
RegOS connection: Governance frameworks built in advisory directly power RegOS's Post-Market Governance domain — giving compliance teams a live, auditable record rather than a static document.
Real-World Evidence & Research
Turn your observational data into publishable science and regulatory-grade evidence — supporting drug effectiveness studies, label expansions, comparative effectiveness research, and value-based care negotiations.
- Retrospective cohort and case-control study design
- Propensity score matching and causal inference methods
- Patient journey mapping and treatment pattern analysis
- Regulatory submission support (FDA, EMA)
- Payer and health system value documentation
RegOS connection: RWE study outputs are packaged as structured evidence through RegOS — giving biopharma and health systems submission-ready documentation with full data lineage and reproducibility guarantees.