Use Cases
How Precoh RegOS has been applied across the clinical AI lifecycle — from pre-submission validation to post-market surveillance.
Where RegOS creates the most impact
Each use case represents a distinct regulatory challenge that RegOS was designed to address — with structured intake, reproducible validation, and tamper-evident outputs.
Pre-submission evaluation of a radiology AI model
1,200-bed Academic Medical Center · Radiology AI · Class II SaMD
A radiology AI developer needed to evaluate their thoracic CT triage model against FDA SaMD requirements before filing. Previous internal reviews had identified some gaps but couldn't quantify them against submission thresholds or produce structured evidence documentation.
RegOS ran a complete five-domain evaluation: data governance, clinical validation, fairness and bias, technical standards, and post-market governance. A fairness warning flagged subgroup performance variance in elderly patients — identified and remediated before submission.
Standardizing AI evaluation across a national health AI sandbox
National Health AI Oversight Body · Multi-vendor · 50+ submissions
A regulatory sandbox operator was receiving AI submissions in inconsistent formats from multiple developers — each requiring manual review, custom documentation requests, and ad hoc scoring. Decisions were difficult to compare and impossible to audit systematically.
RegOS replaced the ad hoc process with a structured intake pipeline, consistent multi-domain evaluation, and a sealed decision record for every submission. Evaluators moved from document review to structured governance — with every decision traceable to the same rulebook version.
Biopharma oncology label expansion with FDA-grade RWE
Biopharma · Oncology · OMOP CDM · 8.5M patient records
A biopharma company sought to support an oncology label expansion through real-world evidence from a 5-hospital network. The challenge: transforming heterogeneous EHR data into OMOP-standardized, FDA-grade evidence — with a governance trail that could survive regulatory scrutiny.
The Precoh team built an OMOP CDM implementation across all five institutions, standardized 8.5M patient records, designed a computable oncology phenotype, and executed the comparative effectiveness study — delivering a structured evidence package with full data lineage and reproducibility documentation.
Ongoing governance of a deployed sepsis prediction model
1,200-bed Academic Medical Center · Sepsis AI · 3.2M patient records
A health system had deployed a sepsis prediction model with excellent initial performance — 94.2% sensitivity and 72-hour alert lead time. Eighteen months post-deployment, clinical leadership needed assurance that performance had held across a changing patient population, without the burden of a full re-validation from scratch.
RegOS's post-market governance module ran a structured performance evaluation against 3.2M records, quantified drift across demographic subgroups, and issued an updated evidence package — confirming model validity and projecting the next re-validation trigger point.