BEKHealth

BEKhealth is best understood as two products sharing one data spine. On the recruitment side, BEKplatform ingests data from 40+ EHR vendors and turns a sponsor's protocol — the actual inclusion/exclusion text — into a query that runs across every site in your network. The vendor's own example walks from eligibility criteria to 45,000 matched patients in seconds. That's the workflow that traditionally takes weeks of manual chart pre-screening, and the value proposition is real if the query engine performs as described. On the evidence side, the same longitudinal patient record becomes research-ready RWD with daily refresh instead of the 3-to-6-month legacy lag, with an audit trail back to source records. The vendor cites therapeutic depth in dermatology and cardiometabolic populations, and a partnership with RTI Health Solutions for evidence that has to survive regulatory and payer scrutiny. If you're buying RWD today, the daily refresh cadence and the provenance chain back to source are the two things to verify hardest — those are what separate a defensible dataset from an unverifiable extract. Strengths: the network scale (21M+ patients, 200+ research sites, 20,000 clinicians) is the moat, not the model. The dual-outcome architecture genuinely avoids duplicate chart ingestion. The ROI calculator is unusually concrete for a sales-led vendor — it takes your study count, enrollment per study, screen failure rate, and startup time as inputs, and produces an ROI multiplier (the vendor's example is 5.9x, or $5.90 per $1 invested). Weaknesses: the AI is described as deep-learning NLP and LLMs but the vendor names no specific models, so there's no way to independently benchmark the extraction layer. The 95%+ accuracy figure and the 10x/2x enrollment claims are vendor-reported, not published study results. Implementation is gated on data access approval, which means the 2-to-8-week clock is not something you fully control. And the entire commercial motion is sales-led — you'll run the ROI calculator, then a demo, then a scoping conversation; there's no self-serve path. Where it fits: sponsors and CROs that need feasibility answered with real patient counts across a network of community sites; site networks looking to convert existing EHR data into study volume and RWD revenue via BEKnetwork; RWD buyers specifically needing dermatology or cardiometabolic longitudinal data with provenance. Where it doesn't: a single small independent site with no EHR integration and no dedicated research staff won't get value out of the platform, and a study targeting a population so rare that community EHR volume can't move enrollment is a poor fit for a community-EHR-derived dataset. Teams that only want pure RWD analytics with no recruitment capability are buying half the product.