AI-Driven Transparency Scoring Tool for Value-Based Care Claims

AI-Driven Transparency Scoring Tool for Value-Based Care ClaimsAI-Driven Transparency Scoring Tool for Value-Based Care Claims

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Adriana E. Reyes

• Apr 2

vbcbench.com is a benchmarking tool I designed and built end-to-end that scores the transparency of public outcome claims made by value-based care organizations. Think of it as a Moody's-style rating for healthcare outcome reporting.

On the back end, I built an AI-assisted extraction pipeline that pulls outcome claims from public sources (company websites, CMS data, PubMed, SEC filings), applies semantic deduplication, and scores each claim against a deterministic rubric evaluating source accountability, comparator presence, denominator clarity, and timeframe documentation. The scoring engine is fully rules-based (v1.0, scale 1–5) to ensure reproducibility and auditability.

On the front end, I handled deployment on Vercel including SSL configuration and subdomain routing, and iterated on the UI to surface scored results in a clean, interpretable format for a non-technical audience.

The current dataset covers 10 companies and ~51 validated claim snippets, with infrastructure in place to scale across the broader VBC market. Still a work in progress.

AI Engineer Next.js OpenAI Python

Sanket Sabharwal, PhD

pro

• Mar 30

Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim. We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model. In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?" That question is what prompted our first article titled "The Machine Learning Readiness Checklist". We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets. If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:

https://www.algorithmic.co/blogs/the-machine-learning-checklist We'll be sharing more each week. Follow along if this is relevant to what you're building.

P.S. This article is from late 2025 but more than ever relevant.

Machine Learning Engineering softwareengineering AI Engineer