AI-Driven Medical Standards Gap Analysis

I need an AI-enabled workflow that examines healthcare regulatory documents—specifically those governing medical standards—then pinpoints and explains every compliance gap in detail. The system should parse large volumes of text, compare current documentation against the latest regulations, and produce an evidence-backed narrative that lets me see exactly where our policies, procedures, or clinical protocols fall short. I’m looking for a partner who can: • Ingest PDFs, Word files, or web-based regulations using natural-language processing, preferably in Python with libraries such as spaCy, LangChain, or similar. • Map each requirement, clause, or standard to our internal documents, flagging any divergence or omission. • Generate a structured report (Word or searchable PDF) that lists gaps, cites the original regulation verbatim, and recommends concrete remediation steps. • Provide an exportable data file (CSV or JSON) so we can track resolution over time. Acceptance criteria • 100 % coverage of all sections in the supplied regulations. • Clear traceability: every identified gap links back to a paragraph number or section heading in both source and internal documents. • Final report passes a manual spot-check for accuracy on at least 10 randomly selected requirements. If you have prior experience automating regulatory analysis for healthcare, I would love to review sample outputs or a quick demo of your approach.

Project ID: 40334597