AI-powered Accreditation Readiness: An Adoptable Model for Automated Quality Plan Gap Analysis

Proposed Session Description: Healthcare facilities accredited by AABB, FACT, and CAP must maintain compliance with overlapping standards — from AABB Standards for Blood Banks and Transfusion Services and Cellular Therapy Standards to FACT-JACIE Standards, CAP checklists, and FDA cGMP/cGTP regulations. Preparing for inspections requires manually cross-referencing quality management plans, SOPs, and records against each standard’s requirements — a process that is labor-intensive, error-prone, and often reveals critical gaps too late.
This session introduces a practical, AI-powered workflow for automating gap analysis of facility quality plans against accreditation standards. The model is standard-agnostic and adoptable: whether your facility follows AABB blood bank standards, cellular therapy standards, or both, the same AI agent can ingest quality documents, map content to specific checklist requirements, identify coverage gaps (full, partial, or missing), classify finding severity, and generate prioritized remediation action plans with timelines.
The session features a live demonstration using a real-world case study: an AI agent analyzed a laboratory compliance plan against CAP GEN/COM checklists and identified 23 gaps, including the critical finding that the document was a business compliance plan rather than a CAP-compliant Quality Management Plan. This case illustrates both AI’s analytical power and the importance of validation.
Faculty will cover the end-to-end workflow — from document intake through gap identification, severity classification, action plan generation, and remediation tracking — along with validation strategies, regulatory considerations (21 CFR Part 11), and governance frameworks for deploying AI in accreditation readiness. An interactive exercise allows attendees to evaluate AI-generated gap reports against expert judgment.