Automated Post-Analytical Workflow for Newborn Screening Using a Hybrid Rule-Based and Retrieval-Augmented Large Language Model System.

Abstract

Newborn screening using tandem mass spectrometry requires complex post-analytical workflows, including QC review, patient report generation, and cutoff reassessment, which are repetitive, time-consuming, and error-prone. We developed a Python-based clinical decision support system with a graphical user interface, integrating four functionalities: QC and patient result evaluation with deterministic flagging, report drafting using hybrid rule-based and retrieval-augmented generation-enabled large language models, cutoff analysis, and a source-attributed reference chatbot. All artificial intelligence (AI)-generated outputs required mandatory specialist review before release. Implementation reduced the mean per-batch post-analytical processing time from 50.5±14.8 min to 24.3±8.8 min and the cutoff evaluation time from 9.2±1.9 hr to 63.6±13.6 min. Across 20 synthetic cases representing 10 disorders, five specialists identified no major errors affecting clinical interpretation. Repeated generation from identical inputs (50 drafts) produced stable diagnostic conclusions, and all source-attributed statements cited correct sources, with 56 of 60 fully covered by the cited excerpt. Deterministic QC flagging, patient result flagging, and cutoff calculations were fully concordant with manual review. This human-in-the-loop system demonstrates the feasibility and efficiency of controlled AI integration into clinical laboratory workflows, whereas diagnostic performance requires further validation using confirmed-positive specimens and human-comparator benchmarks.