Intelligent Narrative Summaries and Risk Scoring of Laboratory Panels with Large Language Models
TL;DR
A prompt-driven pipeline that converts FHIR R4 laboratory panels into structured, paragraph-length clinical narratives paired with a calibrated 0-1 risk score, using GPT-4o-mini as the generation engine is described, demonstrating feasibility for abnormality flagging and narrative generation.
Abstract
Laboratory medicine sits at the intersection of clinical science and data management. A single hospital admission can generate dozens of analyte values, yet most electronic health record (EHR) interfaces present them as rows in a table, leaving interpretation entirely to the clinician. Alert fatigue, driven in part by poorly calibrated notifications remains one of the most documented usability problems in modern EHR design [1]. This paper describes a prompt-driven pipeline that converts FHIR R4 laboratory panels into structured, paragraph-length clinical narratives paired with a calibrated 0-1 risk score, using GPT-4o-mini as the generation engine. The full behavioral specification is encoded in the prompt and output schema. We evaluated the system on 200 laboratory panels, each drawn from a distinct synthetic patient, from Synthea-generated FHIR bundles spanning seven panel categories (metabolic, lipid, blood count, diabetes monitoring, kidney, liver, and urine). We compared three configurations: a rule-only template baseline, the LLM alone (no seed), and the hybrid pipeline in which a deterministic rule-based risk seed is supplied to the LLM. Abnormal-analyte detection was near ceiling and statistically indistinguishable for both LLM configurations (F1 ≈ 0.97), indicating that the model recovers out-of-range analytes directly from the structured table with or without the seed. The seed's measurable contribution is to risk-score calibration: the correlation between the model's 0–1 risk score and the reference rule score rose from r = 0.87 (no seed) to r = 0.96 (with seed). The rule seed thus functions as a calibration mechanism rather than a detection aid. These are proof-of-concept results on synthetic structured data. They demonstrate feasibility for abnormality flagging and narrative generation; they do not constitute a claim of clinical validity, which would require real-world data and clinician review. The paper contributes a reproducible architectural framework, a systematic quantitative benchmark on synthetic panels, and a grounded discussion of the integration challenges and future directions that separate a research prototype from a clinically deployed tool.