It is established that clinical AI systems require model-aware serialization middleware, multi-layer evaluation frameworks, and capacity verification before deployment, as well as a patient-safety gap where AI fails for the patients who need it most.
Abstract
We present FHIRBench, a benchmark evaluating six FHIR clinical data serialization strategies across four frontier LLMs (Claude Sonnet 4.5, GPT-5.4, DeepSeek V3.2, Qwen3 32B) on three clinical tasks using 100 stratified synthetic FHIR R4 patient bundles. We employ two evaluation layers: token-level F1 and LLM-as-judge rubric on four clinical dimensions, yielding 7,200 evaluations per layer. Our findings reveal four results. First, serialization significantly impacts quality but the direction diverges between layers: Condensed outperforms Raw JSON on F1 for 3/4 models (Wilcoxon p < 10^-17), while Raw JSON achieves higher judge scores for 3/4 models (p < 10^-7). Narrative achieves 95% of Raw JSON's quality at 83% fewer tokens. Second, model rankings completely reverse between layers -- Claude ranks last on F1 but first on clinical quality (p = 1.0 x 10^-6), demonstrating that single-metric evaluation produces misleading model selection. Third, a significant Model x Serializer interaction (Friedman p = 0.0009) precludes universal format recommendations, with GPT-5.4 favoring Raw JSON while open-weight models favor compressed formats. Fourth, Llama 3.1 70B exhibits 100% inference failure on complex patients despite operating within its nominal context window, revealing a patient-safety gap where AI fails for the patients who need it most. These findings establish that clinical AI systems require model-aware serialization middleware, multi-layer evaluation frameworks, and capacity verification before deployment. Code and data publicly available.
The choice of FHIR-to-text serialisation format significantly impacts clinical LLM quality (Kruskal-Wallis H=163.86, p<10^-33, delta=0.24 on a 5-point scale), yet remains unstudied as a clinical deployment variable. We present FHIRBench-UK, evaluating five large language models across six serialisation formats and three clinical tasks on 100 UK Core FHIR patient bundles (18,000 scored prompts across clean and perturbed cohorts). Our findings converge with independent work on open-weight models (Pator, 2026). The optimal format is context-dependent: raw_json dominates for clinical QA, hybrid_adaptive for clinical reasoning, and structured_markdown for summarisation. In 58% of model-task-complexity scenarios, raw_json is suboptimal. Model capability moderates format sensitivity: Claude Sonnet 4.5 shows 0.10-point sensitivity versus Llama 3.3's 0.39, making adaptive serialisation most valuable for budget-constrained deployments using mid-tier models. All findings replicate under clinically realistic data perturbation. The study additionally confirms a complete ranking inversion between token-level F1 and clinical quality (rho=-0.90), replicating US findings across UK Core profiles. We recommend task-aware serialisation routing as a zero-cost quality intervention for NHS FHIR-based LLM deployments.
Overall, advanced prompting markedly improved model performance, and top-tier LLMs demonstrated robust interpretive capability, while caution is needed for variables with complex clinical semantics such as blood pressure.
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