Privacy-PreservingLLM Middleware in LIS: Edge-Computing for Coagulation InterpretationUnder High-Dimensional Noise

Why It Matters

The middleware proves that high‑accuracy AI can be run locally in compliance‑heavy healthcare settings, unlocking LIS modernization without costly infrastructure or privacy risks.

Key Takeaways

• Edge LLM runs on 32 GB RAM, no GPU
• Achieves 97% guideline concordance under noisy inputs
• Critical violation rate limited to 1.5%, near zero hallucinations
• Throughput 1.45 RPS, latency 1.38 s, meets clinical limits
• Cohen’s Kappa 0.88 shows expert-level agreement

Pulse Analysis

The healthcare sector has long struggled to embed advanced AI within Laboratory Information Systems because patient data must remain on‑premise and many clinics lack dedicated GPU clusters. Deploying a quantized 7‑billion‑parameter LLM on a standard 32 GB RAM terminal sidesteps these barriers, delivering a privacy‑preserving inference layer that never leaves the hospital network. By leveraging the Ollama framework and Retrieval‑Augmented Generation, the middleware enriches raw coagulation test entries with contextual knowledge while respecting strict regulatory mandates. This approach also reduces latency compared with cloud‑based inference, preserving real‑time clinical workflows.

Rigorous testing on a synthetic set of 2,000 activated partial thromboplastin time cases demonstrated that the edge LLM maintains a 97 % guideline concordance even when 30 % semantic and 15 % lexical noise are introduced. In contrast, a conventional rule‑based engine fell to 10 % accuracy, highlighting the model’s resilience to real‑world data imperfections. Hallucination controls built with Pydantic V2 schemas and a Tenacity‑driven self‑reflection loop capped the critical violation rate at 1.5 %, while throughput reached 1.45 requests per second with an average latency of 1.38 seconds. The self‑reflection loop retries up to three times, ensuring deterministic outputs for safety‑critical environments.

The successful deployment signals a shift toward zero‑marginal‑cost AI augmentation in resource‑constrained medical settings. With a Cohen’s Kappa of 0.88 against an independent AI‑as‑Expert panel, the system delivers expert‑level interpretation that can be trusted for clinical decision support. Hospitals can now modernize their LIS without investing in expensive hardware or compromising patient confidentiality, opening pathways for broader AI adoption across diagnostics, triage, and population health monitoring. Future work will explore scaling to multimodal lab data and integrating federated learning to further enhance privacy guarantees. By keeping computation at the edge, institutions avoid data transfer fees and simplify compliance audits.