LLM Pipeline Cuts Clinical Rule Creation by 97%, Saving £2M+

Solution

Built a RAG pipeline using Mistral‑7B to turn clinical guidelines into structured decision rules for CDS tools. The system ingested NICE and SIGN documents, embedded them in a FAISS vector store, retrieved relevant sections, and generated grounded rule logic (rule sources were extracted alongside generated rules for rapid review). A validation layer checked units and contradictions, and clinicians reviewed rule sources and generated rules.

Added comprehensive monitoring and QA across the pipeline, including retrieval‑quality metrics, rule‑consistency checks, and clinician override rates. Alerts flagged rules requiring review, and feedback loops refined retrieval parameters and prompt templates over time. This ensured the system consistently produced high‑quality, evidence‑grounded rules at scale with minimal clinician effort.

Built a modular orchestration framework so each pipeline stage — ingestion, indexing, retrieval, rule generation, validation, and review — ran as interchangeable components. This allowed rapid upgrades (e.g., new LLMs or retrieval methods) without rebuilding the system and enabled continuous ingestion of updated guidelines with automatic re‑processing of affected rules for long‑term scalability.

Delivered targeted AI upskilling across functions:

Prompt engineering training
RAG and LLM architecture workshops
AI product management training
Education on safe validation of LLM outputs

These interventions accelerated adoption and cross‑functional alignment.

Added robust safety and governance features:

Source citation for every generated rule
Full retrieval audit trail
Grounding checks to prevent hallucinated logic
Automatic rule‑by‑rule traceability
Mandatory clinical sign‑off with evidence links
A controlled environment compliant with internal data governance and regulatory expectations

These measures established trust and enabled the safe deployment of LLM‑assisted rule generation.

The pipeline automated more than 95% of the labour previously performed manually; clinicians now validated outputs rather than writing rules end‑to‑end.

Before: 20–30 clinician days × £500/day = £10,000–£15,000 per treatment area
After: <1 clinician day = £500 per treatment area[vc_column_text][vc_column_inner][vc_row_inner][vc_row_inner el_class="HOICST_Impact"][vc_column_inner][vc_column_text]

Impact:

Reduced rule creation time from 30+ days to <1 day, a 97% reduction in time.[vc_column_text][vc_column_text]Saved an estimated £10,000–£15,000 per treatment area. Across 170 clinical areas, this represented total savings of more than £2 million.[vc_column_text][vc_column_text]Enabled rapid scaling the product suite across multiple disease and therapeutic areas days, not months.[ vc_column_text][vc_column_text]Improved ruleconsistency accuracy through evidence‑grounded retrieval structured generation.[ vc_column_text][vc_column_text]Established reusable enterprise pipelinethat accelerated development business areas.[ vc_column_text][vc_column_text]Significantly upskilled clinical, engineering, teams, raising organisation’s AIliteracy.[ vc_column_text][ vc_column_inner][ vc_row_inner][vc_row_inner el_class =max ][vc_column_inner][vc_empty_space height =60px ][vc_column_text]

Context

Clinical decision support (CDS) tools are central to prescribing treatments in regulated medical environments. These tools are populated by rules derived from large clinical guideline texts such as NICE and SIGN documents. Traditionally, clinicians read guideline documents, interpret nuanced recommendations, and transform them into machine-readable rules ingested by CDS systems. This manual digitisation is expensive, slow, and subject to inter-clinician variation, which limited scalability and introduced inconsistency into a safety‑critical workflow. The objective was to reduce reliance on costly clinician time, improve rule consistency and accuracy, and rapidly scale a CDS product suite across multiple disease and therapeutic areas while maintaining auditability, guideline alignment, and regulatory compliance.

Challenges

Manual rule creation required 20–30 clinician days per treatment area, necessitating a large expert clinical staff and costing £500 per clinician day. At £500/day, each rule cost between £10,000 and £15,000 to produce, making broad scaling prohibitively expensive. Subjective interpretation of dense, complex guidelines led to inconsistent rule sets from the same source documents. Any automated approach needed to remain auditable, evidence‑grounded, safe for regulated clinical environments, and acceptable to clinical governance teams. Technical, product, and clinical stakeholders lacked familiarity and confidence with AI‑assisted rule generation, creating adoption and trust barriers that also needed to be addressed.

Implementation

The solution implemented a retrieval‑augmented generation (RAG) pipeline using Mistral-7B to convert guideline text into structured, auditable decision rules for CDS tools. NICE and SIGN guideline documents were ingested, chunked, embedded, and indexed in a FAISS vector store. Retrieval returned relevant guideline sections which were then supplied to the LLM with structured prompts to generate grounded rule logic. Each generated rule was accompanied by extracted source citations so reviewers could rapidly verify provenance.

A validation layer ran automated checks for unit consistency, numerical ranges, and internal contradictions, and flagged any logical conflicts. Clinicians shifted from authoring rules to validating outputs: they reviewed rule text alongside highlighted source passages and either approved, edited, or rejected each rule. The pipeline automated more than 95% of the labour previously performed manually — clinicians now validated outputs rather than writing rules end‑to‑end.

Safety and governance features were embedded across the pipeline: source citation for every generated rule, a full retrieval audit trail, grounding checks to prevent hallucinated logic, automatic rule‑by‑rule traceability, and mandatory clinical sign‑off with direct evidence links. The system operated within a controlled environment compatible with internal data governance and regulatory expectations.

The architecture was built as a modular orchestration framework so ingestion, indexing, retrieval, rule generation, validation, and review were interchangeable components. This modularity allowed rapid upgrades (for example, swapping in new LLMs or retrieval methods) and supported continuous ingestion of updated guidelines with automatic re‑processing of affected rules. Monitoring and QA were added end‑to‑end, incorporating retrieval‑quality metrics, rule‑consistency checks, clinician override rates, and alerts that flagged rules requiring review. Feedback loops refined retrieval parameters and prompt templates over time.

To accelerate adoption, the programme delivered targeted AI upskilling across functions: prompt engineering training, RAG and LLM architecture workshops, AI product management training, and education on safe validation of LLM outputs. These activities increased cross‑functional confidence and operational readiness.

Results

The RAG pipeline reduced rule creation time from 20–30 clinician days to less than one day — a 97% time reduction. Cost per treatment area dropped from approximately £10,000–£15,000 to about £500, saving an estimated £10,000–£15,000 per treatment area. Across 170 clinical areas, the programme delivered total savings of more than £2 million. The solution automated over 95% of previously manual labour, enabling the CDS product suite to scale across multiple disease and therapeutic areas in days rather than months.

Consistency and accuracy of rules improved through evidence‑grounded retrieval and structured generation, and the audit trail and mandatory sign‑off restored clinical and regulatory confidence. The reusable enterprise pipeline accelerated product development across multiple business areas and materially upskilled clinical, engineering, and product teams, raising organisational AI literacy and enabling safe, governed use of LLM‑assisted rule generation at scale.