Medical Software: 97% Faster Clinical Rule Creation, £2M+ Saved

Context

Medical software development in the Healthcare and Life Sciences sectors required a step change in how clinical decision support (CDS) business rules were created. The organisation’s objective was to reduce the creation time of business rules that power CDS products while improving consistency and scalability and reducing reliance on costly, manual processes. The project focused on safely applying large language model (LLM) automation to convert clinical guidelines into auditable, evidence-aligned business rules suitable for regulated environments, while preserving clinician oversight and regulatory traceability.

Challenges

Prior to automation, manual creation of business rules typically took 4–6 weeks per clinical area, demanded specialist clinicians to read and interpret hundreds of pages of guidance, and suffered from inconsistency and bottlenecks. High clinician labour costs—experts billed at a minimum of £500 per day—made scaling prohibitively expensive. At the same time, adopting LLM-based automation introduced additional constraints: outputs had to be clinically guideline-aligned, auditable, and demonstrably safe in a regulated environment, requiring strong provenance, reproducibility, and processes for clinician sign-off to meet evidence-based and regulatory requirements.

Implementation

The Fractional Head of AI and cross-functional teams designed and deployed a retrieval-augmented generation (RAG) pipeline built around Mistral 7B to automatically extract, summarise, and transform clinical guidelines into structured business rules. The pipeline integrated document retrieval, grounding (source citation and evidence linking), layered validation checks, and human-in-the-loop review so that clinicians reviewed and signed off on machine-generated outputs rather than authoring rules from scratch. Governance was embedded end-to-end: every generated rule included source citations, rule-by-rule traceability, an audit trail, and discrete validation steps equivalent to unit tests, with clinical sign-off protocols aligned to evidence-based practice and regulatory expectations. A modular orchestration framework enabled rapid updates to the rule generation workflow as new models or capabilities emerged and allowed continuous ingestion of new guidelines while referencing existing rules to avoid duplication and drift. Performance monitoring, quality metrics, and feedback loops were integrated to refine intermediate pipeline artifacts and final rule quality without increasing clinician workload. To support adoption, the project delivered tailored AI upskilling sessions for clinicians, product managers, and engineering teams, providing hands-on training in prompt engineering, RAG principles, validation methods, and safe clinical use of LLM outputs, enabling cross-functional trust and literacy.

Results

The RAG-based automation reduced rule creation time from more than 30 days to under one day—a 97% reduction—by shifting clinicians from a laborious manual authoring role to a rapid review and sign-off role. Time savings translated directly into cost reductions of approximately £9,500–£14,500 per disease or treatment area; across 170 clinical areas this represented total savings of more than £2 million. The solution enabled rapid scaling of the CDS product suite across multiple disease and therapeutic areas, improved the accuracy and consistency of business rules via evidence‑grounded retrieval and standardized generation workflows, and established a reusable enterprise pipeline that accelerated product development across business areas. Technical, clinical, and product teams were significantly upskilled, increasing organisational AI literacy and trust in AI-assisted rule generation, while robust governance, traceability, and validation measures ensured outputs remained safe, auditable, and aligned with clinical guidelines.