Business Context: Why Drug Interaction Checking Needs AI
A typical patient on multiple long-term medications has a medication list that requires specialist knowledge to assess safely. GPs managing complex patients, pharmacists reviewing new prescriptions, and nurses administering medications in ward settings all rely on reference tools to check for interactions. Existing tools, primarily electronic BNF and specialist pharmacology databases, require the clinician to know which drugs to check against which and to interpret clinical severity ratings without much context. An AI-powered checker changes this in two ways. First, it can accept a full medication list and check all pairs simultaneously rather than requiring the clinician to check drugs one at a time. Second, it can provide a natural language summary of the interaction mechanism, clinical significance, and recommended management, rather than a severity rating and a brief note. This is more useful at the point of care where the clinician needs to make a decision in minutes, not hours. The regulatory context is critical. MHRA classifies clinical decision support tools as medical devices if they are intended to support diagnosis or treatment decisions. SpeedMVPs builds the MVP with clear positioning as an informational reference tool rather than a decision-making system, and advises on the MHRA registration pathway if the product's intended use requires device classification.
Architecture: SNOMED Lookup, Literature RAG, and Response Generation
The system is built around three integrated components. The drug identification layer uses NHS SNOMED API to resolve drug names (including brand names, generic names, and common abbreviations) to SNOMED CT codes. This normalisation step ensures that the same drug entered in different formats is recognised as the same compound. The knowledge base layer holds a curated corpus of pharmacological interaction literature: BNF interaction data, NICE clinical guidelines, and peer-reviewed interaction studies. This corpus is chunked, embedded, and stored in a Supabase pgvector instance. The response generation layer accepts a medication list, identifies all drug pairs, retrieves relevant interaction evidence from the knowledge base, and presents it to Claude with a structured prompt requesting a clinical summary per pair. Claude produces a ranked list of interactions by clinical significance, with mechanism, management advice, and source citations for each. The frontend (Next.js) displays results in a clinical summary format, with expandable evidence sections for each interaction. All queries are logged for audit, and the system does not retain patient-identifiable information longer than the session.
AI Components: RAG over Clinical Literature with Claude
Anthropic Claude is chosen for this application for two reasons. Its instruction following is reliable enough to produce consistently structured clinical summaries without hallucinating drug names or mechanisms. Its ability to reason across multiple retrieved evidence chunks simultaneously means it can synthesise interaction evidence from multiple sources into a coherent clinical picture. The RAG pipeline is the core accuracy safeguard. Claude's responses are grounded in retrieved literature chunks, not its general training knowledge, and the system prompt explicitly instructs Claude to cite its sources and flag when evidence is limited or contradictory. This evidence-grounding is critical for clinical trust. A clinician will not trust an interaction checker that cannot tell them where the interaction data comes from. The system includes explicit uncertainty handling: when retrieved evidence is sparse or conflicting, Claude is instructed to say so rather than produce a confident summary from insufficient data. NHS SNOMED API integration ensures drug identification is aligned with the UK clinical coding standard, which is important for any system intended to integrate with NHS digital infrastructure under NHS Digital data standards.
Challenges: Clinical Safety, MHRA Classification, and Scope Limitation
The primary challenge is clinical safety boundary setting. An AI drug interaction checker that gives confidently wrong information about a severe interaction is more dangerous than one that declines to answer. SpeedMVPs builds the system with a conservative confidence threshold: when interaction evidence is limited or ambiguous, the system surfaces the uncertainty to the user and recommends specialist pharmacist or clinical pharmacologist consultation. The MHRA regulatory boundary requires careful product design. A tool described as helping clinicians make prescribing decisions is likely to be classified as a medical device under the UK Medical Devices Regulations 2002. A tool described as an informational reference that presents published literature summaries, with a clear statement that the clinician makes the clinical decision, occupies a different regulatory position. SpeedMVPs works with clients during scoping to establish the correct positioning and advises when MHRA pre-submission engagement is appropriate. GDPR considerations arise if the system processes patient medication lists. Even an anonymised list of drugs may be indirectly identifiable in some clinical contexts. The MVP default is to process medication lists in-session only without retention, with explicit patient consent required for any storage.
Outcomes: Clinical Value and Workflow Integration
Drug interaction checker tools that integrate with clinical workflows provide measurable value in two ways. First, they reduce the time cost of interaction checking from minutes per patient to seconds, which increases the rate at which checks are actually performed. Many clinicians skip manual interaction checks for time reasons. When the check takes 10 seconds, compliance improves. Second, the quality of interaction information improves. AI-synthesised interaction summaries with mechanism explanations and management advice are more actionable than severity ratings from a reference database. Pilots of AI-enhanced clinical decision support tools in NHS settings have consistently found that quality of the information provided is the primary driver of clinician trust, ahead of speed. This is why the evidence citation and uncertainty flagging in the MVP are not optional features, but core to clinical adoption.
Lessons: Clinical Safety Is Non-Negotiable, Scope Is Everything
The most important decision in building a drug interaction checker is deciding what it will not do. A tool that attempts to cover all possible drug interactions across all clinical contexts will fail at all of them. SpeedMVPs recommends starting with a specific drug class or clinical specialty where the interaction risk is highest and the evidence base is most complete. Cardiovascular polypharmacy, for example, has well-documented interaction patterns and a clear clinical need. This focused scope allows the knowledge base to be curated to high quality and the output to be validated against clinical expert review before launch. Involve a clinical pharmacist or clinical pharmacologist in the knowledge base curation and output review. AI systems for clinical use must be validated by people with clinical domain expertise. This is not optional. It is both good practice and increasingly a regulatory expectation under NHS Digital's Digital Technology Assessment Criteria.