From c9cc832382f0b625af8e2b9d3a80f8286086a910 Mon Sep 17 00:00:00 2001 From: Andy Charlwood Date: Mon, 16 Feb 2026 00:47:37 +0000 Subject: [PATCH] feat: US-018 - Enrich embedding texts and regenerate embeddings --- Ralph/prd.json | 2 +- src/data/embeddings.json | 26880 ++++++++++++++++++------------------- src/lib/search.ts | 84 +- 3 files changed, 13512 insertions(+), 13454 deletions(-) diff --git a/Ralph/prd.json b/Ralph/prd.json index 9c6df93..bcba321 100644 --- a/Ralph/prd.json +++ b/Ralph/prd.json @@ -351,7 +351,7 @@ "Typecheck passes" ], "priority": 18, - "passes": false, + "passes": true, "notes": "This combines the PRD's US-005 (enrich texts) and US-006 (regenerate embeddings) since they must happen together. Review what buildEmbeddingTexts() currently produces and identify gaps — the benchmark questions highlight what's missing (e.g., clinical specialties, methodology detail, dm+d context, employer classification). After modifying the texts, run npm run generate-embeddings to regenerate. Verify the embedding count matches before and after." }, { diff --git a/src/data/embeddings.json b/src/data/embeddings.json index 5e53af4..6b6155a 100644 --- a/src/data/embeddings.json +++ b/src/data/embeddings.json @@ -2,13227 +2,13227 @@ { "id": "exp-interim-head-2025", "embedding": [ - 0.037629976868629456, - 0.030436577275395393, - -0.028229303658008575, - -0.03129085153341293, - -0.04871901497244835, - -0.0018688003765419126, - -0.047425977885723114, - 0.053550928831100464, - -0.07616686820983887, - 0.0046846820041537285, - -0.07142390310764313, - -0.05230565369129181, - 0.052562031894922256, - -0.04262035712599754, - -0.01490679569542408, - 0.00986370351165533, - 0.01686575450003147, - -0.11652044206857681, - 0.019190948456525803, - -0.04903571680188179, - -0.07523394376039505, - 0.027224451303482056, - -0.04138112813234329, - 0.04909144341945648, - 0.03153352811932564, - 0.02097509056329727, - 0.014370360411703587, - -0.02546102926135063, - 0.00005170175791135989, - 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--git a/src/lib/search.ts b/src/lib/search.ts index a4980a9..cb0f40b 100644 --- a/src/lib/search.ts +++ b/src/lib/search.ts @@ -247,25 +247,68 @@ export function groupBySection(items: PaletteItem[]): Array<{ section: PaletteSe export function buildEmbeddingTexts(): Array<{ id: string; text: string }> { const texts: Array<{ id: string; text: string }> = [] - // Consultations (Experience) + // Consultations (Experience) — enriched with plan outcomes, employer classification, clinical specialties consultations.forEach((c) => { + const isNHS = c.organization.includes('NHS') || c.organization.includes('ICB') + const employer = isNHS + ? `NHS employer: ${c.organization}` + : `Private sector employer: ${c.organization} (not NHS)` const examBullets = c.examination.join('. ') + const planOutcomes = c.plan.join('. ') const codedDescriptions = c.codedEntries.map(e => e.description).join('. ') + + // Role-specific enrichment for clinical specialties and methodology + let roleContext = '' + if (c.id === 'high-cost-drugs-2022') { + roleContext = ' Clinical specialties covered: rheumatology, ophthalmology (wet AMD, DMO, RVO), dermatology, gastroenterology, neurology, and migraine. Wrote most of the system\'s high-cost drug pathways, implementing NICE technology appraisals while balancing legal requirements against financial costs and local clinical preferences.' + } else if (c.id === 'deputy-head-2024') { + roleContext = ' Created dm+d medicines data table integrating all dictionary of medicines and devices products with standardised strengths, morphine equivalents, and Anticholinergic Burden scoring — single source of truth for all medicines analytics. Supported tirzepatide commissioning (NICE TA1026) with financial projections and authored executive paper advocating primary care model, driving system shift to GP-led delivery.' + } else if (c.id === 'interim-head-2025') { + roleContext = ' Built Python switching algorithm using real-world GP prescribing data to identify patients eligible for cost-effective alternatives — compressed months of manual analysis into 3 days. Created novel GP payment system linking incentive rewards to prescribing savings.' + } else if (c.id === 'pharmacy-manager-2017') { + roleContext = ' Community pharmacy role at Tesco PLC, a private sector employer. Served as Local Pharmaceutical Committee (LPC) representative for Norfolk. Full HR responsibilities including recruitment, performance management, grievances.' + } + texts.push({ id: `exp-${c.id}`, - text: `${c.role} at ${c.organization}, ${c.duration}. ${c.history} Key achievements: ${examBullets}. ${codedDescriptions}.`, + text: `${c.role} at ${c.organization}, ${c.duration}. ${employer}. ${c.history} Key achievements: ${examBullets}. Outcomes: ${planOutcomes}. ${codedDescriptions}.${roleContext}`, }) }) - // Skills + // Skills — enriched with role context and practical application + const skillContextMap: Record = { + 'data-analysis': 'Applied across NHS medicines optimisation, identifying £14.6M efficiency programme. Used for prescribing pattern analysis, budget forecasting, and population health analytics serving 1.2M people.', + 'python': 'Used to build switching algorithms (14,000 patients, £2.6M savings), controlled drug monitoring systems, Blueteq form automation, and Sankey chart visualisation tools. Self-taught.', + 'sql': 'Core tool for patient-level analytics, dm+d data integration, and transforming practice-level data to patient-level SQL analysis. Used across all NHS data roles.', + 'power-bi': 'Built PharMetrics interactive dashboard tracking £220M prescribing budget. Created dashboards used by 200+ clinicians and commissioners across Norfolk & Waveney ICB.', + 'javascript-typescript': 'Used for web development including this portfolio website. Built with React, TypeScript, and Vite.', + 'excel': 'Used for financial modelling, data validation, and ad-hoc analysis. Foundational tool across all roles from community pharmacy to NHS ICB.', + 'algorithm-design': 'Designed patient switching algorithm and automated incentive scheme analysis. Applied to real-world GP prescribing data at population scale.', + 'data-pipelines': 'Built automated data processing pipelines for medicines analytics, enabling self-serve models for team data fluency.', + 'medicines-optimisation': 'Core domain expertise spanning community pharmacy through to NHS ICB-level population health. Led efficiency programmes worth £14.6M+.', + 'population-health': 'Leading population health analytics for 1.2M people across Norfolk & Waveney ICS. Developing patient-level datasets from real-world GP prescribing data.', + 'nice-ta': 'Led NICE technology appraisal implementation across high-cost drug pathways. Covered rheumatology, ophthalmology, dermatology, gastroenterology, neurology, and migraine.', + 'health-economics': 'Financial modelling for DOAC switching programmes, tirzepatide commissioning, and pharmaceutical rebate negotiations.', + 'clinical-pathways': 'Wrote most of the Norfolk & Waveney ICB high-cost drug pathways. Created Sankey chart tool for patient pathway visualisation and trust compliance auditing.', + 'controlled-drugs': 'Built Python-based population-scale monitoring system calculating oral morphine equivalents (OME) across all opioid prescriptions. Enables high-risk patient identification and potential diversion detection.', + 'budget-management': 'Managed £220M NHS prescribing budget with sophisticated forecasting models, variance analysis, and monthly financial reporting to the ICB executive team.', + 'stakeholder-engagement': 'Presented to Chief Medical Officer bimonthly. Engaged with GP practices, trusts, commissioners, and pharmaceutical companies across the integrated care system.', + 'pharma-negotiation': 'Renegotiated pharmaceutical rebate terms ahead of patent expiry, securing improved commercial position for the ICB.', + 'team-development': 'Improved team data fluency through training and documentation. Supervised staff through NVQ3 to pharmacy technician registration. Created national induction training at Tesco.', + 'change-management': 'Completed NHS Mary Seacole Programme (2018, 78%). Led transformation to patient-level SQL analytics and self-serve analytical models.', + 'financial-modelling': 'Built interactive DOAC switching dashboard with rebate mechanics, workforce constraints, and patent expiry timelines. Financial projections for tirzepatide commissioning.', + 'executive-comms': 'Authored executive papers for ICB board including tirzepatide commissioning advocacy. Presented evidence-based recommendations to CMO bimonthly.', + } + skills.forEach((skill) => { + const context = skillContextMap[skill.id] ?? '' texts.push({ id: `skill-${skill.id}`, - text: `${skill.name} is a ${skill.category.toLowerCase()} skill used ${skill.frequency.toLowerCase()}, with ${skill.proficiency}% proficiency and ${skill.yearsOfExperience} years of experience since ${skill.startYear}.`, + text: `${skill.name} is a ${skill.category.toLowerCase()} skill used ${skill.frequency.toLowerCase()}, with ${skill.proficiency}% proficiency and ${skill.yearsOfExperience} years of experience since ${skill.startYear}. ${context}`, }) }) - // KPI-backed Achievements + // KPI-backed Achievements — enriched with full story context and outcomes const achievementMap: Array<{ id: string; title: string; subtitle: string; kpiId: string }> = [ { id: 'ach-0', title: '£14.6M Efficiency Savings Identified', subtitle: 'Data-driven prescribing interventions', kpiId: 'savings' }, { id: 'ach-1', title: '£220M Budget Oversight', subtitle: 'Full analytical accountability to ICB board', kpiId: 'budget' }, @@ -275,26 +318,40 @@ export function buildEmbeddingTexts(): Array<{ id: string; text: string }> { achievementMap.forEach((entry) => { const kpi = kpis.find(k => k.id === entry.kpiId) - const storyContext = kpi?.story - ? ` ${kpi.story.context} ${kpi.story.role} Outcomes: ${kpi.story.outcomes.join('. ')}.` - : '' + const explanation = kpi?.explanation ?? '' + const storyParts: string[] = [] + if (kpi?.story) { + storyParts.push(kpi.story.context) + storyParts.push(kpi.story.role) + if (kpi.story.period) storyParts.push(`Period: ${kpi.story.period}.`) + storyParts.push(`Outcomes: ${kpi.story.outcomes.join('. ')}.`) + } texts.push({ id: entry.id, - text: `Achievement: ${entry.title}. ${entry.subtitle}. ${kpi?.explanation ?? ''}${storyContext}`, + text: `Achievement: ${entry.title}. ${entry.subtitle}. ${explanation} ${storyParts.join(' ')}`, }) }) - // Investigations (Active Projects) + // Investigations (Active Projects) — enriched with role context and cross-references + const projectContextMap: Record = { + 'inv-pharmetrics': 'Built during Deputy Head role at NHS Norfolk & Waveney ICB. Provides self-serve analytics for budget holders across the integrated care system. Live at medicines.charlwood.xyz.', + 'inv-switching-algorithm': 'Built during Interim Head role at NHS Norfolk & Waveney ICB. Uses real-world GP prescribing data to auto-identify patients on expensive drugs suitable for cost-effective alternatives. Compressed months of manual analysis into 3 days. Includes novel GP payment system linking incentive rewards to prescribing savings.', + 'inv-blueteq-gen': 'Built during High-Cost Drugs & Interface Pharmacist role at NHS Norfolk & Waveney ICB. Automates prior approval form creation for high-cost drug pathways spanning rheumatology, ophthalmology, dermatology, gastroenterology, neurology, and migraine.', + 'inv-cd-monitoring': 'Built during Deputy Head role at NHS Norfolk & Waveney ICB. Calculates oral morphine equivalents (OME) across all opioid prescriptions at population scale. Enables previously impossible population-level controlled drug analysis. Related to controlled drugs skill.', + 'inv-sankey-tool': 'Built during High-Cost Drugs & Interface Pharmacist role at NHS Norfolk & Waveney ICB. Visualises patient journeys through high-cost drug pathways. Enables trust-level compliance auditing across multiple clinical specialties.', + } + investigations.forEach((inv) => { const techList = inv.techStack.join(', ') const resultList = inv.results.join('. ') + const context = projectContextMap[inv.id] ?? '' texts.push({ id: `proj-${inv.id}`, - text: `Project: ${inv.name}. ${inv.methodology} Tech stack: ${techList}. Results: ${resultList}.`, + text: `Project: ${inv.name} (${inv.status}, ${inv.requestedYear}). ${inv.methodology} Tech stack: ${techList}. Results: ${resultList}. ${context}`, }) }) - // Education + // Education — enriched with research grades and specific subject details const educationItems: Array<{ id: string; docId: string; fallbackTitle: string; fallbackSub: string }> = [ { id: 'edu-0', docId: 'doc-mary-seacole', fallbackTitle: 'NHS Leadership Academy — Mary Seacole Programme', fallbackSub: 'NHS Leadership Academy · 2018' }, { id: 'edu-1', docId: 'doc-mpharm', fallbackTitle: 'MPharm (Hons) — 2:1', fallbackSub: 'University of East Anglia · 2011–2015' }, @@ -306,10 +363,11 @@ export function buildEmbeddingTexts(): Array<{ id: string; text: string }> { const doc = documents.find(d => d.id === entry.docId) if (doc) { const research = doc.researchDetail ? ` Research: ${doc.researchDetail}.` : '' + const researchGrade = doc.researchGrade ? ` Research grade: ${doc.researchGrade}.` : '' const classification = doc.classification ? ` Classification: ${doc.classification}.` : '' texts.push({ id: entry.id, - text: `Education: ${doc.title}. ${doc.type} from ${doc.institution ?? doc.source}, ${doc.duration ?? doc.date}.${classification}${research} ${doc.notes ?? ''}`, + text: `Education: ${doc.title}. ${doc.type} from ${doc.institution ?? doc.source}, ${doc.duration ?? doc.date}.${classification}${research}${researchGrade} ${doc.notes ?? ''}`, }) } else { texts.push({