refactor: remove dead code — orphaned files, unused types and functions
Delete 3 orphaned files (SubNav, TopBar, problems.ts), remove 4 unused type definitions from pmr.ts (ViewId, NavItem, ReferralFormData, Problem), trim types/index.ts to only Phase, and remove unused utility functions (calculateSkillOffset, formatBootLine, getProfileContent, DotColorName).
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import type { Problem } from '@/types/pmr'
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export const problems: Problem[] = [
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{
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id: 'prob-budget',
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code: 'MGT001',
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description: '£220M prescribing budget oversight and management',
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since: 'Jul 2024',
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status: 'Active',
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narrative: 'Responsible for managing the £220M prescribing budget for NHS Norfolk & Waveney ICB. Developed sophisticated forecasting models identifying cost pressures and enabling proactive financial planning. This is an ongoing responsibility requiring continuous monitoring and strategic intervention.',
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linkedConsultations: ['deputy-head-2024'],
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},
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{
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id: 'prob-sql-transform',
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code: 'TRN001',
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description: 'Patient-level SQL analytics transformation',
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since: '2025',
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status: 'In Progress',
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narrative: 'Leading transformation from practice-level data to patient-level SQL analytics, enabling targeted interventions and a self-serve model for the wider team. This foundational change will unlock previously impossible analysis at population scale.',
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linkedConsultations: ['interim-head-2025', 'deputy-head-2024'],
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},
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{
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id: 'prob-data-literacy',
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code: 'LEA001',
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description: 'Team data literacy programme',
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since: 'Jul 2024',
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status: 'In Progress',
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narrative: 'Educating colleagues on data interpretation and analytics best practices, improving data fluency across the team through training, documentation, and self-serve tools. Ongoing initiative to build sustainable analytical capability.',
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linkedConsultations: ['deputy-head-2024'],
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},
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{
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id: 'prob-efficiency',
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code: 'EFF001',
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description: 'Manual prescribing analysis inefficiency',
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resolved: 'Oct 2025',
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status: 'Resolved',
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outcome: 'Python algorithm: 14,000 pts, £2.6M/yr',
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narrative: 'Built Python-based switching algorithm using real-world GP prescribing data to automatically identify patients on expensive drugs suitable for cost-effective alternatives. Compressed months of manual analysis into 3 days. Identified 14,000 patients and £2.6M in annual savings, with £2M on target for delivery this financial year.',
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linkedConsultations: ['interim-head-2025'],
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},
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{
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id: 'prob-efficiency-target',
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code: 'EFF002',
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description: '£14.6M efficiency target identification and delivery',
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resolved: 'Oct 2025',
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status: 'Resolved',
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outcome: 'Over-target performance achieved',
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narrative: 'Identified and prioritised a £14.6M efficiency programme through comprehensive data analysis. Achieved over-target performance by October 2025 through targeted, evidence-based interventions across the integrated care system.',
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linkedConsultations: ['interim-head-2025'],
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},
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{
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id: 'prob-blueteq-backlog',
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code: 'AUT001',
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description: 'Blueteq form creation backlog',
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resolved: '2023',
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status: 'Resolved',
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outcome: '70% reduction, 200hrs saved',
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narrative: 'Developed software automating Blueteq prior approval form creation. Achieved 70% reduction in required forms, 200 hours immediate savings, and ongoing 7–8 hours weekly efficiency gains.',
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linkedConsultations: ['high-cost-drugs-2022'],
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},
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{
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id: 'prob-asthma-screening',
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code: 'INN001',
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description: 'Asthma screening scalability',
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resolved: '2019',
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status: 'Resolved',
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outcome: 'National rollout: ~300 branches, ~£1M',
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narrative: 'Identified and shared an asthma screening process that was adopted nationally across the Tesco pharmacy estate (~300 branches). Reduced pharmacist time from approximately 60 hours to 6 hours per store per month, enabling the network to claim approximately £1M in revenue.',
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linkedConsultations: ['pharmacy-manager-2017'],
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},
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{
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id: 'prob-incentive-calc',
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code: 'AUT002',
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description: 'Incentive scheme manual calculation',
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resolved: '2025',
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status: 'Resolved',
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outcome: 'Automated: 50% Rx reduction in 2 months',
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narrative: 'Automated incentive scheme analysis, improving accuracy and targeting precision whilst enabling a novel GP payment system linking rewards to delivered savings. Achieved 50% reduction in targeted prescribing within the first two months of deployment.',
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linkedConsultations: ['interim-head-2025'],
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},
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{
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id: 'prob-hcd-tracking',
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code: 'DAT001',
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description: 'High-cost drug spend tracking gaps',
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resolved: '2023',
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status: 'Resolved',
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outcome: 'Blueteq-secondary care data integration',
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narrative: 'Integrated Blueteq data with secondary care activity databases, resolving critical data-matching limitations and enabling accurate high-cost drug spend tracking across the system.',
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linkedConsultations: ['high-cost-drugs-2022'],
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},
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{
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id: 'prob-pathway-opacity',
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code: 'VIS001',
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description: 'Patient pathway opacity',
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resolved: '2023',
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status: 'Resolved',
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outcome: 'Sankey chart analysis tool',
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narrative: 'Created Python-based Sankey chart analysis tool visualising patient journeys through high-cost drug pathways, enabling trusts to audit compliance and identify improvement opportunities.',
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linkedConsultations: ['high-cost-drugs-2022'],
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},
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{
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id: 'prob-opioid-monitoring',
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code: 'MON001',
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description: 'Population opioid exposure monitoring',
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resolved: '2024',
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status: 'Resolved',
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outcome: 'CD monitoring system: OME tracking',
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narrative: 'Developed Python-based controlled drug monitoring system calculating oral morphine equivalents across all opioid prescriptions to track patient-level exposure over time, identifying high-risk patients and potential diversion—enabling previously impossible patient safety analysis at population scale.',
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linkedConsultations: ['deputy-head-2024'],
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},
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]
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