AI Regulatory Readiness Assessment | DREAM Method @media print { body { print-color-adjust: exact; } .no-print { display: none; } } .gradient-bg { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); } .question-card { transition: all 0.3s ease; border-left: 4px solid #e5e7eb; } .question-card.answered { border-left-color: #10b981; background-color: #f0fdf4; } .progress-bar { transition: width 0.5s ease; } AI Regulatory Readiness Assessment Evaluate your healthcare organization's readiness for AI implementation in Canada Health Canada Compliance SaMD & GMLP Requirements Provincial Privacy Laws PHIPA, PIPA, HIPA Compliance Professional Standards College Requirements Assessment Progress 0% Complete Complete all sections to receive your personalized compliance report Organization Information Organization Type Select your organization type Hospital/Health Authority Private Clinic Pharmacy Allied Health Practice Long-term Care Mental Health Services Other Healthcare Organization Province/Territory Select your province Ontario British Columbia Alberta Saskatchewan Manitoba Quebec New Brunswick Nova Scotia Prince Edward Island Newfoundland and Labrador Yukon Northwest Territories Nunavut Organization Size Select organization size Small (1-50 employees) Medium (51-250 employees) Large (251-1000 employees) Enterprise (1000+ employees) AI Implementation Stage Select current stage Planning/Researching Pilot Implementation Partial Deployment Full Implementation Advanced/Multiple Systems Health Canada Regulatory Compliance Assess your understanding and compliance with Health Canada's Software as Medical Device (SaMD) and Good Machine Learning Practice (GMLP) requirements. 1. Does your organization understand when AI tools qualify as Software as Medical Device (SaMD)? Yes, we have clear policies and can classify AI tools appropriately Mostly, but some uncertainty exists Basic understanding, but need more guidance Limited understanding of SaMD classification No understanding of SaMD requirements 2. Are you familiar with Health Canada's Good Machine Learning Practice (GMLP) principles? Very familiar and actively applying all 10 principles Familiar with most principles and applying many Some familiarity but inconsistent application Limited familiarity with GMLP principles Not familiar with GMLP principles 3. Does your organization have processes for evaluating AI vendor regulatory compliance? Yes, comprehensive vendor assessment protocols in place Basic vendor assessment process exists Informal vendor evaluation practices Limited vendor evaluation capabilities No vendor assessment process 4. Does your organization understand post-market surveillance requirements for AI medical devices? Yes, we have monitoring protocols for AI performance Some monitoring in place but could be improved Basic understanding but limited monitoring Minimal understanding of post-market requirements No understanding of post-market surveillance 5. Does your organization have multi-disciplinary expertise for AI evaluation as recommended by GMLP? Yes, including clinical, technical, ethical, and legal expertise Most expertise areas covered Some expertise areas but gaps exist Limited multi-disciplinary expertise No structured multi-disciplinary approach Provincial Privacy Legislation Compliance Evaluate your compliance with provincial privacy laws including PHIPA (Ontario), PIPA (Alberta), HIPA (Saskatchewan), and other provincial requirements. 6. Does your organization understand the privacy legislation that governs your jurisdiction? Yes, thoroughly understand applicable privacy laws Good understanding with some areas for improvement Basic understanding but need more guidance Limited understanding of privacy requirements Minimal understanding of privacy legislation 7. Do you have processes for obtaining appropriate consent for AI use with patient information? Yes, comprehensive consent processes for AI applications Good consent processes with minor gaps Basic consent processes but AI-specific needs unclear Limited consent processes for AI use No specific consent processes for AI 8. Does your organization implement the "need-to-know" principle for AI access to patient information? Yes, strict access controls limit AI to minimum necessary information Good access controls with some room for improvement Basic access controls but not AI-specific Limited access control implementation No specific access controls for AI systems 9. Do you have appropriate safeguards to protect personal health information used by AI systems? Yes, comprehensive technical, administrative, and physical safeguards Good safeguards with minor gaps Basic safeguards but AI-specific needs unclear Limited safeguards for AI systems Minimal safeguards in place 10. Does your organization have procedures for managing patient access rights regarding AI use of their information? Yes, clear procedures for patient access and correction rights Good procedures with some areas for improvement Basic procedures but AI-specific processes unclear Limited procedures for patient access rights No specific procedures for AI-related access rights Professional Standards and Ethics Assess your alignment with professional regulatory college requirements and ethical standards for AI use in healthcare practice. 11. Are you familiar with your professional regulatory college's guidance on AI use? Yes, thoroughly familiar and compliant with college guidance Familiar with most guidance and generally compliant Some familiarity but need more understanding Limited familiarity with professional AI guidance Not familiar with professional college AI guidance 12. Does your organization ensure human oversight remains in clinical decision-making when using AI? Yes, clear protocols ensure human oversight for all AI-assisted decisions Generally good human oversight with minor gaps Some human oversight but protocols could be clearer Limited human oversight protocols No specific human oversight requirements 13. Do you have processes to address bias and ensure equitable AI outcomes? Yes, comprehensive bias detection and mitigation processes Good bias awareness with some mitigation efforts Basic understanding but limited bias mitigation Minimal bias detection processes No bias detection or mitigation processes 14. Does your organization provide AI literacy training for healthcare professionals? Yes, comprehensive ongoing AI literacy training programs Good training programs with regular updates Basic training but could be more comprehensive Limited AI literacy training No AI literacy training provided 15. Do you have clear accountability frameworks for AI-assisted care decisions? Yes, clear accountability and liability frameworks for AI use Good accountability frameworks with minor gaps Basic accountability but AI-specific needs unclear Limited accountability frameworks No specific accountability frameworks for AI Organizational Readiness Evaluate your organization's technical, financial, and cultural readiness for AI implementation. 16. Does your organization have adequate IT infrastructure to support AI implementation? Yes, robust IT infrastructure with AI capabilities Good infrastructure with some upgrades needed Basic infrastructure but significant upgrades required Limited IT infrastructure Inadequate IT infrastructure for AI 17. Does your organization have dedicated resources (budget, personnel) for AI initiatives? Yes, dedicated AI budget and personnel Some dedicated resources with plans for expansion Limited dedicated resources Minimal resources allocated to AI No dedicated AI resources 18. Does your organization have strong leadership support for AI implementation? Yes, strong leadership commitment and vision for AI Good leadership support with some hesitation Moderate leadership support Limited leadership support Minimal or no leadership support 19. Is your organization's culture open to technological innovation and change? Yes, highly innovative culture embraces new technology Generally open to innovation with some resistance Moderate openness to change Resistant to change and new technology Highly resistant to technological change 20. Does your organization have experience implementing new healthcare technologies? Yes, extensive successful technology implementation experience Good implementation experience with some challenges Limited implementation experience Minimal technology implementation experience No significant technology implementation experience Calculate Your Readiness Score Your AI Regulatory Readiness Report 0% Overall Readiness Score Detailed Section Scores Health Canada Compliance 0% Privacy Compliance 0% Professional Standards 0% Organizational Readiness 0% Personalized Recommendations Priority Action Plan Additional Resources Federal Resources Health Canada - Software as Medical Device Guidance Health Canada - Good Machine Learning Practice Privacy Commissioner - AI Decision-Making Guidance Pan-Canadian AI for Health Principles Provincial Resources // Assessment logic let assessmentData = { answers: {}, orgInfo: {} }; // Progress tracking function updateProgress() { const totalQuestions = 20; const answeredQuestions = Object.keys(assessmentData.answers).length; const progressPercentage = (answeredQuestions / totalQuestions) * 100; document.getElementById('progress-bar').style.width = progressPercentage + '%'; document.getElementById('progress-text').textContent = Math.round(progressPercentage) + '% Complete'; // Enable calculate button when all questions answered const calculateBtn = document.getElementById('calculate-results'); if (answeredQuestions === totalQuestions) { calculateBtn.disabled = false; calculateBtn.classList.remove('opacity-50', 'cursor-not-allowed'); } } // Handle radio button changes document.addEventListener('change', function(e) { if (e.target.type === 'radio') { const questionNum = e.target.name; const value = parseInt(e.target.value); assessmentData.answers[questionNum] = value; // Mark question as answered const questionCard = e.target.closest('.question-card'); questionCard.classList.add('answered'); updateProgress(); } if (e.target.type === 'select-one' && ['org-type', 'province', 'org-size', 'ai-stage'].includes(e.target.id)) { assessmentData.orgInfo[e.target.id] = e.target.value; } }); // Calculate results document.getElementById('calculate-results').addEventListener('click', function() { calculateResults(); document.getElementById('results-section').classList.remove('hidden'); this.scrollIntoView({ behavior: 'smooth', block: 'center' }); }); function calculateResults() { // Calculate section scores const section1 = calculateSectionScore(['q1', 'q2', 'q3', 'q4', 'q5']); const section2 = calculateSectionScore(['q6', 'q7', 'q8', 'q9', 'q10']); const section3 = calculateSectionScore(['q11', 'q12', 'q13', 'q14', 'q15']); const section4 = calculateSectionScore(['q16', 'q17', 'q18', 'q19', 'q20']); const overallScore = (section1 + section2 + section3 + section4) / 4; // Display scores document.getElementById('overall-score').textContent = Math.round(overallScore) + '%'; document.getElementById('section1-score').textContent = Math.round(section1) + '%'; document.getElementById('section2-score').textContent = Math.round(section2) + '%'; document.getElementById('section3-score').textContent = Math.round(section3) + '%'; document.getElementById('section4-score').textContent = Math.round(section4) + '%'; // Add interpretations document.getElementById('overall-interpretation').textContent = getOverallInterpretation(overallScore); document.getElementById('section1-interpretation').textContent = getSectionInterpretation(section1); document.getElementById('section2-interpretation').textContent = getSectionInterpretation(section2); document.getElementById('section3-interpretation').textContent = getSectionInterpretation(section3); document.getElementById('section4-interpretation').textContent = getSectionInterpretation(section4); // Create chart createReadinessChart(section1, section2, section3, section4); // Generate recommendations generateRecommendations(section1, section2, section3, section4, overallScore); // Generate action plan generateActionPlan(section1, section2, section3, section4); // Update provincial resources updateProvincialResources(); } function calculateSectionScore(questions) { let total = 0; let maxPossible = questions.length * 4; // Max score per question is 4 questions.forEach(q => { total += assessmentData.answers[q] || 0; }); return (total / maxPossible) * 100; } function getOverallInterpretation(score) { if (score >= 80) return "Excellent readiness for AI implementation"; if (score >= 60) return "Good readiness with some areas for improvement"; if (score >= 40) return "Moderate readiness requiring focused preparation"; if (score >= 20) return "Limited readiness requiring significant preparation"; return "Foundational work needed before AI implementation"; } function getSectionInterpretation(score) { if (score >= 80) return "Strong performance"; if (score >= 60) return "Good foundation"; if (score >= 40) return "Needs improvement"; if (score >= 20) return "Requires attention"; return "Critical gaps"; } function createReadinessChart(section1, section2, section3, section4) { const ctx = document.getElementById('readiness-chart').getContext('2d'); new Chart(ctx, { type: 'radar', data: { labels: ['Health Canada Compliance', 'Privacy Compliance', 'Professional Standards', 'Organizational Readiness'], datasets: [{ label: 'Your Readiness Score', data: [section1, section2, section3, section4], backgroundColor: 'rgba(59, 130, 246, 0.2)', borderColor: 'rgba(59, 130, 246, 1)', borderWidth: 2, pointBackgroundColor: 'rgba(59, 130, 246, 1)', pointBorderColor: '#fff', pointHoverBackgroundColor: '#fff', pointHoverBorderColor: 'rgba(59, 130, 246, 1)' }] }, options: { responsive: true, maintainAspectRatio: false, scales: { r: { angleLines: { display: true }, beginAtZero: true, max: 100, ticks: { stepSize: 20 } } }, plugins: { legend: { display: false } } } }); } function generateRecommendations(section1, section2, section3, section4, overall) { const recommendations = []; if (section1 < 60) { recommendations.push({ title: "Health Canada Regulatory Compliance", level: "high", content: "Immediate action needed to understand Software as Medical Device (SaMD) requirements and Good Machine Learning Practice (GMLP) principles. Consider engaging regulatory consultants and establishing multi-disciplinary evaluation teams." }); } if (section2 < 60) { recommendations.push({ title: "Provincial Privacy Legislation", level: "high", content: "Strengthen understanding of provincial privacy requirements and implement robust consent processes for AI use. Establish clear data governance policies and access controls." }); } if (section3 < 60) { recommendations.push({ title: "Professional Standards and Ethics", level: "medium", content: "Develop comprehensive AI literacy training programs and establish clear accountability frameworks. Implement bias detection and mitigation processes." }); } if (section4 < 60) { recommendations.push({ title: "Organizational Readiness", level: "medium", content: "Invest in IT infrastructure upgrades and secure dedicated resources for AI initiatives. Build leadership support and foster a culture of innovation." }); } if (overall >= 80) { recommendations.push({ title: "Advanced Implementation", level: "low", content: "Your organization shows excellent readiness. Focus on pilot implementations and continuous monitoring. Consider becoming a center of excellence for other organizations." }); } const recommendationsHTML = recommendations.map(rec => `

${rec.title}

${rec.content}

`).join(''); document.getElementById('recommendations-content').innerHTML = recommendationsHTML; } function generateActionPlan(section1, section2, section3, section4) { const actions = []; // Prioritize based on lowest scores const sections = [ { name: "Health Canada Compliance", score: section1, actions: ["Review SaMD classification requirements", "Establish GMLP compliance processes", "Create vendor assessment protocols"] }, { name: "Privacy Compliance", score: section2, actions: ["Update consent processes for AI", "Implement need-to-know access controls", "Establish data governance framework"] }, { name: "Professional Standards", score: section3, actions: ["Develop AI literacy training", "Create human oversight protocols", "Implement bias detection processes"] }, { name: "Organizational Readiness", score: section4, actions: ["Assess IT infrastructure needs", "Secure dedicated AI resources", "Build leadership support"] } ]; // Sort by score (lowest first) and take top priorities sections.sort((a, b) => a.score - b.score); const actionHTML = sections.slice(0, 2).map((section, index) => `

Priority ${index + 1}: ${section.name} (${Math.round(section.score)}%)

    ${section.actions.map(action => `
  • ${action}
  • `).join('')}
`).join(''); document.getElementById('action-plan-content').innerHTML = actionHTML; } function updateProvincialResources() { const province = assessmentData.orgInfo.province; const resources = { 'ON': [ 'Ontario - Personal Health Information Protection Act (PHIPA)', 'Information and Privacy Commissioner of Ontario', 'Ontario Health Data Centre', 'Digital Health Ontario Resources' ], 'BC': [ 'British Columbia - Personal Information Protection Act', 'Office of the Information and Privacy Commissioner (BC)', 'BC Ministry of Health AI Guidelines', 'Provincial Health Services Authority' ], 'AB': [ 'Alberta - Personal Information Protection Act (PIPA)', 'Office of the Information and Privacy Commissioner (AB)', 'Alberta Health AI Implementation Guide', 'Alberta Medical Association Resources' ], 'SK': [ 'Saskatchewan - Health Information Protection Act (HIPA)', 'Saskatchewan Information and Privacy Commissioner', 'Saskatchewan Health Authority Guidelines', 'College of Physicians and Surgeons of Saskatchewan' ] }; const provincialResourcesHTML = (resources[province] || ['Contact your provincial privacy commissioner', 'Review provincial health information legislation', 'Consult professional regulatory colleges']).map(resource => `
  • ${resource}
  • ` ).join(''); document.getElementById('provincial-resources').innerHTML = provincialResourcesHTML; } // Initialize updateProgress();