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Mayo Clinic Engineering

To advance healthcare technology and innovation that enables patient-centered care and personalized medicine at global scale

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To advance healthcare technology and innovation that enables patient-centered care and personalized medicine at global scale

Strengths

  • INFRASTRUCTURE: Robust digital health platform serving 32M users
  • TALENT: World-class engineering and clinical informatics teams
  • DATA: Vast repository of clinical data for innovation (14PB+)
  • INTEGRATION: Seamless cross-platform clinical systems integration
  • REPUTATION: Trusted healthcare technology backed by Mayo brand

Weaknesses

  • LEGACY: Technical debt in critical clinical systems
  • AGILITY: Slow development cycles (avg 8 months for major releases)
  • ADOPTION: Uneven clinician uptake of new technology solutions
  • TALENT: Difficulty attracting top tech talent vs Silicon Valley
  • SECURITY: Growing complexity of compliance and security frameworks

Opportunities

  • PARTNERSHIP: Strategic tech alliances with major cloud providers
  • TELEHEALTH: Rapid growth in virtual care demand (127% YoY)
  • DATA: Monetization of anonymized health datasets and insights
  • GLOBAL: International expansion of digital health platforms
  • WEARABLES: Integration with consumer health monitoring devices

Threats

  • COMPETITION: Big tech companies entering healthcare space
  • REGULATION: Evolving compliance landscape for health data
  • CYBERSECURITY: Increasing sophistication of healthcare attacks
  • TALENT: War for engineering talent with health/ML expertise
  • COSTS: Rising infrastructure costs amid rapid data growth

Key Priorities

  • MODERNIZE: Accelerate legacy system modernization initiatives
  • TALENT: Develop specialized healthcare tech talent pipeline
  • INNOVATION: Expand open innovation and partnership ecosystem
  • SECURITY: Strengthen cybersecurity and compliance frameworks
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To advance healthcare technology and innovation that enables patient-centered care and personalized medicine at global scale

MODERNIZE

Transform legacy systems into future-proof platforms

  • MIGRATION: Complete cloud migration for 3 critical clinical systems with zero downtime
  • ARCHITECTURE: Implement microservices architecture for patient engagement platform by Q3
  • TECHNICAL DEBT: Reduce critical technical debt backlog by 40% as measured by SonarQube
  • AUTOMATION: Achieve 85% test automation coverage across core clinical applications
TALENT

Build world-class healthcare tech talent ecosystem

  • RECRUITMENT: Hire 35 senior engineers in AI, cloud, and security with 90+ day retention
  • ACADEMY: Launch Mayo Tech Academy with 100+ engineers completing advanced certification
  • RETENTION: Reduce engineering turnover to below 12% through targeted engagement programs
  • DIVERSITY: Increase underrepresented groups in tech roles by 25% through targeted outreach
INNOVATE

Accelerate healthcare breakthroughs via technology

  • AI MODELS: Deploy 5 clinically-validated AI models with documented 15%+ accuracy improvement
  • PARTNERSHIPS: Establish 3 strategic technology partnerships with validated clinical outcomes
  • PLATFORM: Launch healthcare innovation API platform with 500+ active developer users
  • INCUBATION: Create fast-track program graduating 10 internal healthcare tech innovations
SECURE

Protect patient data with world-class security

  • ZERO-TRUST: Implement zero-trust architecture for 100% of clinical applications by Q4
  • COMPLIANCE: Achieve HITRUST certification for all clinical platforms with zero exceptions
  • MONITORING: Deploy advanced threat detection covering 100% of infrastructure with <15min MTTR
  • TRAINING: Achieve 95% completion rate for role-specific security training across all IT staff
METRICS
  • DIGITAL PLATFORM ENGAGEMENT: 45M monthly active users by 2025 (currently 32M)
  • PATIENT SATISFACTION: Achieve NPS of 85 for digital platforms (currently 76)
  • ENGINEERING VELOCITY: Reduce release cycle time by 40% (from 8 months to 4.8 months)
VALUES
  • Patient-centered innovation
  • Technology excellence
  • Data-driven decision making
  • Collaborative problem solving
  • Continuous improvement
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Align the learnings

Mayo Clinic Engineering Retrospective

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To advance healthcare technology and innovation that enables patient-centered care and personalized medicine at global scale

What Went Well

  • REVENUE: Digital health services grew 23% YoY, exceeding projections by 7%
  • ADOPTION: Telehealth visits increased 42% with 89% patient satisfaction
  • EFFICIENCY: IT automation initiatives reduced operational costs by $12.8M
  • INNOVATION: 14 new digital health tools released, 2 ahead of schedule

Not So Well

  • PROJECTS: 3 major platform initiatives delayed by average of 4.2 months
  • TALENT: Engineering turnover reached 17%, above healthcare tech average
  • INTEGRATION: API standardization project 32% behind milestone schedule
  • SECURITY: 3 critical vulnerabilities required emergency patching

Learnings

  • AGILE: Cross-functional teams deliver 3.2x faster than siloed groups
  • CLOUD: Hybrid cloud architecture reduced costs 28% vs on-premise only
  • TALENT: Remote-first hiring expanded qualified candidate pool by 174%
  • PARTNERS: Co-development with 3rd parties accelerated time-to-market

Action Items

  • MODERNIZE: Accelerate legacy modernization with 25% budget increase
  • TALENT: Launch healthcare tech academy with competitive compensation
  • SECURITY: Implement zero-trust architecture across all clinical systems
  • INFRASTRUCTURE: Migrate 70% of remaining on-premise workloads to cloud
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To advance healthcare technology and innovation that enables patient-centered care and personalized medicine at global scale

Strengths

  • EXPERTISE: Top AI research team with clinical domain knowledge
  • INFRASTRUCTURE: Established ML ops platform for model deployment
  • DATA: Unparalleled clinical dataset quality and diversity
  • INTEGRATION: AI seamlessly embedded in clinical workflows
  • VALIDATION: Rigorous AI validation and clinical testing processes

Weaknesses

  • SILOS: Fragmented AI initiatives across departments
  • TALENT: Shortage of specialized healthcare AI engineers
  • COMPUTE: Limited high-performance computing infrastructure
  • ADOPTION: Clinical resistance to AI-augmented decision support
  • GOVERNANCE: Incomplete AI ethics and governance frameworks

Opportunities

  • DIAGNOSIS: AI-enhanced diagnostic accuracy across specialties
  • EFFICIENCY: Workflow automation to address clinical staff shortage
  • PERSONALIZATION: Precision medicine through genomic AI models
  • RESEARCH: Accelerated drug discovery and clinical trials
  • PREVENTIVE: Predictive analytics for population health

Threats

  • COMPETITION: Tech giants with superior AI infrastructure
  • REGULATION: Evolving FDA oversight of AI as medical devices
  • TRUST: Patient concerns about AI in clinical decision-making
  • LIABILITY: Unclear legal framework for AI-assisted diagnoses
  • BIAS: Potential for biased algorithms affecting patient outcomes

Key Priorities

  • UNIFY: Create centralized AI Center of Excellence
  • COMPUTE: Invest in dedicated healthcare AI infrastructure
  • GOVERNANCE: Develop comprehensive AI ethics framework
  • INTEGRATION: Seamlessly embed AI into clinical workflows