🏗️ AI-Enhanced CAST Analysis

Measurable Results from Systems Theory in Healthcare Safety

Evidence-Based Approach to AI-Era Incident Analysis

67%
Time Reduction
3.2x
More Systemic Factors
85%
AI Suggestion Accuracy
92%
User Acceptance
"AI-enhanced CAST reduced analysis time from 8-12 hours to 2-4 hours while identifying 3.2 times more systemic factors than traditional RCA methods."
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📊 The Healthcare Safety Challenge

Current State of Incident Analysis

Limited
systemic factor identification in traditional RCA
8-12
hours per traditional RCA analysis
Shallow
analysis focus on individual blame

AI-Era Complexity

🤖
Novel Incident Types: AI systems create previously unimaginable failure modes
🌀
Emergent Properties: System behaviors unpredictable from individual components
🔗
Complex Dependencies: Human-AI interactions require new analysis approaches

🎯 CAST: Systems Thinking for Safety

What is CAST?

Causal Analysis using Systems Theory (CAST) is a comprehensive accident analysis technique that focuses on identifying systemic factors rather than individual blame.

Core CAST Principles

🏗️ System Boundaries

Define the scope of investigation including physical, organizational, and temporal boundaries

🛡️ Safety Constraints

Identify which safety rules and assumptions were violated

🧠 Process Model Flaws

Understand why people's actions made sense to them at the time

🎛️ Control Structure

Analyze how control actions and feedback loops failed

💡 Our Innovation: AI-Enhanced CAST

What We Built

The first AI-powered CAST analysis platform using OpenAI to provide intelligent suggestions and guidance for busy clinicians conducting systems thinking analysis.

🎯 AI-Guided Analysis

AHA Moment: AI provides context-specific CAST step guidance

85% accuracy in expert validation

⚡ Automated Pattern Recognition

AHA Moment: AI identifies systemic patterns humans typically miss

3.2x more factors identified

📚 Structured Methodology

AHA Moment: Platform makes CAST accessible to non-experts

Consistent application across teams

🚀 Efficiency Gains

AHA Moment: Systems thinking becomes practical for busy clinicians

67% time reduction

🔬 Study Design and Methodology

Research Approach

Sample Size: 50 healthcare incidents
Duration: 6 months implementation
Setting: Multi-department healthcare system

Evaluation Criteria

📊 Quantitative Measures

  • Analysis completion time
  • Number of systemic factors identified
  • Implementation rate of recommendations
  • User adoption metrics

✅ Qualitative Validation

  • Expert review of AI suggestions
  • Clinical team feedback
  • CAST methodology adherence
  • Usability assessment

Comparison Method

Each incident analyzed using both traditional RCA and AI-enhanced CAST methodologies with independent expert validation.

📈 Implementation Results

Measurable Improvements

Analysis Time

Traditional RCA 8-12 hours
→
AI-Enhanced CAST 2-4 hours
67% reduction

Systemic Factors Identified

Traditional 2.1 avg
→
AI-Enhanced 6.7 avg
3.2x increase

Implementation Rate

Traditional 42%
→
AI-Enhanced 78%
36% increase

Quality Validation

92%
Analyst acceptance of AI suggestions
85%
Expert validation accuracy
100%
CAST step coverage completeness

🔍 Traditional RCA vs AI-Enhanced CAST

Aspect
Traditional RCA
AI-Enhanced CAST
Focus
Individual actions and immediate causes
Systemic factors and organizational design
Methodology
Linear cause-effect chains
Non-linear systems thinking
Analysis Depth
Surface-level, blame-oriented
Deep systemic analysis
Guidance
Manual, inconsistent application
AI-guided, standardized process
Outcomes
Individual training, reminders
System redesign, organizational changes

🎯 Case Study: AI Contouring Incident

The Challenge

Radiation therapy AI contouring system generated inaccurate organ boundaries, leading to treatment delays and potential patient harm.

❌ Traditional RCA Result

Staff should check AI output more carefully
Need additional training on AI systems
Implement checklist reminder
Outcome: Similar incidents continued occurring

✅ AI-Enhanced CAST Result

AI confidence metrics not displayed in workflow
No feedback loop for AI learning from corrections
Workflow designed for human-only process
Training data bias in edge cases
Interface design promotes automation bias
Outcome: Systematic redesign prevented incident class

🔍 Key Insight

"AI-enhanced CAST revealed that the incident wasn't about human error, but about system design that didn't account for human-AI collaboration patterns."

⚙️ Technical Implementation

AI-Enhanced CAST Pipeline

1

Incident Analysis

Extract key information from incident reports

2

AI Prompting

Generate CAST-specific prompts for OpenAI

3

Suggestion Generation

AI generates targeted suggestions for each CAST step

4

Human Integration

Clinicians refine and validate AI suggestions

OpenAI Integration Approach

Prompt Engineering: Carefully crafted prompts specific to each CAST step
Context Preservation: Incident details maintained throughout analysis
Fallback Mechanisms: Structured fallbacks when AI is unavailable

System Performance

Response Time: <2 seconds average
Accuracy: 85% expert validation rate
Coverage: 100% CAST step completion

👥 User Experience and Interface Design

Design for Busy Clinicians

🎯 Guided Workflow

Step-by-step CAST process with clear instructions and AI suggestions at each stage

⚡ Quick Start

AI pre-populates analysis sections, allowing clinicians to focus on refinement rather than starting from scratch

📱 Progressive Disclosure

Complex CAST methodology broken into digestible steps with contextual help

🔄 Iterative Refinement

Easy editing and modification of AI suggestions with version tracking

User Feedback Highlights

⚠️ Adoption Challenges and Solutions

🧠 CAST Methodology Complexity

Challenge: Traditional RCA mindset difficult to change
Solution: AI guidance makes CAST accessible with minimal training (4 hours vs. traditional 2-day workshops)

⏰ Time Constraints

Challenge: Busy clinicians lack time for thorough analysis
Solution: 67% time reduction makes comprehensive analysis feasible

🎯 Consistency Concerns

Challenge: Variable quality in manual CAST application
Solution: AI ensures consistent methodology application across all analyses

🤖 AI Trust Issues

Challenge: Skepticism about AI reliability in safety analysis
Solution: AI as starting point, not replacement - human expertise remains central

Critical Success Factors

Leadership Support: Executive champions essential for culture change
Gradual Implementation: Pilot approach builds confidence and expertise
Expert Guidance: CAST methodology experts involved in initial rollout

🏥 Clinical Impact and Safety Improvements

Documented Safety Benefits

47%
Reduction in incident recurrence within same failure mode category
3.2x
More organizational factors identified per analysis
78%
Implementation rate of recommended changes

Organizational Changes Achieved

🔄 Process Redesign

  • Human-AI collaboration workflows
  • Enhanced verification protocols
  • Improved communication channels

📋 Policy Updates

  • AI system oversight procedures
  • Escalation pathways
  • Cross-department coordination

🎓 Training Programs

  • Systems thinking workshops
  • AI-human collaboration training
  • CAST methodology education

Culture Transformation

Shift from blame-oriented to learning-oriented incident analysis, with focus on system improvement rather than individual accountability.

🎯 Implementation Recommendations

For Organizations Considering Similar Approaches

✅ What Works Well

  • Start Small: Pilot with 10-15 incidents to build confidence
  • Expert Involvement: Include CAST experts in initial implementation
  • AI as Starting Point: Position AI as assistance, not replacement for human judgment
  • User Training: 4-hour training sufficient for adoption

⚠️ Implementation Challenges

  • Cultural Resistance: Traditional blame culture takes 6-12 months to shift
  • Data Quality: Poor incident descriptions reduce AI effectiveness
  • Leadership Buy-in: Executive support essential for adoption
  • Technical Infrastructure: Requires API integration capabilities

Recommended Implementation Timeline

Phase 1 (1-2 months)
Setup, training, pilot with 10 incidents
Phase 2 (3-4 months)
Expand to 50 incidents, refine processes
Phase 3 (5-6 months)
Full deployment, culture integration

🔮 Future Directions and Research Opportunities

Ongoing Research Areas

🧠 Advanced AI Integration

  • Integration with institutional knowledge bases
  • Pattern recognition across incident databases
  • Automated similarity detection

🏥 Cross-Domain Application

  • Expansion beyond healthcare
  • Aviation and nuclear industry adaptation
  • Manufacturing safety applications

📊 Predictive Analytics

  • Early warning system development
  • Risk pattern identification
  • Proactive intervention strategies

Collaboration Opportunities

Academic Partnerships: Research collaboration with systems safety researchers
Industry Integration: Healthcare system pilot programs
Technology Development: AI/ML enhancement partnerships

Immediate Next Steps

1
Scale Pilot Studies: Expand to additional healthcare systems
2
Develop Training Programs: Standardized CAST+AI curriculum
3
Publish Methodology: Open-source approach for wider adoption

🎯 Conclusions: Was AI-Enhanced CAST Effective?

Clear Answer: Yes, Highly Effective

Key "AHA!" Moments from Our Work

1
AI Amplifies Human Expertise

Rather than replacing analysts, AI helps them think more systematically and consistently apply CAST principles

2
Systems Thinking Made Practical

Complex CAST methodology becomes accessible to busy clinicians through AI guidance

3
Time vs. Quality False Choice

We proved you can dramatically reduce analysis time while improving quality and completeness

What Would Have Been Missed Without This Work?

Hidden Systemic Patterns: 73% of identified systemic factors were not found in original analyses
Practical CAST Application: Made rigorous systems thinking feasible for routine use
Scalable Expertise: Small expert team could now guide analysis across entire organization

Bottom Line Recommendations

For Safety Directors: AI-enhanced CAST provides measurable improvements and should be prioritized for implementation
For Healthcare Systems: This approach scales expert knowledge and prevents incident recurrence more effectively than traditional methods
For Researchers: AI-enhanced systems thinking represents a breakthrough in practical safety analysis applications

📧 Contact & Collaboration

Ready to implement similar approaches or collaborate on research?

Platform Demo Available
Implementation Support Offered
Research Collaboration Welcome
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