🎓 About & Research Foundation
Understanding the Academic Foundation Behind QualityWisdom
📚 Research Foundation
QualityWisdom is built upon two complementary peer-reviewed research publications that establish both the systematic and adaptive approaches to quality management in complex, technology-driven environments.
📊 Systematic Process Analysis Research
This foundational study explores the revolutionary application of process mining in quality management across industries. Using radiation oncology as a case study - an industry known for its complexity, inherent risks, and sheer volume of data - the research demonstrates how adopting a process-oriented management approach and systemic thinking is essential for ensuring safety, efficiency, and the highest quality outcomes in any high-stakes environment.
The study utilized process mining techniques along with a quality management system to analyze event logs obtained from operational systems, revealing hidden patterns and inefficiencies that traditional management approaches often miss across various industries.
"Process mining offers a data-centric method for analyzing and improving workflows to ensure optimal outcomes. By transitioning from traditional management to a data-driven leadership approach, organizations can optimize workflows, enhance quality, and adapt to the evolving demands of modern operations."
🤖 AI Integration & Real-Time Response Research
This complementary research addresses the critical challenge of managing unforeseen events in AI-enhanced healthcare environments. The study proposes a comprehensive framework based on High Reliability Organization (HRO) principles for managing real-time, unexpected events in complex clinical workflows.
The research emphasizes the critical role of human-centered decision-making through cognitive diversity, psychological safety, and emotional intelligence to foster collective intelligence, enabling teams to navigate AI-driven complexities while safeguarding patient safety.
"As AI continues to shape radiation oncology, this framework emphasizes the role of collective intelligence in navigating AI-driven complexities and safeguarding patient outcomes through proactive risk assessment, adaptive teamwork at situation assessment points, and reactive learning through incident analysis."
🔮 Proactive Phase
Risk assessments, preventive measures, and team readiness through huddles and safety checks
⚡ Adaptive Phase
Real-time situation assessment leveraging collective intelligence and team adaptability
🔄 Reactive Phase
Post-event analysis, debriefings, and systematic improvement integration
🔗 Research Synergy
Together, these publications create a comprehensive approach to quality management: the first provides the systematic analytical foundation through process mining, while the second addresses the dynamic human factors necessary for managing unexpected events in technology-enhanced environments. This dual approach forms the core philosophy behind QualityWisdom's integrated platform.
🔬 Methodology
The research employed a comprehensive methodology integrating multiple analytical approaches, centered around the Plan-Do-Study-Act (PDSA) cycle framework:
Figure 1: Integrated PDSA Cycle Framework with Process Mining and Quality Management Components
This framework demonstrates how process mining integrates with traditional quality improvement methodologies to create a comprehensive, data-driven approach to organizational excellence.
Figure 2: Evolution from Traditional Management to Adaptive Leadership
This visualization demonstrates the transformation from rigid, top-down processes (red straight line) to data-driven, adaptive workflows (green smooth curve) that better reflect real-world complexity and enable continuous improvement.
Figure 3: High Reliability Organization (HRO) Framework for Managing AI-Enhanced Healthcare Operations
This framework illustrates the three-phase approach (Proactive, Adaptive, Reactive) with psychological safety and cognitive diversity as foundational elements, incorporating specific tools and HRO principles for each phase.
📊 Process Mining Framework
- Process Discovery: Automatic identification of actual workflow patterns
- Conformance Checking: Comparison of planned vs. actual processes
- Process Enhancement: Data-driven improvement recommendations
- Organizational Mining: Analysis of team collaboration and resource utilization
🔄 PDSA Integration
The process mining framework was integrated into the Plan-Do-Study-Act (PDSA) cycle for continuous quality improvement:
- Plan: Use process mining to develop process models
- Do: Implement planned processes with continuous monitoring
- Study: Analyze collected data to evaluate effectiveness
- Act: Make necessary adjustments based on insights
🏥 Quality Management Integration
- Incident Learning System (ILS) for error reporting and pattern detection
- Daily huddles with multidisciplinary cognitive diversity
- Structured problem categorization (Simple, Complicated, Complex)
- Multiple root cause analysis tools (5 Whys, Fishbone, Brainstorming)
💡 Key Findings
🎯 Process Bottlenecks Identified
Through process mining analysis, critical workflow bottlenecks were discovered in the radiation oncology case study:
- Manual Review Processes: Extended delays due to traditional manual workflows
- Quality Control Checks: Resource constraints causing workflow delays
Result: Targeted interventions led to significant process optimization and time reduction.
📈 Measurable Improvements (Case Study)
- 43% reduction in review cycle time (52.83 to 30.27 hours)
- 33% improvement in overall throughput time (15 to 10 days)
- Statistical significance confirmed (p < 0.001)
- Methodology applicable across industries with similar complexity
🤖 AI Integration Success
Implementation of AI-powered contouring showed:
- Substantial improvement in dosimetrist workflow and comfort
- Reduced manual contouring burden
- Maintained quality through robust supervision protocols
👥 Organizational Insights
Organizational mining revealed:
- Team collaboration patterns and communication barriers
- Resource utilization optimization opportunities
- The critical importance of cognitive diversity in decision-making
🏗️ Implementation in QualityWisdom
Both research foundations have been directly implemented in QualityWisdom, creating a comprehensive platform that combines systematic analysis with adaptive human-centered response capabilities:
🧠 Cognitive Diversity & Psychological Safety
- T0 Critical Decision Support: Multidisciplinary team activation implementing situation assessment points
- Brainstorming Tools: Structured ideation fostering psychological safety and preventing cognitive biases
- Team Huddles: Daily cognitive diversity sessions implementing proactive, adaptive, and reactive phases
- SBAR Communication: Structured situation-background-assessment-recommendation framework
🔍 Multi-Perspective Analysis & HRO Principles
- 5 Whys Analysis: Deep root cause investigation for reactive learning
- Fishbone Diagrams: Systematic cause categorization
- FMEA Integration: Proactive risk assessment aligned with HRO principles
- CAST Analysis: Complex systems accident analysis for unexpected events
- STPA Integration: System-theoretic process analysis for proactive risk management
📊 Process Analytics & Data-Driven Insights
- Process Mining Dashboards: Real-time workflow visualization revealing hidden patterns
- Bottleneck Detection: Automated efficiency analysis preventing predictable failures
- Variant Analysis: Best practice identification through process discovery
- Conformance Checking: Comparison of planned vs. actual processes
🔄 Adaptive Framework Implementation
- Three-Phase Management: Proactive preparation, adaptive response, reactive learning
- PDSA Cycle Integration: Systematic improvement methodology
- Incident Learning System: Pattern recognition and continuous learning
- Project Management: Improvement initiative tracking with real-time adaptation
⚡ Real-Time Situation Assessment
- Collective Intelligence Tools: Team problem-solving frameworks
- Emotional Intelligence Integration: Team dynamics management during high-pressure situations
- Force Functions & Checklists: Baseline safety systems
- Dynamic Response Protocols: Adaptive teamwork for unexpected events
🤖 AI & Technology Integration
- Human-AI Collaboration: Frameworks for managing AI-driven complexities
- Situational Awareness Tools: Real-time monitoring and assessment capabilities
- Technology Risk Assessment: Proactive evaluation of AI integration risks
- Adaptive Quality Assurance: Dynamic QA protocols for technology-enhanced workflows
📈 Comprehensive Leadership Transformation
QualityWisdom embodies both research findings about the evolution from traditional to adaptive, data-driven leadership:
Traditional Management
- Top-down predefined processes
- Rigid adherence to protocols
- Limited adaptability
- Reactive problem-solving
- Siloed decision-making
Adaptive Data-Driven Leadership
- Process mining insights with human-centered adaptability
- Flexible, responsive workflows with situation assessment
- Continuous evidence-based improvement
- Proactive risk management with real-time adaptation
- Collective intelligence and cognitive diversity
📖 Citations
📊 Process Mining Research
Bakhtiari, M. (2025). Utilizing process mining in quality management: A case study in radiation oncology. PLOS Digital Health, 4(5), e0000647.
DOI: https://doi.org/10.1371/journal.pdig.0000647
Published: May 15, 2025 | Journal: PLOS Digital Health
🤖 AI Integration Research
Bakhtiari, M. (2025). Radiation oncology at crossroads: Rise of AI and managing the unexpected. Journal of Applied Clinical Medical Physics.
DOI: https://doi.org/10.1002/acm2.70043
Published: February 17, 2025 | Journal: Journal of Applied Clinical Medical Physics
🌟 Combined Research Impact
This dual research approach demonstrates how organizations can both systematically analyze their processes through data-driven insights AND develop human-centered frameworks for managing unexpected events in technology-enhanced environments. Together, they provide a comprehensive blueprint for quality management transformation in complex systems.