Interactive Process Mining in Healthcare

Foreword
Preface
Acknowledgements
Contents
1 Interactive Process Mining in Healthcare: An Introduction
	1.1 A New Age in Health Care
	1.2 The Look for the Best Medical Evidence: Data Driven vs Knowledge Driven
	1.3 To an Interactive Approach
	1.4 Why Process Mining?
	1.5 Interactive Process Mining
	References
Part I Basics
	2 Value-Driven Digital Transformation in Health and Medical Care
		2.1 Evolution of Patient-Centric Medical Care
			2.1.1 Holistic Approaches to Healthcare Improvement in a Patient-Centric Framework
			2.1.2 VALUE Based HC Concept
			2.1.3 The Triple Aim of Healthcare with Attention for Health Care Professionals: The Quadruple AIM
		2.2 Data-Driven Sustainable Healthcare Framework
			2.2.1 International Consortium for Health Outcome Measures
			2.2.2 Digital Health Transformation
			2.2.3 IT Infrastructure as Enabling Agent of Digital Transformation
			2.2.4 Artificial Intelligence Widely Available for Contributing to the Transformation
		2.3 Challenges and Adoption Barriers to Digital Healthcare Transformation
			2.3.1 Data Management Clash
			2.3.2 Organizational Self-awareness for Digital Adoption Readiness
			2.3.3 Inherent Risks of AI
			2.3.4 Actions to Reduce Challenges, Hurdles and Barriers
		2.4 Summary
		References
	3 Towards a Knowledge and Data-Driven Perspective in Medical Processes
		3.1 Introduction
		3.2 Process-Related Perspectives in Healthcare
		3.3 Technologies for Clinical Decision-Making
			3.3.1 Computer-Interpretable Guidelines
			3.3.2 Development and Maintenance Issues with Computer-Interpretable Guidelines
		3.4 Technologies for Clinical Process Management
			3.4.1 Process Discovery and Continuous Improvement
			3.4.2 Workflow Inference Models
		3.5 Challenges of Clinical Decision-Making and Process Management Technologies
		References
	4 Process Mining in Healthcare
		4.1 Process Mining
		4.2 Process Mining in Healthcare
			4.2.1 Variability in the Medical Processes
			4.2.2 Infrequent Behaviour Could be the Interesting One
			4.2.3 Medical Processes Should be Personalized
			4.2.4 Medical Processes Are Not Deterministic
			4.2.5 Medical Decisions Are Not Only Based on Medical Evidence, But Also on Medical Expertise
			4.2.6 Understandability Is Key
			4.2.7 Must Involve Real World Data
			4.2.8 Solving the Real Problem
			4.2.9 Different Solutions for Different Medical Disciplines
			4.2.10 Medical Processes Evolve in Time
		4.3 Conclusion
		References
	5 Data Quality in Process Mining
		5.1 Introduction
		5.2 Data Quality Taxonomies
			5.2.1 General Data Quality Taxonomies
			5.2.2 Data Quality Taxonomies in Process Mining
				5.2.2.1 Process Mining Manifesto
				5.2.2.2 Taxonomy by 5:bosewanna2013
				5.2.2.3 Taxonomy by 5:verhulst2016evaluating
				5.2.2.4 Event Log Imperfection Patterns by 5:suriadi2017event
				5.2.2.5 Taxonomy by 5:vanbrabant2019quality
		5.3 Data Quality Assessment
			5.3.1 Data Quality Issues in Real-Life Healthcare Logs
			5.3.2 Data Quality Assessment Frameworks
				5.3.2.1 Framework by 5:fox2018data
				5.3.2.2 Framework by 5:andrews2019leveraging
				5.3.2.3 Framework by 5:martin2019interactive
			5.3.3 Tools for Data Quality Assessment
		5.4 Data Cleaning
			5.4.1 Data Cleaning Heuristics
				5.4.1.1 Incorrect Timestamps
				5.4.1.2 Missing Case Identifiers
				5.4.1.3 Missing Events
				5.4.1.4 Incorrect/Missing Attribute Values
			5.4.2 A Reflection on Data Cleaning Heuristics
		5.5 Conclusion
		References
	6 Towards Open Process Models in Healthcare: Open Standards and Legal Considerations
		6.1 Introduction
			6.1.1 Pathways, Guidelines and Computerized Clinical Decision Support
		6.2 The Need of Semantics for Clinical Processes
		6.3 Data and Contextual Semantics with openEHR
			6.3.1 Governance of Clinical Models
			6.3.2 The Connection of Process Mining with OpenEHR
		6.4 Workflow Semantics with openEHR
		6.5 Privacy and Legal Framework
		References
Part II Interactive Process Mining in Health
	7 Applying Interactive Process Mining Paradigm in Healthcare Domain
		7.1 Dealing with Digital Transformation Paradigm in Healthcare
		7.2 Data Science for Medicine: Filling the Gap Between Data and Decision
			7.2.1 Will the Doctors Be Replaced by Computers?
			7.2.2 Towards an Interactive Pattern Recognition Approach
			7.2.3 Through Explainable Models
		7.3 Interactive Process Mining
		7.4 Discussion and Conclusions
		References
	8 Bringing Interactive Process Mining to Health Professionals: Interactive Data Rodeos
		8.1 Introduction
		8.2 Interactive Process Mining Data Rodeos
			8.2.1 Data Rodeo Sessions
			8.2.2 Data Rodeos in an Interactive Process Methodology
		8.3 Interactive Data Tools for Data Rodeos
			8.3.1 Process Mining Ingestion
			8.3.2 Log Filtering and Processing
			8.3.3 Process Mining Discovery
			8.3.4 Model Processing
			8.3.5 Model Enhancement
		8.4 Conclusions
		References
	9 Interactive Process Mining in Practice: Interactive Process Indicators
		9.1 Approaching the Process Assessment to Health Professionals
		9.2 Interactive Process Indicators (IPIs)
		9.3 Measuring the Value Chain
		9.4 Interactive Process Indicators by Example
			9.4.1 Analyzing the Hospital Process
			9.4.2 Base Process
			9.4.3 Adding a Special Unit
			9.4.4 Creating an Information Campaign
		9.5 Conclusions
		References
Part III Interactive Process Mining in Action
	10 Interactive Process Mining in Emergencies
		10.1 The Emergency Process
		10.2 An Interactive Process Indicator for Emergency Departments
			10.2.1 Seasons
			10.2.2 Working Days and Weekends
			10.2.3 Age
			10.2.4 Hyperfrequenters
			10.2.5 Returns and Readmissions
			10.2.6 Length of Stay
			10.2.7 Exitus
		10.3 Discussion and Conclusion
		References
	11 Interactive Process Mining in Surgery with Real Time Location Systems: Interactive Trace Correction
		11.1 Introduction
		11.2 Background
		11.3 Trace Correction
		11.4 Experiments
			11.4.1 Interactive Pattern Recognition for Improving the Application of Error-Correcting Techniques to RTLS
			11.4.2 Physical Model as Graph Model
			11.4.3 Interactive Error Model
			11.4.4 Results of the Algorithm Using the Physical Model
			11.4.5 Interactive Process Correction: Process Graph Model
		11.5 Discussion and Conclusions
		References
	12 Interactive Process Mining in Type 2 Diabetes Mellitus
		12.1 Introduction
		12.2 Type 2 Diabetes as a Process
		12.3 Process Mining Approach to Type 2 Diabetes
		12.4 Type 2 Diabetes Management Processes
			12.4.1 Analysis of HbA1C
		12.5 Conclusion
		References
	13 Interactive Process Mining in IoT and Human BehaviourModelling
		13.1 Introduction
		13.2 Study Data and Procedure
			13.2.1 Clustering Behaviour Models
		13.3 Results
			13.3.1 Group 0
			13.3.2 Group 1
			13.3.3 Group 2
			13.3.4 Group 3
		13.4 Interpreting Group IPIs
		13.5 Conclusion
		References
	14 Interactive Process Mining for Medical Training
		14.1 Process Mining in Medical Training
		14.2 POME Methodology
		14.3 Model Stage
			14.3.1 Process Modeling
			14.3.2 Delphi Panel
		14.4 Data Stage
			14.4.1 Execution and Recording
			14.4.2 Video Tagging
		14.5 Analysis Stage
		14.6 Conclusion
		References
	15 Interactive Process Mining for Discovering Dynamic Risk Models in Chronic Diseases
		15.1 Introduction
		15.2 Chronic Conditions
		15.3 Assessing Chronic Conditions with Risk Models
		15.4 Interactive Data Rodeo for Creating Dynamic Risk Models
			15.4.1 Interactive Process Indicators for BMI and BP
		15.5 Discussion and Conclusions
		References
	16 Interactive Process Mining-Induced Change Management Methodology for Healthcare
		16.1 Towards an Interactive Change Management Model in Value-Based Healthcare
		16.2 Interactive Process Mining-Informed Change Management Methodology for Healthcare
		16.3 The Team
		16.4 Assessment Phase
			16.4.1 Readiness Assessment
			16.4.2 Stakeholders\' Map
		16.5 Arrangement Phase
			16.5.1 Stage 1: Team Setup
			16.5.2 Stage 2: Orientation and Creativity
			16.5.3 Stage 3: Optimization
			16.5.4 Stage 4: Mise en place
			16.5.5 Stage 5: First Contact
		16.6 Adaptation and Adoption Phase
		16.7 Application Phase
			16.7.1 Analysing Change
			16.7.2 Norming Change
			16.7.3 Performing Change
			16.7.4 Monitoring Change
			16.7.5 Fixing Change
		16.8 Conclusion
		References
	17 Interactive Process Mining Challenges
		17.1 Introduction
		17.2 Engage Health Professionals
		17.3 Look for the Best Representation Languages
		17.4 Interactive Data Quality Assessment
		17.5 Data Protection Laws Barriers
		17.6 Dealing with Medical Data
		17.7 Validation and Adaption of Best Practices and Clinical Guidelines
		17.8 Conclusions
		References
Index