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El. knyga: Process Modeling and Management for Healthcare

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Edited by (Politecnico di Milano, Italy), Edited by (Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro, Italy), Edited by
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From the Foreword:

"[ This book] provides a comprehensive overview of the fundamental concepts in healthcare process management as well as some advanced topics in the cutting-edge research of the closely related areas. This book is ideal for graduate students and practitioners who want to build the foundations and develop novel contributions in healthcare process modeling and management."

--Christopher Yang, Drexel University

Process modeling and process management are traversal disciplines which have earned more and more relevance over the last two decades. Several research areas are involved within these disciplines, including database systems, database management, information systems, ERP, operations research, formal languages, and logic. Process Modeling and Management for Healthcare provides the reader with an in-depth analysis of what process modeling and process management techniques can do in healthcare, the major challenges faced, and those challenges remaining to be faced. The book features contributions from leading authors in the field.

The book is structured into two parts. Part one covers fundamentals and basic concepts in healthcare. It explores the architecture of a process management environment, the flexibility of a process model, and the compliance of a process model. It also features a real application domain of patients suffering from age-related macular degeneration.

Part two of the book includes advanced topics from the leading frontiers of scientific research on process management and healthcare. This section of the book covers software metrics to measure features of the process model as a software artifact. It includes process analysis to discover the formal properties of the process model prior to deploying it in real application domains. Abnormal situations and exceptions, as well as temporal clinical guidelines, are also presented in depth Pro.
List of Figures
xiii
List of Tables
xvii
Foreword xix
Preface xxi
Editors' Bios xxv
Contributors xxvii
Section I Fundamentals
Chapter 1 Models and Architectures for the Enactment of Healthcare Processes
3(32)
Carlo Combi
Barbara Oliboni
Giuseppe Pozzi
Francesca Zerbato
1.1 Introduction
4(2)
1.2 Healthcare Process Management
6(3)
1.3 The Business Process Model Notation -- Bpmn
9(2)
1.3.1 Applications of Bpmn In Healthcare
11(1)
1.4 Modeling Perspectives In Bpm
11(13)
1.5 Workflow Architectures
24(6)
1.6 Conclusion
30(5)
Exercises
31(1)
Glossary
31(2)
Further Reading
33(2)
Chapter 2 Flexible Support of Healthcare Processes
35(32)
Manfred Reichert
Rudiger Pryss
2.1 Introduction
36(1)
2.2 Healthcare Process Characteristics
37(2)
2.3 Flexibility Needs for Healthcare Processes
39(5)
2.3.1 Variability
40(1)
2.3.2 Adaptation
40(2)
2.3.3 Evolution
42(2)
2.3.4 Looseness
44(1)
2.4 Process Variability Support
44(5)
2.5 Process Adaptation Support
49(4)
2.6 Process Evolution Support
53(2)
2.6.1 Deferred Process Evolution
53(1)
2.6.2 Immediate Process Evolution and Instance Migration
54(1)
2.7 Process Looseness Support
55(4)
2.8 Other Process Flexibility Approaches
59(4)
2.8.1 Constraint-Based Processes
59(3)
2.8.2 Object-Centric Processes
62(1)
2.9 Summary
63(4)
Exercises
64(1)
Glossary
65(1)
Further Reading
66(1)
Chapter 3 Process Compliance
67(20)
Stefanie Rinderle-Ma
3.1 What Is Process Compliance?
67(2)
3.2 Compliance for Health Care Processes: Challenges
69(4)
3.3 Checking Compliance of Healthcare Processes At Designtime
73(4)
3.4 Monitoring Compliance Constraints Over Health Care Processes At Runtime
77(4)
3.4.1 Resource-Related Compliance Constraints
78(1)
3.4.2 Time-Related Compliance Constraints
78(1)
3.4.3 Data-Aware Compliance Constraints
79(1)
3.4.4 Ex-Post Compliance and Conformance Checks
80(1)
3.5 Data Quality In Healthcare
81(2)
3.6 Summary and Further Challenges
83(4)
Exercises
84(1)
Glossary
84(1)
Further Reading
84(3)
Chapter 4 Modeling A Process for Managing Age-Related Macular Degeneration
87(38)
Aitor Eguzkitza
Jesus D. Trigo
Miguel Martinez-Espronceda
Luis Serrano
Jose Andonegui
4.1 Introduction
88(2)
4.2 Background
90(4)
4.2.1 Age-Related Macular Degeneration (Amd)
90(1)
4.2.1.1 Classification System
91(1)
4.2.1.2 Strategy for Diagnosis and Follow-Up
92(1)
4.2.1.3 Therapeutic Recommendation
92(1)
4.2.2 Methodology To Formalize Clinical Practice Into Information Systems
93(1)
4.3 Modeling A High Resolution Consultation To Monitor the Treatment of Wet Amd
94(22)
4.3.1 Definition of the Project
95(2)
4.3.2 Design of the Clinical Process
97(1)
4.3.2.1 Step B.1: Definition of the Clinical Process
97(7)
4.3.2.2 Step B.2: Study of Clinical Concepts
104(1)
4.3.2.3 Step B.3: Hierarchical Organization of Knowledge Artifacts
104(2)
4.3.3 Building the Electronic Model
106(1)
4.3.3.1 Step C.1: Creation and Update of Archetypes
106(1)
4.3.3.2 Step C.2: Definition of Semantic Links To Clinical Terminologies
106(1)
4.3.3.3 Step C.3: Building Templates
107(4)
4.3.3.4 Step C.4: Modeling Guideline Rules and Workflow
111(4)
4.3.3.5 Step C.5: Modeling Ui Forms
115(1)
4.4 Implementation of the Service
116(3)
4.5 Discussion
119(2)
4.6 Conclusion
121(4)
Exercises
122(1)
Glossary
123(2)
Chapter 5 Scientific Workflows for Healthcare
125(24)
Giuseppe Tradigo
Patrizia Vizza
Pietro Hiram Guzzi
Andrea Tagarelli
Pierangelo Veltri
5.1 Introduction
126(1)
5.2 Interactive Patient Data Processing
127(8)
5.2.1 Hemodynamics Clinical Data Processing
128(3)
5.2.2 Electrophysiology Data Processing
131(1)
5.2.3 Visual Stimuli Data Processing In Magnetic Resonance
132(3)
5.3 Offline Patient Data Processing
135(9)
5.3.1 Sharing Epr Information for Clinical Protocol Studies
135(1)
5.3.2 Merging Geographic and Health Information
136(3)
5.3.3 Genome-Wide Association Studies for Precision Medicine
139(1)
5.3.4 Mass Spectrometry Workflow for Peptide Discovery
140(2)
5.3.5 Health Status Detection Through Audio Signal Analysis
142(2)
5.4 Summary and Further Perspectives
144(5)
Exercises
145(1)
Glossary
145(1)
Further Reading
146(3)
Section II Advanced Topics
Chapter 6 Metrics for Processes In Healthcare
149(16)
Jan Mendling
6.1 Characteristics of Processes In Healthcare
149(2)
6.2 Measuring the Complexity of Processes
151(5)
6.3 Measuring the Understanding of Processes
156(2)
6.4 Measuring the Performance of Processes
158(2)
6.5 Measuring the Conformance of Processes
160(1)
6.6 Connections Between Measurements
161(4)
Exercises
162(1)
Glossary
162(1)
Further Reading
162(3)
Chapter 7 Healthcare Process Analysis
165(30)
Robert Andrews
Suriadi Suriadi
Moe Wynn
Arthur H.M. Ter Hofstede
7.1 Introduction
166(2)
7.2 Background
168(3)
7.3 Process Mining for Healthcare Processes
171(19)
7.3.1 Preprocessing Hospital Data As An Event Log
172(1)
7.3.2 Data Quality
172(2)
7.3.3 Automated Discovery of Hospital Processes
174(4)
7.3.4 Checking Conformance To Clinical Guidelines
178(4)
7.3.5 Performance Analysis of Hospital Processes
182(2)
7.3.6 Comparative Analysis
184(6)
7.4 Challenges and Outlook
190(5)
Exercises
192(1)
Glossary
192(3)
Chapter 8 Exception Management In Healthcare Processes
195(22)
Mor Peleg
Giuseppe Pozzi
8.1 Introduction
196(1)
8.2 Basics of Exceptions
197(6)
8.2.1 Exception Definition
198(1)
8.2.2 Taxonomy of Expected Exceptions
199(2)
8.2.3 Some Examples of Exceptions
201(2)
8.3 Design Methodology for Exceptions In Healthcare Processes
203(12)
8.3.1 Some Examples of Exception Mapping
207(8)
8.4 Conclusions
215(2)
Exercises
215(1)
Glossary
216(1)
Further Reading
216(1)
Chapter 9 Temporal Clinical Guidelines
217(34)
Luca Anselma
Luca Piovesan
Paolo Terenziani
9.1 Introduction
218(1)
9.2 Representation of Time In Clinical Guidelines
219(13)
9.2.1 Database Representation of Patients' Data
219(2)
9.2.2 Knowledge Representation for Temporal Abstraction
221(5)
9.2.3 Representation of Temporal Constraints
226(6)
9.3 Reasoning About Time In Clinical Guidelines
232(19)
9.3.1 Constraint Propagation
232(1)
9.3.1.1 Introduction To Constraint Propagation
232(4)
9.3.1.2 Temporal Facilities for Clinical Guidelines
236(1)
9.3.1.3 Constraint Propagation In Clinical Guideline Systems
237(4)
9.3.1.4 Constraint Propagation In Artificial Intelligence
241(1)
9.3.2 Temporal Abstraction
242(1)
9.3.2.1 Temporal Abstraction Mechanisms
243(5)
Exercises
248(1)
Glossary
248(1)
Further Reading
249(2)
Bibliography 251(34)
Index 285
Carlo Combi is Full Professor at the Department of Computer Science, University of Verona, Verona, Italy. He received the Laurea Degree in Electrical Engineering from the Politecnico of Milano in 1987. He received the Ph.D. degree in Biomedical Engineering in 1993. He was in 1994 and 1995 Post-Doc fellow at the Department of Biomedical Engineering of the Politecnico of Milano. From 1987 to 1996 he worked within the research group in Medical Informatics at the Politecnico of Milan. From April 1996 to October 2001, Carlo Combi was with the Department of Mathematics and Computer Science of the University of Udine, Italy, as Assistant Professor. Since November 2001, he has been with the Department of Computer Science of the University of Verona, Italy. From November 2001 to February 2005, he was Associate Professor of Computer Science; since March 2005, he has been Professor of Computer Science. From October 2007 to September 2012, he served as head of the Department of Computer Science.

Giuseppe Pozzi is Associate Professor at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano, Milano, Italy. He received the Laurea Degree in Electrical Engineering from the Politecnico of Milano on June 1986. He received the Ph.D. degree on July 1992. On December 1992 he received a post-doc fellowship from Politecnico di Milano. From October 1st, 1993 to October 31st, 2002 he served as research assistant at the Dipartimento di Elettronica e Informazione of the Politecnico di Milano. Since November 1st, 2002 he has been associate professor at the Dipartimento di Elettronica, Informazione e Bioingegneria of the Politecnico di Milano. Giuseppe Pozzis past interests include computer-based analysis of the electrocardiographical signal, multiservice software for medicine, biomedical image compression, classification of public domain medical software, object-oriented and temporal databases for medicine. At present, his interests are mainly focused on active database systems, their application to WorkFlow Management Systems (WFMS), temporal databases and the management of temporal aspects in WfMSs. He is author/coauthor of more than 100 papers. He co-authored 4 books.

Pierangelo Veltri is Associate Professor of Bioinformatics and Computer Science at the Medicine and Surgical Science Department, University Magna Graecia of Catanzaro, Catanzaro, Italy. He received the Laurea Degree in Computer Engineering from the University of Calabria in April 1998. He received the Ph.D. degree from the University of Orsay (Paris XI) in 2002. He was researcher with european fellows from 1998 to 2002 at INRIA of Rocquencourt, France working in the Verso Database Group, and contract teacher from 2000 to 2002 at University of Paris XIII, teaching database and programming languages. From 2002 to 2011 was assistant professor at University of Catanzaro, teaching application of computer science in medicine degree. He is currently associate professor at the same University. He teaches Database for computer science and biomedical engineering master students and Advanced database and health informatic systems for biomedical engineering postgraduate students.

His past interests include semistructured and spatial databases. His interests are currently focused on data analytics, proteomics and genomics data management, voice and health related signal analysis, health informatics. He is author/co-author of more than 100 papers published in international conference proceeding, and more than 30 international journal papers. He has been editor of the ACM Sigbio (special interest group on Bioinformatics, Computational Biology and Biomedical Informatics) newsletter since 2013.
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