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|a 1204137688
|a 1228842624
|a 1237455096
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|a 9783030539931
|q (electronic bk.)
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|a 10.1007/978-3-030-53993-1
|2 doi
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|a (OCoLC)1202438811
|z (OCoLC)1204137688
|z (OCoLC)1228842624
|z (OCoLC)1237455096
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| 049 |
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|a HCDD
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| 245 |
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|a Interactive process mining in healthcare /
|c Carlos Fernandez-Llatas, editor.
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| 260 |
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|a Cham :
|b Springer,
|c 2021.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
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| 347 |
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|b PDF
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| 490 |
1 |
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|a Health Informatics
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| 505 |
0 |
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|a Intro -- 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
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| 505 |
8 |
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|a 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
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|a 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
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|a 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
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| 505 |
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|a 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
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|a Includes index.
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|a This book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making. Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.
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|a Online resource; title from PDF title page (SpringerLink, viewed January 29, 2021).
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| 650 |
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|a Artificial intelligence
|x Medical applications.
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| 650 |
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|a Data mining.
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| 650 |
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|a Medical informatics.
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| 650 |
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|a Medicine
|x Data processing.
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| 650 |
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|a Health services administration.
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| 650 |
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|a Medicine
|x Data processing
|2 fast
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| 650 |
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|a Health services administration
|2 fast
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| 650 |
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|a Artificial intelligence
|x Medical applications
|2 fast
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| 650 |
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|a Data mining
|2 fast
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| 650 |
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7 |
|a Medical informatics
|2 fast
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| 700 |
1 |
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|a Fernandez-Llatas, Carlos.
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| 776 |
0 |
8 |
|i Print version:
|z 303053992X
|z 9783030539924
|w (OCoLC)1159041320
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| 830 |
|
0 |
|a Health informatics.
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-53993-1
|y Click for online access
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| 903 |
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|a SPRING-MED2021
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| 994 |
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|a 92
|b HCD
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