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020 _a9783030539931 (
_q) (
_qelectronic bk.)
020 _a3030539938 (
_q) (
_qelectronic bk.)
020 _z303053992X
020 _z9783030539924
024 1 _aAU@
_b000068176382
040 _aYDX
_beng
_cYDX
_dEBLCP
_dUKAHL
050 _aR859.7
_b.F47 2021 EB
245 0 0 _aInteractive Process Mining in Healthcare
_cCarlos Fernández-Llatas
250 _a1st ed. 2021
260 _aCham
_bSpringer
_c2021
300 _a1 online resource
336 _2rdacontent
_aTexto (visual)
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
490 1 _aHealth Informatics Ser
505 0 _aIntro -- 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
505 8 _a2.1.3 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
505 8 _a2.3.3 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
505 8 _a3.5 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
505 8 _a4.2.9 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
655 0 _aElectronic books
776 0 8 _cOriginal
_z303053992X
_z9783030539924
_w(OCoLC)1159041320
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-53993-1
_zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942 _2lcc
_cLE
998 _db