Healthcare Analytics – From Data to Knowledge to Healthcare Improvement

Name: Healthcare Analytics – From Data to Knowledge to Healthcare Improvement
Author: H Yang

From Data to Knowledge to Healthcare Improvement

Gebonden Engels 2016 9781118919392

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Samenvatting

Features of statistical and operational research methods and tools being used to improve the healthcare industry

With a focus on cutting–edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data–driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency.

Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient–monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features:

Contributions from well–known international experts who shed light on new approaches in this growing area

Discussions on contemporary methods and techniques to address the handling of rich and large–scale healthcare data as well as the overall optimization of healthcare system operations

Numerous real–world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry

Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement

The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate–level courses typically offered within operations research, industrial engineering, business, and public health departments.

HUI YANG, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor–based modeling and analysis of complex systems for process monitoring/control; system diagnostics/ prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self–organizing behaviors.

EVA K. LEE, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health–risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large–scale healthcare/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.

Specificaties

ISBN13:9781118919392

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:632

Uitgever:John Wiley & Sons

Hoofdrubriek:Leiderschap, Algemeen management

Serie:Wiley Series in Operations Research and Management Science

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Inhoudsopgave

LIST OF CONTRIBUTORS xvii
PREFACE xxi
PART I ADVANCES IN BIOMEDICAL AND HEALTH INFORMATICS 1
1 Recent Development in Methodology for Gene Network Problems and Inferences 3 Sung W. Han and Hua Zhong
1.1 Introduction 3
1.2 Background 5
1.3 Genetic Data Available 7
1.4 Methodology 7
1.5 Search Algorithm 13
1.6 PC Algorithm 15
1.7 Application/Case Studies 16
1.8 Discussion 23
1.9 Other Useful Softwares 23
Acknowledgments 24
References 24
2 Biomedical Analytics and Morphoproteomics: An Integrative Approach for Medical Decision Making for Recurrent or Refractory Cancers 31 Mary F. McGuire and Robert E. Brown
2.1 Introduction 31
2.2 Background 32
2.3 Methodology 37
2.4 Case Studies 46
2.5 Discussion 51
2.6 Conclusions 52
Acknowledgments 53
References 53
3 Characterization and Monitoring of Nonlinear Dynamics and Chaos in Complex Physiological Systems 59 Hui Yang, Yun Chen, and Fabio Leonelli
3.1 Introduction 59
3.2 Background 61
3.3 Sensor–Based Characterization and Modeling of Nonlinear Dynamics 65
3.4 Healthcare Applications 80
3.5 Summary 88
Acknowledgments 90
References 90
4 Statistical Modeling of Electrocardiography Signal for Subject Monitoring and Diagnosis 95 Lili Chen, Changyue Song, and Xi Zhang
4.1 Introduction 95
4.2 Basic Elements of ECG 96
4.3 Statistical Modeling of ECG for Disease Diagnosis 99
4.4 An Example: Detection of Obstructive Sleep Apnea from a Single ECG Lead 115
4.5 Materials and Methods 115
4.6 Results 118
4.7 Conclusions and Discussions 121
References 121
5 Modeling and Simulation of Measurement Uncertainty in Clinical Laboratories 127 Varun Ramamohan, James T. Abbott, and Yuehwern Yih
5.1 Introduction 127
5.2 Background and Literature Review 129
5.3 Model Development Guidelines 138
5.4 Implementation of Guidelines: Enzyme Assay Uncertainty Model 141
5.5 Discussion and Conclusions 152
References 154
6 Predictive Analytics: Classification in Medicine and Biology 159 Eva K. Lee
6.1 Introduction 159
6.2 Background 161
6.3 Machine Learning with Discrete Support Vector Machine Predictive Models 163
6.4 Applying DAMIP to Real–World Applications 170
6.5 Summary and Conclusion 182
Acknowledgments 183
References 183
7 Predictive Modeling in Radiation Oncology 189 Hao Zhang, Robert Meyer, Leyuan Shi, Wei Lu, and Warren D Souza
7.1 Introduction 189
7.2 Tutorials of Predictive Modeling Techniques 191
7.3 Review of Recent Predictive Modeling Applications in Radiation Oncology 194
7.4 Modeling Pathologic Response of Esophageal Cancer to Chemoradiotherapy 199
7.5 Modeling Clinical Complications after Radiation Therapy 205
7.6 Modeling Tumor Motion with Respiratory Surrogates 211
7.7 Conclusion 215
References 215
8 Mathematical Modeling of Innate Immunity Responses of Sepsis: Modeling and Computational Studies 221 Chih–Hang J. Wu, Zhenshen Shi, David Ben–Arieh, and Steven Q. Simpson
8.1 Background 221
8.2 System Dynamic Mathematical Model (SDMM) 223
8.3 Pathogen Strain Selection 224
8.5 Discussion 247
8.6 Conclusion 254
References 254
PART II ANALYTICS FOR HEALTHCARE DELIVERY 261
9 Systems Analytics: Modeling and Optimizing Clinic Workflow and Patient Care 263 Eva K. Lee, Hany Y. Atallah, Michael D. Wright, Calvin Thomas IV, Eleanor T. Post, Daniel T. Wu, and Leon L. Haley Jr
9.1 Introduction 264
9.2 Background 266
9.3 Challenges and Objectives 267
9.4 Methods and Design of Study 268
9.5 Computational Results, Implementation, and ED Performance Comparison 285
9.6 Benefits and Impacts 292
9.7 Scientific Advances 297
Acknowledgments 298
References 299
10 A Multiobjective Simulation Optimization of the Macrolevel Patient Flow Distribution 303 Yunzhe Qiu and Jie Song
10.1 Introduction 303
10.2 Literature Review 305
10.3 Problem Description and Modeling 308
10.4 Methodology 312
10.5 Case Study: Adjusting Patient Flow for a Two–Level Healthcare System Centered on the Puth 316
10.6 Conclusions and the Future Work 329
Acknowledgments 330
References 331
11 Analysis of Resource Intensive Activity Volumes in US Hospitals 335 Shivon Boodhoo and Sanchoy Das
11.1 Introduction 335
11.2 Structural Classification of Hospitals 337
11.3 Productivity Analysis of Hospitals 339
11.4 Resource and Activity Database for US Hospitals 341
11.5 Activity–Based Modeling of Hospital Operations 344
11.6 Resource use Profile of Hospitals from HUC Activity Data 351
11.7 Summary 357
References 358
12 Discrete–Event Simulation for Primary Care Redesign: Review and a Case Study 361 Xiang Zhong, Molly Williams, Jingshan Li, Sally A. Kraft, and Jeffrey S. Sleeth
12.1 Introduction 361
12.2 Review of Relevant Literature 362
12.3 A Simulation Case Study at a Pediatric Clinic 369
12.4 What If Analyses 376
12.5 Conclusions 382
References 382
13 Temporal and Spatiotemporal Models for Ambulance Demand 389 Zhengyi Zhou and David S. Matteson
13.1 Introduction 389
13.2 Temporal Ambulance Demand Estimation 391
13.3 Spatiotemporal Ambulance Demand Estimation 398
13.4 Conclusions 409
References 410
14 Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries 413 Naoru Koizumi, Monica Gentili, Rajesh Ganesan, Debasree DasGupta, Amit Patel, Chun–Hung Chen, Nigel Waters, and Keith Melancon
14.1 Introduction 414
14.2 Methods 416
14.3 Results 423
14.4 Conclusions 433
Acknowledgment 435
References 435
15 Predictive Analytics in 30–Day Hospital Readmissions for Heart Failure Patients 439 Si–Chi Chin, Rui Liu, and Senjuti B. Roy
15.1 Introduction 440
15.2 Analytics in Prediction Hospital Readmission Risk 441
15.3 Analytics in Recommending Intervention Strategies 447
15.4 Related Work 457
15.5 Conclusion 459
References 459
16 Heterogeneous Sensing and Predictive Modeling of Postoperative Outcomes 463 Yun Chen, Fabio Leonelli, and Hui Yang
16.1 Introduction 463
16.2 Research Background 466
16.3 Research Methodology 474
16.4 Materials and Experimental Design 491
16.5 Experimental Results 491
16.6 Discussion and Conclusions 498
Acknowledgments 499
References 499
17 Analyzing Patient Physician Interaction in Consultation for Shared Decision Making 503 Thembi Mdluli, Joyatee Sarker, Carolina Vivas–Valencia, Nan Kong, and Cleveland G. Shields
17.1 Introduction 503
17.2 Literature Review 505
17.3 Our Recent Data Mining Studies 510
17.4 Future Directions 515
17.5 Concluding Remarks 519
References 520
18 The History and Modern Applications of Insurance Claims Data in Healthcare Research 523 Margrét V. Bjarndóttir, David Czerwinski, and Yihan Guan
18.1 Introduction 523
18.2 Healthcare Cost Predictions 531
18.3 Measuring Quality of Care 540
18.4 Conclusions 548
References 548
19 Understanding the Role of Social Media in Healthcare via Analytics: a Health Plan Perspective 555 Sinjini Mitra and Rema Padman
19.1 Introduction 555
19.2 Literature Review 556
19.3 Case Study Description 562
19.4 Research Methods and Analytics Tools 564
19.5 Results and Discussions 568
19.6 Conclusions 584
References 585
INDEX 589