Healthcare Analytics: From Data to Knowledge to Healthcare Improvement

ISBN-13: 9781118919392 / Angielski / Twarda / 2016 / 632 str.

Opis

Features 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, "He""althcare 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 efficient 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 care quality and operational efficiency.

Organized into two main sections, "Part One" 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 Two" 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 the various analytical methods and tools that can be applied to successful predictive modeling

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.

Spis treści

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

Blurb

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.