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Healthcare Analytics: From Data to Knowledge to Healthcare Improvement

Healthcare Analytics: From Data to Knowledge to Healthcare Improvement

Hui Yang (Editor), Eva K. Lee (Editor)

ISBN: 978-1-118-91940-8 August 2016 632 Pages

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Description

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.

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

1.4.1 Structural Equation Model 8

1.4.2 Score Function Formulation 9

1.4.3 Two-Stage Learning 12

1.4.4 Further Issues 13

1.5 Search Algorithm 13

1.5.1 Global Optimal Solution Search 13

1.5.2 Heuristic Algorithm for a Local Optimal Solution 14

1.6 PC Algorithm 15

1.7 Application/Case Studies 16

1.7.1 Skin Cutaneous Melanoma (SKCM) Data from the TCGA Data Portal Website 16

1.7.2 The CCLE (Cancer Cell Line Encyclopedia) Project 20

1.7.3 Cellular Signaling Network in Flow Cytometry Data 20

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

2.2.1 Data 33

2.2.2 Tools 33

2.2.3 Algorithms 34

2.2.4 Literature Review 35

2.3 Methodology 37

2.3.1 Morphoproteomics (Fig. 2.1(1–3)) 39

2.3.2 Biomedical Analytics (Fig. 2.1(4–10)) 40

2.3.3 Integrating Morphoproteomics and Biomedical Analytics 44

2.4 Case Studies 46

2.4.1 Clinical: Therapeutic Recommendations for Pancreatic Adenocarcinoma 46

2.4.2 Clinical: Biology Underlying Exceptional Responder in Refractory Hodgkin’s Lymphoma 48

2.4.3 Research: Role of the Hypoxia Pathway in Both Oncogenesis and Embryogenesis 50

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

3.3.1 Multifractal Spectrum Analysis of Nonlinear Time Series 65

3.3.2 Recurrence Quantification Analysis 75

3.3.3 Multiscale Recurrence Quantification Analysis 78

3.4 Healthcare Applications 80

3.4.1 Nonlinear Characterization of Heart Rate Variability 81

3.4.2 Multiscale Recurrence Analysis of Space–Time Physiological Signals 85

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

4.3.1 ECG Signal Denoising 100

4.3.2 Waveform Detection 105

4.3.3 Feature Extraction 106

4.3.4 Disease Classification and Diagnosis 111

4.4 An Example: Detection of Obstructive Sleep Apnea from a Single ECG Lead 115

4.4.1 Introduction to Obstructive Sleep Apnea 115

4.5 Materials and Methods 115

4.5.1 Database 115

4.5.2 QRS Detection and RR Correction 116

4.5.3 R Wave Amplitudes and EDR Signal 117

4.5.4 Feature Set 117

4.5.5 Classifier Training with Feature Selection 118

4.6 Results 118

4.6.1 QRS Detection and RR Correction 118

4.6.2 Feature Selection 118

4.6.3 OSA Detection 120

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

5.2.1 Measurement Uncertainty: Background and Analytical Estimation 130

5.2.2 Uncertainty in Clinical Laboratories 134

5.2.3 Uncertainty in Clinical Laboratories: A System Approach 136

5.3 Model Development Guidelines 138

5.3.1 System Description and Process Phases 138

5.3.2 Modeling Guidelines 139

5.4 Implementation of Guidelines: Enzyme Assay Uncertainty Model 141

5.4.1 Calibration Phase 142

5.4.2 Sample Analysis Phase 149

5.4.3 Results and Analysis 150

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

6.3.1 Modeling of Reserved-Judgment Region for General Groups 164

6.3.2 Discriminant Analysis via Mixed-Integer Programming 165

6.3.3 Model Variations 167

6.3.4 Theoretical Properties and Computational Strategies 170

6.4 Applying DAMIP to Real-World Applications 170

6.4.1 Validation of Model and Computational Effort 171

6.4.2 Applications to Biological and Medical Problems 171

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

7.2.1 Feature Selection 191

7.2.2 Support Vector Machine 192

7.2.3 Logistic Regression 193

7.2.4 Decision Tree 193

7.3 Review of Recent Predictive Modeling Applications in Radiation Oncology 194

7.3.1 Machine Learning for Medical Image Processing 194

7.3.2 Machine Learning in Real-Time Tumor Localization 196

7.3.3 Machine Learning for Predicting Radiotherapy Response 197

7.4 Modeling Pathologic Response of Esophageal Cancer to Chemoradiotherapy 199

7.4.1 Input Features 200

7.4.2 Feature Selection and Predictive Model Construction 200

7.4.3 Results 202

7.4.4 Discussion 204

7.5 Modeling Clinical Complications after Radiation Therapy 205

7.5.1 Dose-Volume Thresholds: Relationship to OAR Complications 205

7.5.2 Modeling the Radiation-Induced Complications via Treatment Plan Surface 206

7.5.3 Modeling Results 208

7.6 Modeling Tumor Motion with Respiratory Surrogates 211

7.6.1 Cyberknife System Data 211

7.6.2 Modeling for the Prediction of Tumor Positions 212

7.6.3 Results of Tumor Positions Modeling 212

7.6.4 Discussion 214

7.7 Conclusion 215

References 215

8 Mathematical Modeling of Innate Immunity Responses of Sepsis: Modeling and Computational Studies 221
Chih-Hang J. Wu, Zhenzhen 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

8.3.1 Step 1: Kupffer Local Response Model 224

8.3.2 Step 2: Neutrophils Immune Response Model 228

8.3.3 Step 3: Damaged Tissue Model 233

8.3.4 Step 4: Monocytes Immune Response Model 234

8.3.5 Step 5: Anti-inflammatory Immune Response Model 237

8.4 Mathematical Models of Innate Immunity of AIR 239

8.4.1 Inhibition of Anti-inflammatory Cytokines 239

8.4.2 Mathematical Model of Innate Immunity of AIR 239

8.4.3 Stability Analysis 241

8.5 Discussion 247

8.5.1 Effects of Initial Pathogen Load on Sepsis Progression 247

8.5.2 Effects of Pro- and Anti-inflammatory Cytokines on Sepsis Progression 250

8.6 Conclusion 254

References 254

Part II Analytics for Healthcare Delivery 261

9 Systems Analytics: Modeling and Optimizing ClinicWorkflow 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

9.4.1 ED Workflow and Services 269

9.4.2 Data Collection and Time-Motion Studies 270

9.4.3 Machine Learning for Predicting Patient Characteristics and Return Patterns 274

9.4.4 The Computerized ED System Workflow Model 277

9.4.5 Model Validation 282

9.5 Computational Results, Implementation, and ED Performance Comparison 285

9.5.1 Phase I: Results 285

9.5.2 Phase I: Adoption and Implementation 288

9.5.3 Phase II: Results 288

9.5.4 Phase II: Adoption and Implementation 290

9.6 Benefits and Impacts 292

9.6.1 Quantitative Benefits 294

9.6.2 Qualitative Benefits 296

9.7 Scientific Advances 297

9.7.1 Hospital Care Delivery Advances 297

9.7.2 OR Advances 298

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

10.2.1 Simulation Modeling on Patient Flow 305

10.2.2 Multiobjective Patient Flow Optimization Problems 306

10.2.3 Simulation Optimization 307

10.3 Problem Description and Modeling 308

10.3.1 Problem Description 308

10.3.2 System Modeling 310

10.4 Methodology 312

10.4.1 Simulation Model Description 312

10.4.2 Optimization 313

10.5 Case Study: Adjusting Patient Flow for a Two-Level Healthcare System Centered on the Puth 316

10.5.1 Background and Data 316

10.5.2 Simulation under Current Situation 318

10.5.3 Model Validation 320

10.5.4 Optimization through Algorithm 1 321

10.5.5 Optimization through Algorithm 2 322

10.5.6 Comparison of the Two Algorithms 327

10.5.7 Managerial Insights and Recommendations 328

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

11.4.1 Medicare Data Sources for Hospital Operations 343

11.5 Activity-Based Modeling of Hospital Operations 344

11.5.1 Direct Care Activities 344

11.5.2 The Hospital Unit of Care (HUC) Model 347

11.5.3 HUC Component Results by State 350

11.6 Resource use Profile of Hospitals from HUC Activity Data 351

11.6.1 Comparing the Resource Use Profile of States 353

11.6.2 Application of the Hospital Classification Rules 355

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

12.2.1 Literature on Primary Care Redesign 362

12.2.2 Literature on Discrete-Event Simulation in Healthcare 366

12.2.3 UW Health Improvement Projects 369

12.3 A Simulation Case Study at a Pediatric Clinic 369

12.3.1 Patient Flow 369

12.3.2 Model Development 371

12.3.3 Model Validation 376

12.4 What–If Analyses 376

12.4.1 Staffing Analysis 376

12.4.2 Resident Doctor 377

12.4.3 Schedule Template Change 377

12.4.4 Volume Change 379

12.4.5 Room Assignment 379

12.4.6 Early Start 380

12.4.7 Additional Observations 382

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

13.2.1 Notation 392

13.2.2 Factor Modeling with Constraints and Smoothing 393

13.2.3 Adaptive Forecasting with Time Series Models 395

13.3 Spatiotemporal Ambulance Demand Estimation 398

13.3.1 Spatiotemporal Finite Mixture Modeling 400

13.3.2 Estimating Ambulance Demand 403

13.3.3 Model Performance 405

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

14.2.1 Mathematical Model: Optimal Locations of Transplant Centers and OPO Boundaries 416

14.2.2 Discrete-Event Simulation: Evaluation of Optimal OPO Boundaries 422

14.3 Results 423

14.3.1 New Locations of Transplant Centers 423

14.3.2 New OPO Boundaries 426

14.3.3 Evaluation of New OPO Boundaries 428

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

15.2.1 The Overall Prediction Pipeline 441

15.2.2 Data Preprocessing 441

15.2.3 Predictive Models 442

15.2.4 Experiment and Evaluation 444

15.3 Analytics in Recommending Intervention Strategies 447

15.3.1 The Overall Intervention Pipeline 447

15.3.2 Bayesian Network Construction 448

15.3.3 Recommendation Rule Generation 452

15.3.4 Intervention Recommendation 453

15.3.5 Experiments 454

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

16.2.1 Acute Physiology and Chronic Health Evaluation (APACHE) 466

16.2.2 Simplified Acute Physiology Score (SAPS) 469

16.2.3 Mortality Probability Model (MPM) 470

16.2.4 Sequential Organ Failure Assessment (SOFA) 472

16.3 Research Methodology 474

16.3.1 Data Categorization 475

16.3.2 Data Preprocessing and Missing Data Imputation 475

16.3.3 Feature Extraction 482

16.3.4 Feature Selection 484

16.3.5 Predictive Model 487

16.3.6 Cross-Validation and Ensemble Voting Processes 489

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

17.2.1 Patient–Physician Interaction on Prognosis Discussion 506

17.2.2 Physician–Patient Interaction on Pain Assessment 509

17.3 Our Recent Data Mining Studies 510

17.3.1 Predicting Patient Satisfaction with Survey Data 510

17.3.2 Predicting Patient Satisfaction with Conservation Data 513

17.4 Future Directions 515

17.4.1 Regression Shrinkage and Selection 515

17.4.2 Conversational Characterization 517

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

18.1.1 Advantages and Limitations of Claims Data 525

18.1.2 Application Areas 526

18.1.3 Statistical Methodologies Used in Claims-Based Studies 528

18.2 Healthcare Cost Predictions 531

18.2.1 Modeling of Healthcare Costs 531

18.2.2 Modeling of Disease Burden and Interactions 533

18.2.3 Performance Measures and Baselines 534

18.2.4 Prediction Algorithms 534

18.2.5 Applying Regression Trees to Cost Predictions 535

18.2.6 Applying Clustering Algorithms to Cost Predictions 537

18.2.7 Identifying High-Cost Members 539

18.2.8 Discussion 539

18.3 Measuring Quality of Care 540

18.3.1 Structure, Process, and Outcomes 540

18.3.2 The Quality of Quality Data 542

18.3.3 Composite Quality Measures 542

18.3.4 Practical Considerations for Constructing Quality Scores 544

18.3.5 A Statistical Approach to Measuring Quality 545

18.3.6 Quality as a Case Management Tool 546

18.3.7 Discussion 547

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

19.2.1 Privacy and Security Concerns in Social Media and Healthcare 559

19.2.2 Analytics in Healthcare and Social Media 561

19.3 Case Study Description 562

19.3.1 Survey Design 563

19.4 Research Methods and Analytics Tools 564

19.4.1 The Logistic Regression Model 564

19.5 Results and Discussions 568

19.5.1 Descriptive Statistics 568

19.5.2 Baseline of Technology Usage 570

19.5.3 Mobile and Social Media Usage 571

19.5.4 Clustering of Member Population by Technology, Social, and Mobile Media Usage 572

19.5.5 Interest in Adopting Online Tools for Healthcare Purposes 573

19.5.6 Interest in Adopting Mobile Apps for Healthcare Purposes 574

19.5.7 Health and Wellness Objectives 577

19.5.8 Privacy and Security Concerns 580

19.5.9 Predictive Models 581

19.6 Conclusions 584

References 585

Index 589