Handbook of Healthcare Analytics Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations

How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21^st century?

Editors Tinglong Dai and Sridhar Tayur, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing interests, global health, personalized medicine, residential care and concierge medicine, among others—and structures what has been a highly fragmented research area into a coherent scientific discipline.

The handbook also provides an easy-to-comprehend introduction to five essential research tools—Markov decision process, game theory and information economics, queueing games, econometric methods, and data science—by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts.

The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics. This Handbook:

• Instills analytics scholars with a way of thinking that incorporates behavioral, incentive, and policy considerations in various healthcare settings. This change in perspective—a shift in gaze away from narrow, local and one-off operational improvement efforts that do not replicate, scale or remain sustainable—can lead to new knowledge and innovative solutions that healthcare has been seeking so desperately.
• Facilitates collaboration between healthcare experts and analytics scholar to frame and tackle their pressing concerns through appropriate modern mathematical tools designed for this very purpose.

The handbook is designed to be accessible to the independent reader, and it may be used in a variety of settings, from a short lecture series on specific topics to a semester-long course.

Tinglong Dai, PhD, is Associate Professor of Operations Management and Business Analytics at Johns Hopkins University. A recipient of numerous awards, including Johns Hopkins Discovery Award, Institute for Operations Research and the Management Sciences (INFORMS) Public Sector Operations Research Best Paper Award and Production and Operations Management Society (POMS) Best Healthcare Paper Award, his research spans across healthcare analytics, marketing/operations interfaces, and artificial intelligence.

Sridhar Tayur, PhD, is Ford Distinguished Research Chair and Professor of Operations Management at Tepper School of Business, Carnegie Mellon University. He has been elected as Member of National Academy of Engineering, Fellow of Institute for Operations Research and the Management Sciences (INFORMS), and Distinguished Fellow of the Manufacturing and Service Operations Management Society (MSOM). An Academic Capitalist, he is Founder of the supply chain software company SmartOps and the social enterprise OrganJet.

List of Contributors xvii

Preface xix

Glossary of Terms xxvii

Acknowledgments xxxv

Part I Thrusts Macro-level Thrusts (MaTs)

1 Organizational Structure 1
Jay Levine

1.1 Introduction to the Healthcare Industry 2

1.2 Academic Medical Centers 6

1.3 Community Hospitals and Physicians 16

1.4 Conclusion 19

2 Access to Healthcare 21
Donald R. Fischer

2.1 Introduction 21

2.2 Goals 27

2.3 Opportunity for Action 29

3 Market Design 31
Itai Ashlagi

3.1 Introduction 31

3.2 Matching Doctors to Residency Programs 31

3.2.1 Early Days 31

3.2.2 A Centralized Market and New Challenges 32

3.2.3 Puzzles and Theory 33

3.3 Kidney Exchange 35

3.3.1 Background 35

3.3.2 Creating a Thick Marketplace for Kidney Exchange 36

3.3.3 Dynamic Matching 38

3.3.4 The Marketplace for Kidney Exchange in the United States 41

3.3.5 Final Comments on Kidney Exchange 43

References 44

Meso-level Thrusts (MeTs)

4 Competing Interests 51
Joel Goh

4.1 Introduction 51

4.2 The Literature on Competing Interests 53

4.2.1 Evaluation of Pharmaceutical Products 53

4.2.1.1 Individual Drug Classes 54

4.2.1.2 Multiple Interventions 55

4.2.1.3 Review Articles 56

4.2.2 Physician Ownership 56

4.2.2.1 Physician Ownership of Ancillary Services 57

4.2.2.2 Physician Ownership of Ambulatory Surgery Centers 59

4.2.2.3 Physician Ownership of Speciality Hospitals 60

4.2.2.4 Physician-Owned Distributors 61

4.2.3 Medical Reporting 62

4.2.3.1 DRG Upcoding 63

4.2.3.2 Non-DRG Upcoding 64

4.3 Examples 65

4.3.1 Example 1: Physician Decisions with Competing Interests 66

4.3.2 Example 2: Evidence of HAI Upcoding 70

4.4 Summary and FutureWork 72

References 73

5 Quality of Care 79
Hummy Song and Senthil Veeraraghavan

5.1 Frameworks for Measuring Healthcare Quality 79

5.1.1 The Donabedian Model 79

5.1.2 The AHRQ Framework 81

5.2 Understanding Healthcare Quality: Classification of the Existing

OR/MS Literature 82

5.2.1 Structure 82

5.2.2 Process 85

5.2.3 Outcome 91

5.2.4 Patient Experience 92

5.2.5 Access 94

5.3 Open Areas for Future Research 95

5.3.1 Understanding Structures and Their Interactions with Processes and Outcomes 95

5.3.2 Understanding Patient Experiences and Their Interactions with Structure 96

5.3.3 Understanding Processes andTheir Interactions with Outcomes 97

5.3.4 Understanding Access to Care 98

5.4 Conclusions 98

Acknowledgments 99

References 99

6 Personalized Medicine 109
Turgay Ayer and Qiushi Chen

6.1 Introduction 109

6.2 Sequential Decision Disease Models with Health Information Updates 111

6.2.1 Case Study: POMDP Model for Personalized Breast Cancer Screening 113

6.2.2 Case Study: Kalman Filter for Glaucoma Monitoring 116

6.2.3 Other Relevant Studies 118

6.3 One-Time Decision Disease Models with Risk Stratification 120

6.3.1 Case Study: Subtype-Based Treatment for DLBCL 121

6.3.2 Other Applications 124

6.4 Artificial Intelligence-Based Approaches 125

6.4.1 Learning from Existing Health Data 126

6.4.2 Learning from Trial and Error 127

6.5 Conclusions and Emerging Future Research Directions 128

References 130

7 Global Health 137
Karthik V. Natarajan and Jayashankar M. Swaminathan

7.1 Introduction 137

7.2 Funding Allocation in Global Health Settings 139

7.2.1 Funding Allocation for Disease Prevention 139

7.2.2 Funding Allocation for Treatment of Disease Conditions 143

7.2.2.1 Service Settings 143

7.2.2.2 Product Settings 146

7.3 Inventory Allocation in Global Health Settings 147

7.3.1 Inventory Allocation for Disease Prevention 147

7.3.2 Inventory Allocation for Treatment of Disease Conditions 149

7.4 Capacity Allocation in Global Health Settings 153

7.5 Conclusions and Future Directions 155

References 156

8 Healthcare Supply Chain 159
Soo-Haeng Cho and Hui Zhao

8.1 Introduction 159

8.2 Literature Review 162

8.3 Model and Analysis 164

8.3.1 Generic Injectable Drug Supply Chain 164

8.3.1.1 Model 166

8.3.1.2 Analysis 168

8.3.2 Influenza Vaccine Supply Chain 171

8.3.2.1 Model 172

8.3.2.2 Analysis 173

8.4 Discussion and Future Research 177

Appendix 180

Acknowledgment 182

References 182

9 Organ Transplantation 187
Bar𝚤¸s Ata, John J. Friedewald and A. CemRanda

9.1 Introduction 187

9.2 The Deceased-Donor Organ Allocation system: Stakeholders and Their Objectives 189

9.3 Research Opportunities in the Area 199

9.3.1 Past Research on the Transplant Candidate’s Problem 199

9.3.2 Challenges in Modeling Patient Choice 201

9.3.3 Past Research on the Deceased-donor Organ Allocation Policy 202

9.3.4 Challenges in Modeling the Deceased-donor Organ Allocation Policy 206

9.3.5 Research Problems from the Perspective of Other Stakeholders 206

9.4 Concluding Remarks 208

References 209

Micro-level Thrusts (MiTs)

10 Ambulatory Care 217
Nan Liu

10.1 Introduction 217

10.2 How Operations are Managed in Primary Care Practice 218

10.3 What Makes Operations Management Difficult in Ambulatory Care 220

10.3.1 Competing Objectives 220

10.3.2 Environmental Factors 221

10.4 Operations Management Models 222

10.4.1 System-Wide Planning 222

10.4.2 Appointment Template Design 226

10.4.3 Managing Patient Flow 231

10.5 New Trends in Ambulatory Care 234

10.5.1 Online Market 234

10.5.2 Telehealth 235

10.5.3 Retail Approach of Outpatient Care 236

10.6 Conclusion 237

References 237

11 Inpatient Care 243
Van-Anh Truong

11.1 Modeling the Inpatient Ward 244

11.2 Inpatient Ward Policies 246

11.3 Interface with ED 247

11.4 Interface with Elective Surgeries 248

11.5 Discharge Planning 250

11.6 Incentive, Behavioral, and Organizational Issues 251

11.7 Future Directions 252

11.7.1 Essential Quantitative Tools 253

11.7.2 Resources for Learners 253

References 253

12 Residential Care 257
Nadia Lahrichi, Louis-Martin Rousseau and Willem-Jan van Hoeve

12.1 Overview of Home Care Delivery 257

12.1.1 Home Care 258

12.1.2 Home Healthcare 258

12.1.2.1 Temporary Care 259

12.1.2.2 Specialized Programs 259

12.1.3 Operational Challenges 260

12.1.3.1 Discussion of the Planning Horizon 262

12.1.3.2 Home Care Planning Problem 263

12.2 An Overview of Optimization Technology 263

12.2.1 Linear Programming 263

12.2.2 Mixed Integer Programming 264

12.2.3 Constraint Programming 265

12.2.4 Heuristics and Dedicated Methods 265

12.2.5 Technology Comparison 266

12.2.5.1 Solution Expectations and Solver Capabilities 266

12.2.5.2 Development Time and Maintenance 267

12.3 Territory Districting 267

12.4 Provider-to-Patient Assignment 270

12.4.1 Workload Measures 270

12.4.2 Workload Balance 271

12.4.3 Assignment Models 272

12.4.4 Assignment of New Patients 273

12.5 Task Scheduling and Routing 273

12.6 Perspectives 276

12.6.1 Integrated Decision-Making Under a New Business Model 277

12.6.2 Home Telemetering Forecasting Adverse Events 277

12.6.3 Forecasting the Wound Healing Process 278

12.6.4 Adjustment of Capacity and Demand 279

References 280

13 ConciergeMedicine 287
Srinagesh Gavirneni and Vidyadhar G. Kulkarni

13.1 Introduction 287

13.2 Model Setup 291

13.3 Concierge Option—No Abandonment 293

13.3.1 A Given Participation Level 𝛼 294

13.3.2 How to choose d? 295

13.3.2.1 All Customers Are Better Off 295

13.3.2.2 Customers Are Better Off on Average 297

13.3.3 Optimal Participation Level 299

13.4 Concierge Option—Abandonment 301

13.4.1 Choosing the Optimal 𝛼 and 𝛽 303

13.5 Correlated Service Times and Waiting Costs 304

13.6 MDVIP Adoption 306

13.6.1 The Data 307

13.6.2 AbandonmentModel Applied to MDVIP Data 308

13.6.2.1 Modeling Heterogeneous Waiting Costs 309

13.6.2.2 Participation in Concierge Medicine 310

13.6.2.3 Impact of Concierge Medicine 310

13.6.2.4 Choosing the Concierge Participation Level 312

13.7 Research Opportunities 313

References 316

Part II Tools

14 Markov Decision Processes 319
Alan Scheller-Wolf

14.1 Introduction 319

14.2 Modeling 321

14.3 Types of Results 325

14.3.1 Numerical Results 325

14.3.2 Analytical Results 327

14.3.3 Insights 328

14.4 Modifications and Extensions of MDPs 328

14.4.1 Imperfect State Information 328

14.4.2 Extremely Large or Continuous State Spaces 329

14.4.3 Uncertainty about Transition Probabilities 330

14.4.4 Constrained Optimization 331

14.5 Future Applications 332

14.6 Recommendations for Additional Reading 333

References 334

15 Game Theory and Information Economics 337
Tinglong Dai

15.1 Introduction 337

15.2 Key Concepts 339

15.2.1 GameTheory: Key Concepts 339

15.2.2 Information Economics: Key Concepts 340

15.2.2.1 Nonobservability of Information 341

15.2.2.2 Asymmetric Information 341

15.3 Summary of Healthcare Applications 343

15.3.1 Incentive Design for Healthcare Providers 344

15.3.2 Quality-Speed Tradeoff 345

15.3.3 Gatekeepers 346

15.3.4 Healthcare Supply Chain 346

15.3.5 Vaccination 346

15.3.6 Organ Transplantation 347

15.3.7 Healthcare Network 347

15.3.8 Mixed Motives of Healthcare Providers 347

15.4 Potential Applications 348

15.4.1 Micro-Level applications 348

15.4.2 Macro-Level Applications 349

15.4.3 Meso-Level Applications 349

15.5 Resources for Learners 351

References 351

16 Queueing Games 355
Mustafa Akan

16.1 Introduction 355

16.1.1 Scope of the Review 356

16.2 Basic QueueingModels 356

16.2.1 Components of a Queueing System 356

16.2.2 Performance Measures 357

16.2.3 M/M/1 358

16.2.4 M/G/1 359

16.2.5 M/M/c 360

16.2.6 Priorities 361

16.2.6.1 Achievable Region Approach 363

16.2.7 Networks of Queues 364

16.2.8 Approximations 364

16.3 Strategic Queueing 365

16.3.1 Waiting as an Equilibrium Device 366

16.3.2 Demand Dependent on Service Time 367

16.3.3 Physician-Induced Demand 369

16.3.4 Joining the Queue 370

16.3.4.1 Observable Queue 370

16.3.4.2 Unobservable Queue 371

16.3.5 Waiting for a Better Match 373

16.4 Discussion and Future Research Directions 376

References 376

17 EconometricMethods 381
Diwas KC

17.1 Introduction 381

17.2 Statistical Modeling 382

17.2.1 Statistical Inference 383

17.2.2 Biased Estimates 384

17.3 The Experimental Ideal and the Search for Exogenous Variation 386

17.3.1 Instrumental Variables 386

17.3.1.1 Example 1 (IV): Patient Flow through an Intensive Care Unit 388

17.3.1.2 Example 2 (IV): Focused Factories 391

17.3.2 Difference Estimators 392

17.3.3 Fixed Effects Estimators 394

17.3.3.1 Examples 3-4 (D-in-D): Process Compliance and Peer Effects of Productivity 395

17.4 Structural Estimation 395

17.4.1 Example 5: Managing Operating Room Capacity 396

17.4.2 Example 6: Patient Choice Modeling 397

17.5 Conclusion 399

References 400

18 Data Science 403
Rema Padman

18.1 Introduction 403

18.1.1 Background 404

18.1.2 Methods 407

18.1.3 Attribute Selection and Ranking 408

18.1.4 Information Gain (IG) Attribute Ranking 408

18.1.5 Relief-F Attribute Ranking 408

18.1.6 Markov Blanket Feature Selection 408

18.1.7 Correlation-Based Feature Selection 409

18.1.8 Classification 409

18.2 Three Illustrative Examples of Data Science in Healthcare 410

18.2.1 Medication Reconciliation 410

18.2.2 Dynamic Prediction of Medical Risks 413

18.2.3 Practice-Based Clinical Pathway Learning 416

18.3 Discussion 419

18.3.1 Challenges and Opportunities 419

18.3.2 Data Science in Action 420

18.3.3 Health Data ScienceWorldwide 421

18.4 Conclusions 421

References 422

Index 429

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