How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st 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.
Table of Contents
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
