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 xviiPreface xixGlossary of Terms xxviiAcknowledgments xxxvPart I Thrusts Macro-level Thrusts (MaTs)1 Organizational Structure 1Jay Levine1.1 Introduction to the Healthcare Industry 21.2 Academic Medical Centers 61.3 Community Hospitals and Physicians 161.4 Conclusion 192 Access to Healthcare 21Donald R. Fischer2.1 Introduction 212.2 Goals 272.3 Opportunity for Action 293 Market Design 31Itai Ashlagi3.1 Introduction 313.2 Matching Doctors to Residency Programs 313.2.1 Early Days 313.2.2 A Centralized Market and New Challenges 323.2.3 Puzzles and Theory 333.3 Kidney Exchange 353.3.1 Background 353.3.2 Creating a Thick Marketplace for Kidney Exchange 363.3.3 Dynamic Matching 383.3.4 The Marketplace for Kidney Exchange in the United States 413.3.5 Final Comments on Kidney Exchange 43References 44Meso-level Thrusts (MeTs)4 Competing Interests 51Joel Goh4.1 Introduction 514.2 The Literature on Competing Interests 534.2.1 Evaluation of Pharmaceutical Products 534.2.1.1 Individual Drug Classes 544.2.1.2 Multiple Interventions 554.2.1.3 Review Articles 564.2.2 Physician Ownership 564.2.2.1 Physician Ownership of Ancillary Services 574.2.2.2 Physician Ownership of Ambulatory Surgery Centers 594.2.2.3 Physician Ownership of Speciality Hospitals 604.2.2.4 Physician-Owned Distributors 614.2.3 Medical Reporting 624.2.3.1 DRG Upcoding 634.2.3.2 Non-DRG Upcoding 644.3 Examples 654.3.1 Example 1: Physician Decisions with Competing Interests 664.3.2 Example 2: Evidence of HAI Upcoding 704.4 Summary and FutureWork 72References 735 Quality of Care 79Hummy Song and Senthil Veeraraghavan5.1 Frameworks for Measuring Healthcare Quality 795.1.1 The Donabedian Model 795.1.2 The AHRQ Framework 815.2 Understanding Healthcare Quality: Classification of the ExistingOR/MS Literature 825.2.1 Structure 825.2.2 Process 855.2.3 Outcome 915.2.4 Patient Experience 925.2.5 Access 945.3 Open Areas for Future Research 955.3.1 Understanding Structures and Their Interactions with Processes and Outcomes 955.3.2 Understanding Patient Experiences and Their Interactions with Structure 965.3.3 Understanding Processes andTheir Interactions with Outcomes 975.3.4 Understanding Access to Care 985.4 Conclusions 98Acknowledgments 99References 996 Personalized Medicine 109Turgay Ayer and Qiushi Chen6.1 Introduction 1096.2 Sequential Decision Disease Models with Health Information Updates 1116.2.1 Case Study: POMDP Model for Personalized Breast Cancer Screening 1136.2.2 Case Study: Kalman Filter for Glaucoma Monitoring 1166.2.3 Other Relevant Studies 1186.3 One-Time Decision Disease Models with Risk Stratification 1206.3.1 Case Study: Subtype-Based Treatment for DLBCL 1216.3.2 Other Applications 1246.4 Artificial Intelligence-Based Approaches 1256.4.1 Learning from Existing Health Data 1266.4.2 Learning from Trial and Error 1276.5 Conclusions and Emerging Future Research Directions 128References 1307 Global Health 137Karthik V. Natarajan and Jayashankar M. Swaminathan7.1 Introduction 1377.2 Funding Allocation in Global Health Settings 1397.2.1 Funding Allocation for Disease Prevention 1397.2.2 Funding Allocation for Treatment of Disease Conditions 1437.2.2.1 Service Settings 1437.2.2.2 Product Settings 1467.3 Inventory Allocation in Global Health Settings 1477.3.1 Inventory Allocation for Disease Prevention 1477.3.2 Inventory Allocation for Treatment of Disease Conditions 1497.4 Capacity Allocation in Global Health Settings 1537.5 Conclusions and Future Directions 155References 1568 Healthcare Supply Chain 159Soo-Haeng Cho and Hui Zhao8.1 Introduction 1598.2 Literature Review 1628.3 Model and Analysis 1648.3.1 Generic Injectable Drug Supply Chain 1648.3.1.1 Model 1668.3.1.2 Analysis 1688.3.2 Influenza Vaccine Supply Chain 1718.3.2.1 Model 1728.3.2.2 Analysis 1738.4 Discussion and Future Research 177Appendix 180Acknowledgment 182References 1829 Organ Transplantation 187Bar𝚤¸s Ata, John J. Friedewald and A. CemRanda9.1 Introduction 1879.2 The Deceased-Donor Organ Allocation system: Stakeholders and Their Objectives 1899.3 Research Opportunities in the Area 1999.3.1 Past Research on the Transplant Candidate’s Problem 1999.3.2 Challenges in Modeling Patient Choice 2019.3.3 Past Research on the Deceased-donor Organ Allocation Policy 2029.3.4 Challenges in Modeling the Deceased-donor Organ Allocation Policy 2069.3.5 Research Problems from the Perspective of Other Stakeholders 2069.4 Concluding Remarks 208References 209Micro-level Thrusts (MiTs)10 Ambulatory Care 217Nan Liu10.1 Introduction 21710.2 How Operations are Managed in Primary Care Practice 21810.3 What Makes Operations Management Difficult in Ambulatory Care 22010.3.1 Competing Objectives 22010.3.2 Environmental Factors 22110.4 Operations Management Models 22210.4.1 System-Wide Planning 22210.4.2 Appointment Template Design 22610.4.3 Managing Patient Flow 23110.5 New Trends in Ambulatory Care 23410.5.1 Online Market 23410.5.2 Telehealth 23510.5.3 Retail Approach of Outpatient Care 23610.6 Conclusion 237References 23711 Inpatient Care 243Van-Anh Truong11.1 Modeling the Inpatient Ward 24411.2 Inpatient Ward Policies 24611.3 Interface with ED 24711.4 Interface with Elective Surgeries 24811.5 Discharge Planning 25011.6 Incentive, Behavioral, and Organizational Issues 25111.7 Future Directions 25211.7.1 Essential Quantitative Tools 25311.7.2 Resources for Learners 253References 25312 Residential Care 257Nadia Lahrichi, Louis-Martin Rousseau and Willem-Jan van Hoeve12.1 Overview of Home Care Delivery 25712.1.1 Home Care 25812.1.2 Home Healthcare 25812.1.2.1 Temporary Care 25912.1.2.2 Specialized Programs 25912.1.3 Operational Challenges 26012.1.3.1 Discussion of the Planning Horizon 26212.1.3.2 Home Care Planning Problem 26312.2 An Overview of Optimization Technology 26312.2.1 Linear Programming 26312.2.2 Mixed Integer Programming 26412.2.3 Constraint Programming 26512.2.4 Heuristics and Dedicated Methods 26512.2.5 Technology Comparison 26612.2.5.1 Solution Expectations and Solver Capabilities 26612.2.5.2 Development Time and Maintenance 26712.3 Territory Districting 26712.4 Provider-to-Patient Assignment 27012.4.1 Workload Measures 27012.4.2 Workload Balance 27112.4.3 Assignment Models 27212.4.4 Assignment of New Patients 27312.5 Task Scheduling and Routing 27312.6 Perspectives 27612.6.1 Integrated Decision-Making Under a New Business Model 27712.6.2 Home Telemetering Forecasting Adverse Events 27712.6.3 Forecasting the Wound Healing Process 27812.6.4 Adjustment of Capacity and Demand 279References 28013 ConciergeMedicine 287Srinagesh Gavirneni and Vidyadhar G. Kulkarni13.1 Introduction 28713.2 Model Setup 29113.3 Concierge Option—No Abandonment 29313.3.1 A Given Participation Level 𝛼 29413.3.2 How to choose d? 29513.3.2.1 All Customers Are Better Off 29513.3.2.2 Customers Are Better Off on Average 29713.3.3 Optimal Participation Level 29913.4 Concierge Option—Abandonment 30113.4.1 Choosing the Optimal 𝛼 and 𝛽 30313.5 Correlated Service Times and Waiting Costs 30413.6 MDVIP Adoption 30613.6.1 The Data 30713.6.2 AbandonmentModel Applied to MDVIP Data 30813.6.2.1 Modeling Heterogeneous Waiting Costs 30913.6.2.2 Participation in Concierge Medicine 31013.6.2.3 Impact of Concierge Medicine 31013.6.2.4 Choosing the Concierge Participation Level 31213.7 Research Opportunities 313References 316Part II Tools14 Markov Decision Processes 319Alan Scheller-Wolf14.1 Introduction 31914.2 Modeling 32114.3 Types of Results 32514.3.1 Numerical Results 32514.3.2 Analytical Results 32714.3.3 Insights 32814.4 Modifications and Extensions of MDPs 32814.4.1 Imperfect State Information 32814.4.2 Extremely Large or Continuous State Spaces 32914.4.3 Uncertainty about Transition Probabilities 33014.4.4 Constrained Optimization 33114.5 Future Applications 33214.6 Recommendations for Additional Reading 333References 33415 Game Theory and Information Economics 337Tinglong Dai15.1 Introduction 33715.2 Key Concepts 33915.2.1 GameTheory: Key Concepts 33915.2.2 Information Economics: Key Concepts 34015.2.2.1 Nonobservability of Information 34115.2.2.2 Asymmetric Information 34115.3 Summary of Healthcare Applications 34315.3.1 Incentive Design for Healthcare Providers 34415.3.2 Quality-Speed Tradeoff 34515.3.3 Gatekeepers 34615.3.4 Healthcare Supply Chain 34615.3.5 Vaccination 34615.3.6 Organ Transplantation 34715.3.7 Healthcare Network 34715.3.8 Mixed Motives of Healthcare Providers 34715.4 Potential Applications 34815.4.1 Micro-Level applications 34815.4.2 Macro-Level Applications 34915.4.3 Meso-Level Applications 34915.5 Resources for Learners 351References 35116 Queueing Games 355Mustafa Akan16.1 Introduction 35516.1.1 Scope of the Review 35616.2 Basic QueueingModels 35616.2.1 Components of a Queueing System 35616.2.2 Performance Measures 35716.2.3 M/M/1 35816.2.4 M/G/1 35916.2.5 M/M/c 36016.2.6 Priorities 36116.2.6.1 Achievable Region Approach 36316.2.7 Networks of Queues 36416.2.8 Approximations 36416.3 Strategic Queueing 36516.3.1 Waiting as an Equilibrium Device 36616.3.2 Demand Dependent on Service Time 36716.3.3 Physician-Induced Demand 36916.3.4 Joining the Queue 37016.3.4.1 Observable Queue 37016.3.4.2 Unobservable Queue 37116.3.5 Waiting for a Better Match 37316.4 Discussion and Future Research Directions 376References 37617 EconometricMethods 381Diwas KC17.1 Introduction 38117.2 Statistical Modeling 38217.2.1 Statistical Inference 38317.2.2 Biased Estimates 38417.3 The Experimental Ideal and the Search for Exogenous Variation 38617.3.1 Instrumental Variables 38617.3.1.1 Example 1 (IV): Patient Flow through an Intensive Care Unit 38817.3.1.2 Example 2 (IV): Focused Factories 39117.3.2 Difference Estimators 39217.3.3 Fixed Effects Estimators 39417.3.3.1 Examples 3-4 (D-in-D): Process Compliance and Peer Effects of Productivity 39517.4 Structural Estimation 39517.4.1 Example 5: Managing Operating Room Capacity 39617.4.2 Example 6: Patient Choice Modeling 39717.5 Conclusion 399References 40018 Data Science 403Rema Padman18.1 Introduction 40318.1.1 Background 40418.1.2 Methods 40718.1.3 Attribute Selection and Ranking 40818.1.4 Information Gain (IG) Attribute Ranking 40818.1.5 Relief-F Attribute Ranking 40818.1.6 Markov Blanket Feature Selection 40818.1.7 Correlation-Based Feature Selection 40918.1.8 Classification 40918.2 Three Illustrative Examples of Data Science in Healthcare 41018.2.1 Medication Reconciliation 41018.2.2 Dynamic Prediction of Medical Risks 41318.2.3 Practice-Based Clinical Pathway Learning 41618.3 Discussion 41918.3.1 Challenges and Opportunities 41918.3.2 Data Science in Action 42018.3.3 Health Data ScienceWorldwide 42118.4 Conclusions 421References 422Index 429

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 xviiPreface xixGlossary of Terms xxviiAcknowledgments xxxvPart I Thrusts Macro-level Thrusts (MaTs)1 Organizational Structure 1Jay Levine1.1 Introduction to the Healthcare Industry 21.2 Academic Medical Centers 61.3 Community Hospitals and Physicians 161.4 Conclusion 192 Access to Healthcare 21Donald R. Fischer2.1 Introduction 212.2 Goals 272.3 Opportunity for Action 293 Market Design 31Itai Ashlagi3.1 Introduction 313.2 Matching Doctors to Residency Programs 313.2.1 Early Days 313.2.2 A Centralized Market and New Challenges 323.2.3 Puzzles and Theory 333.3 Kidney Exchange 353.3.1 Background 353.3.2 Creating a Thick Marketplace for Kidney Exchange 363.3.3 Dynamic Matching 383.3.4 The Marketplace for Kidney Exchange in the United States 413.3.5 Final Comments on Kidney Exchange 43References 44Meso-level Thrusts (MeTs)4 Competing Interests 51Joel Goh4.1 Introduction 514.2 The Literature on Competing Interests 534.2.1 Evaluation of Pharmaceutical Products 534.2.1.1 Individual Drug Classes 544.2.1.2 Multiple Interventions 554.2.1.3 Review Articles 564.2.2 Physician Ownership 564.2.2.1 Physician Ownership of Ancillary Services 574.2.2.2 Physician Ownership of Ambulatory Surgery Centers 594.2.2.3 Physician Ownership of Speciality Hospitals 604.2.2.4 Physician-Owned Distributors 614.2.3 Medical Reporting 624.2.3.1 DRG Upcoding 634.2.3.2 Non-DRG Upcoding 644.3 Examples 654.3.1 Example 1: Physician Decisions with Competing Interests 664.3.2 Example 2: Evidence of HAI Upcoding 704.4 Summary and FutureWork 72References 735 Quality of Care 79Hummy Song and Senthil Veeraraghavan5.1 Frameworks for Measuring Healthcare Quality 795.1.1 The Donabedian Model 795.1.2 The AHRQ Framework 815.2 Understanding Healthcare Quality: Classification of the ExistingOR/MS Literature 825.2.1 Structure 825.2.2 Process 855.2.3 Outcome 915.2.4 Patient Experience 925.2.5 Access 945.3 Open Areas for Future Research 955.3.1 Understanding Structures and Their Interactions with Processes and Outcomes 955.3.2 Understanding Patient Experiences and Their Interactions with Structure 965.3.3 Understanding Processes andTheir Interactions with Outcomes 975.3.4 Understanding Access to Care 985.4 Conclusions 98Acknowledgments 99References 996 Personalized Medicine 109Turgay Ayer and Qiushi Chen6.1 Introduction 1096.2 Sequential Decision Disease Models with Health Information Updates 1116.2.1 Case Study: POMDP Model for Personalized Breast Cancer Screening 1136.2.2 Case Study: Kalman Filter for Glaucoma Monitoring 1166.2.3 Other Relevant Studies 1186.3 One-Time Decision Disease Models with Risk Stratification 1206.3.1 Case Study: Subtype-Based Treatment for DLBCL 1216.3.2 Other Applications 1246.4 Artificial Intelligence-Based Approaches 1256.4.1 Learning from Existing Health Data 1266.4.2 Learning from Trial and Error 1276.5 Conclusions and Emerging Future Research Directions 128References 1307 Global Health 137Karthik V. Natarajan and Jayashankar M. Swaminathan7.1 Introduction 1377.2 Funding Allocation in Global Health Settings 1397.2.1 Funding Allocation for Disease Prevention 1397.2.2 Funding Allocation for Treatment of Disease Conditions 1437.2.2.1 Service Settings 1437.2.2.2 Product Settings 1467.3 Inventory Allocation in Global Health Settings 1477.3.1 Inventory Allocation for Disease Prevention 1477.3.2 Inventory Allocation for Treatment of Disease Conditions 1497.4 Capacity Allocation in Global Health Settings 1537.5 Conclusions and Future Directions 155References 1568 Healthcare Supply Chain 159Soo-Haeng Cho and Hui Zhao8.1 Introduction 1598.2 Literature Review 1628.3 Model and Analysis 1648.3.1 Generic Injectable Drug Supply Chain 1648.3.1.1 Model 1668.3.1.2 Analysis 1688.3.2 Influenza Vaccine Supply Chain 1718.3.2.1 Model 1728.3.2.2 Analysis 1738.4 Discussion and Future Research 177Appendix 180Acknowledgment 182References 1829 Organ Transplantation 187Bar𝚤¸s Ata, John J. Friedewald and A. CemRanda9.1 Introduction 1879.2 The Deceased-Donor Organ Allocation system: Stakeholders and Their Objectives 1899.3 Research Opportunities in the Area 1999.3.1 Past Research on the Transplant Candidate’s Problem 1999.3.2 Challenges in Modeling Patient Choice 2019.3.3 Past Research on the Deceased-donor Organ Allocation Policy 2029.3.4 Challenges in Modeling the Deceased-donor Organ Allocation Policy 2069.3.5 Research Problems from the Perspective of Other Stakeholders 2069.4 Concluding Remarks 208References 209Micro-level Thrusts (MiTs)10 Ambulatory Care 217Nan Liu10.1 Introduction 21710.2 How Operations are Managed in Primary Care Practice 21810.3 What Makes Operations Management Difficult in Ambulatory Care 22010.3.1 Competing Objectives 22010.3.2 Environmental Factors 22110.4 Operations Management Models 22210.4.1 System-Wide Planning 22210.4.2 Appointment Template Design 22610.4.3 Managing Patient Flow 23110.5 New Trends in Ambulatory Care 23410.5.1 Online Market 23410.5.2 Telehealth 23510.5.3 Retail Approach of Outpatient Care 23610.6 Conclusion 237References 23711 Inpatient Care 243Van-Anh Truong11.1 Modeling the Inpatient Ward 24411.2 Inpatient Ward Policies 24611.3 Interface with ED 24711.4 Interface with Elective Surgeries 24811.5 Discharge Planning 25011.6 Incentive, Behavioral, and Organizational Issues 25111.7 Future Directions 25211.7.1 Essential Quantitative Tools 25311.7.2 Resources for Learners 253References 25312 Residential Care 257Nadia Lahrichi, Louis-Martin Rousseau and Willem-Jan van Hoeve12.1 Overview of Home Care Delivery 25712.1.1 Home Care 25812.1.2 Home Healthcare 25812.1.2.1 Temporary Care 25912.1.2.2 Specialized Programs 25912.1.3 Operational Challenges 26012.1.3.1 Discussion of the Planning Horizon 26212.1.3.2 Home Care Planning Problem 26312.2 An Overview of Optimization Technology 26312.2.1 Linear Programming 26312.2.2 Mixed Integer Programming 26412.2.3 Constraint Programming 26512.2.4 Heuristics and Dedicated Methods 26512.2.5 Technology Comparison 26612.2.5.1 Solution Expectations and Solver Capabilities 26612.2.5.2 Development Time and Maintenance 26712.3 Territory Districting 26712.4 Provider-to-Patient Assignment 27012.4.1 Workload Measures 27012.4.2 Workload Balance 27112.4.3 Assignment Models 27212.4.4 Assignment of New Patients 27312.5 Task Scheduling and Routing 27312.6 Perspectives 27612.6.1 Integrated Decision-Making Under a New Business Model 27712.6.2 Home Telemetering Forecasting Adverse Events 27712.6.3 Forecasting the Wound Healing Process 27812.6.4 Adjustment of Capacity and Demand 279References 28013 ConciergeMedicine 287Srinagesh Gavirneni and Vidyadhar G. Kulkarni13.1 Introduction 28713.2 Model Setup 29113.3 Concierge Option—No Abandonment 29313.3.1 A Given Participation Level 𝛼 29413.3.2 How to choose d? 29513.3.2.1 All Customers Are Better Off 29513.3.2.2 Customers Are Better Off on Average 29713.3.3 Optimal Participation Level 29913.4 Concierge Option—Abandonment 30113.4.1 Choosing the Optimal 𝛼 and 𝛽 30313.5 Correlated Service Times and Waiting Costs 30413.6 MDVIP Adoption 30613.6.1 The Data 30713.6.2 AbandonmentModel Applied to MDVIP Data 30813.6.2.1 Modeling Heterogeneous Waiting Costs 30913.6.2.2 Participation in Concierge Medicine 31013.6.2.3 Impact of Concierge Medicine 31013.6.2.4 Choosing the Concierge Participation Level 31213.7 Research Opportunities 313References 316Part II Tools14 Markov Decision Processes 319Alan Scheller-Wolf14.1 Introduction 31914.2 Modeling 32114.3 Types of Results 32514.3.1 Numerical Results 32514.3.2 Analytical Results 32714.3.3 Insights 32814.4 Modifications and Extensions of MDPs 32814.4.1 Imperfect State Information 32814.4.2 Extremely Large or Continuous State Spaces 32914.4.3 Uncertainty about Transition Probabilities 33014.4.4 Constrained Optimization 33114.5 Future Applications 33214.6 Recommendations for Additional Reading 333References 33415 Game Theory and Information Economics 337Tinglong Dai15.1 Introduction 33715.2 Key Concepts 33915.2.1 GameTheory: Key Concepts 33915.2.2 Information Economics: Key Concepts 34015.2.2.1 Nonobservability of Information 34115.2.2.2 Asymmetric Information 34115.3 Summary of Healthcare Applications 34315.3.1 Incentive Design for Healthcare Providers 34415.3.2 Quality-Speed Tradeoff 34515.3.3 Gatekeepers 34615.3.4 Healthcare Supply Chain 34615.3.5 Vaccination 34615.3.6 Organ Transplantation 34715.3.7 Healthcare Network 34715.3.8 Mixed Motives of Healthcare Providers 34715.4 Potential Applications 34815.4.1 Micro-Level applications 34815.4.2 Macro-Level Applications 34915.4.3 Meso-Level Applications 34915.5 Resources for Learners 351References 35116 Queueing Games 355Mustafa Akan16.1 Introduction 35516.1.1 Scope of the Review 35616.2 Basic QueueingModels 35616.2.1 Components of a Queueing System 35616.2.2 Performance Measures 35716.2.3 M/M/1 35816.2.4 M/G/1 35916.2.5 M/M/c 36016.2.6 Priorities 36116.2.6.1 Achievable Region Approach 36316.2.7 Networks of Queues 36416.2.8 Approximations 36416.3 Strategic Queueing 36516.3.1 Waiting as an Equilibrium Device 36616.3.2 Demand Dependent on Service Time 36716.3.3 Physician-Induced Demand 36916.3.4 Joining the Queue 37016.3.4.1 Observable Queue 37016.3.4.2 Unobservable Queue 37116.3.5 Waiting for a Better Match 37316.4 Discussion and Future Research Directions 376References 37617 EconometricMethods 381Diwas KC17.1 Introduction 38117.2 Statistical Modeling 38217.2.1 Statistical Inference 38317.2.2 Biased Estimates 38417.3 The Experimental Ideal and the Search for Exogenous Variation 38617.3.1 Instrumental Variables 38617.3.1.1 Example 1 (IV): Patient Flow through an Intensive Care Unit 38817.3.1.2 Example 2 (IV): Focused Factories 39117.3.2 Difference Estimators 39217.3.3 Fixed Effects Estimators 39417.3.3.1 Examples 3-4 (D-in-D): Process Compliance and Peer Effects of Productivity 39517.4 Structural Estimation 39517.4.1 Example 5: Managing Operating Room Capacity 39617.4.2 Example 6: Patient Choice Modeling 39717.5 Conclusion 399References 40018 Data Science 403Rema Padman18.1 Introduction 40318.1.1 Background 40418.1.2 Methods 40718.1.3 Attribute Selection and Ranking 40818.1.4 Information Gain (IG) Attribute Ranking 40818.1.5 Relief-F Attribute Ranking 40818.1.6 Markov Blanket Feature Selection 40818.1.7 Correlation-Based Feature Selection 40918.1.8 Classification 40918.2 Three Illustrative Examples of Data Science in Healthcare 41018.2.1 Medication Reconciliation 41018.2.2 Dynamic Prediction of Medical Risks 41318.2.3 Practice-Based Clinical Pathway Learning 41618.3 Discussion 41918.3.1 Challenges and Opportunities 41918.3.2 Data Science in Action 42018.3.3 Health Data ScienceWorldwide 42118.4 Conclusions 421References 422Index 429

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 xviiPreface xixGlossary of Terms xxviiAcknowledgments xxxvPart I Thrusts Macro-level Thrusts (MaTs)1 Organizational Structure 1Jay Levine1.1 Introduction to the Healthcare Industry 21.2 Academic Medical Centers 61.3 Community Hospitals and Physicians 161.4 Conclusion 192 Access to Healthcare 21Donald R. Fischer2.1 Introduction 212.2 Goals 272.3 Opportunity for Action 293 Market Design 31Itai Ashlagi3.1 Introduction 313.2 Matching Doctors to Residency Programs 313.2.1 Early Days 313.2.2 A Centralized Market and New Challenges 323.2.3 Puzzles and Theory 333.3 Kidney Exchange 353.3.1 Background 353.3.2 Creating a Thick Marketplace for Kidney Exchange 363.3.3 Dynamic Matching 383.3.4 The Marketplace for Kidney Exchange in the United States 413.3.5 Final Comments on Kidney Exchange 43References 44Meso-level Thrusts (MeTs)4 Competing Interests 51Joel Goh4.1 Introduction 514.2 The Literature on Competing Interests 534.2.1 Evaluation of Pharmaceutical Products 534.2.1.1 Individual Drug Classes 544.2.1.2 Multiple Interventions 554.2.1.3 Review Articles 564.2.2 Physician Ownership 564.2.2.1 Physician Ownership of Ancillary Services 574.2.2.2 Physician Ownership of Ambulatory Surgery Centers 594.2.2.3 Physician Ownership of Speciality Hospitals 604.2.2.4 Physician-Owned Distributors 614.2.3 Medical Reporting 624.2.3.1 DRG Upcoding 634.2.3.2 Non-DRG Upcoding 644.3 Examples 654.3.1 Example 1: Physician Decisions with Competing Interests 664.3.2 Example 2: Evidence of HAI Upcoding 704.4 Summary and FutureWork 72References 735 Quality of Care 79Hummy Song and Senthil Veeraraghavan5.1 Frameworks for Measuring Healthcare Quality 795.1.1 The Donabedian Model 795.1.2 The AHRQ Framework 815.2 Understanding Healthcare Quality: Classification of the ExistingOR/MS Literature 825.2.1 Structure 825.2.2 Process 855.2.3 Outcome 915.2.4 Patient Experience 925.2.5 Access 945.3 Open Areas for Future Research 955.3.1 Understanding Structures and Their Interactions with Processes and Outcomes 955.3.2 Understanding Patient Experiences and Their Interactions with Structure 965.3.3 Understanding Processes andTheir Interactions with Outcomes 975.3.4 Understanding Access to Care 985.4 Conclusions 98Acknowledgments 99References 996 Personalized Medicine 109Turgay Ayer and Qiushi Chen6.1 Introduction 1096.2 Sequential Decision Disease Models with Health Information Updates 1116.2.1 Case Study: POMDP Model for Personalized Breast Cancer Screening 1136.2.2 Case Study: Kalman Filter for Glaucoma Monitoring 1166.2.3 Other Relevant Studies 1186.3 One-Time Decision Disease Models with Risk Stratification 1206.3.1 Case Study: Subtype-Based Treatment for DLBCL 1216.3.2 Other Applications 1246.4 Artificial Intelligence-Based Approaches 1256.4.1 Learning from Existing Health Data 1266.4.2 Learning from Trial and Error 1276.5 Conclusions and Emerging Future Research Directions 128References 1307 Global Health 137Karthik V. Natarajan and Jayashankar M. Swaminathan7.1 Introduction 1377.2 Funding Allocation in Global Health Settings 1397.2.1 Funding Allocation for Disease Prevention 1397.2.2 Funding Allocation for Treatment of Disease Conditions 1437.2.2.1 Service Settings 1437.2.2.2 Product Settings 1467.3 Inventory Allocation in Global Health Settings 1477.3.1 Inventory Allocation for Disease Prevention 1477.3.2 Inventory Allocation for Treatment of Disease Conditions 1497.4 Capacity Allocation in Global Health Settings 1537.5 Conclusions and Future Directions 155References 1568 Healthcare Supply Chain 159Soo-Haeng Cho and Hui Zhao8.1 Introduction 1598.2 Literature Review 1628.3 Model and Analysis 1648.3.1 Generic Injectable Drug Supply Chain 1648.3.1.1 Model 1668.3.1.2 Analysis 1688.3.2 Influenza Vaccine Supply Chain 1718.3.2.1 Model 1728.3.2.2 Analysis 1738.4 Discussion and Future Research 177Appendix 180Acknowledgment 182References 1829 Organ Transplantation 187Bar𝚤¸s Ata, John J. Friedewald and A. CemRanda9.1 Introduction 1879.2 The Deceased-Donor Organ Allocation system: Stakeholders and Their Objectives 1899.3 Research Opportunities in the Area 1999.3.1 Past Research on the Transplant Candidate’s Problem 1999.3.2 Challenges in Modeling Patient Choice 2019.3.3 Past Research on the Deceased-donor Organ Allocation Policy 2029.3.4 Challenges in Modeling the Deceased-donor Organ Allocation Policy 2069.3.5 Research Problems from the Perspective of Other Stakeholders 2069.4 Concluding Remarks 208References 209Micro-level Thrusts (MiTs)10 Ambulatory Care 217Nan Liu10.1 Introduction 21710.2 How Operations are Managed in Primary Care Practice 21810.3 What Makes Operations Management Difficult in Ambulatory Care 22010.3.1 Competing Objectives 22010.3.2 Environmental Factors 22110.4 Operations Management Models 22210.4.1 System-Wide Planning 22210.4.2 Appointment Template Design 22610.4.3 Managing Patient Flow 23110.5 New Trends in Ambulatory Care 23410.5.1 Online Market 23410.5.2 Telehealth 23510.5.3 Retail Approach of Outpatient Care 23610.6 Conclusion 237References 23711 Inpatient Care 243Van-Anh Truong11.1 Modeling the Inpatient Ward 24411.2 Inpatient Ward Policies 24611.3 Interface with ED 24711.4 Interface with Elective Surgeries 24811.5 Discharge Planning 25011.6 Incentive, Behavioral, and Organizational Issues 25111.7 Future Directions 25211.7.1 Essential Quantitative Tools 25311.7.2 Resources for Learners 253References 25312 Residential Care 257Nadia Lahrichi, Louis-Martin Rousseau and Willem-Jan van Hoeve12.1 Overview of Home Care Delivery 25712.1.1 Home Care 25812.1.2 Home Healthcare 25812.1.2.1 Temporary Care 25912.1.2.2 Specialized Programs 25912.1.3 Operational Challenges 26012.1.3.1 Discussion of the Planning Horizon 26212.1.3.2 Home Care Planning Problem 26312.2 An Overview of Optimization Technology 26312.2.1 Linear Programming 26312.2.2 Mixed Integer Programming 26412.2.3 Constraint Programming 26512.2.4 Heuristics and Dedicated Methods 26512.2.5 Technology Comparison 26612.2.5.1 Solution Expectations and Solver Capabilities 26612.2.5.2 Development Time and Maintenance 26712.3 Territory Districting 26712.4 Provider-to-Patient Assignment 27012.4.1 Workload Measures 27012.4.2 Workload Balance 27112.4.3 Assignment Models 27212.4.4 Assignment of New Patients 27312.5 Task Scheduling and Routing 27312.6 Perspectives 27612.6.1 Integrated Decision-Making Under a New Business Model 27712.6.2 Home Telemetering Forecasting Adverse Events 27712.6.3 Forecasting the Wound Healing Process 27812.6.4 Adjustment of Capacity and Demand 279References 28013 ConciergeMedicine 287Srinagesh Gavirneni and Vidyadhar G. Kulkarni13.1 Introduction 28713.2 Model Setup 29113.3 Concierge Option—No Abandonment 29313.3.1 A Given Participation Level 𝛼 29413.3.2 How to choose d? 29513.3.2.1 All Customers Are Better Off 29513.3.2.2 Customers Are Better Off on Average 29713.3.3 Optimal Participation Level 29913.4 Concierge Option—Abandonment 30113.4.1 Choosing the Optimal 𝛼 and 𝛽 30313.5 Correlated Service Times and Waiting Costs 30413.6 MDVIP Adoption 30613.6.1 The Data 30713.6.2 AbandonmentModel Applied to MDVIP Data 30813.6.2.1 Modeling Heterogeneous Waiting Costs 30913.6.2.2 Participation in Concierge Medicine 31013.6.2.3 Impact of Concierge Medicine 31013.6.2.4 Choosing the Concierge Participation Level 31213.7 Research Opportunities 313References 316Part II Tools14 Markov Decision Processes 319Alan Scheller-Wolf14.1 Introduction 31914.2 Modeling 32114.3 Types of Results 32514.3.1 Numerical Results 32514.3.2 Analytical Results 32714.3.3 Insights 32814.4 Modifications and Extensions of MDPs 32814.4.1 Imperfect State Information 32814.4.2 Extremely Large or Continuous State Spaces 32914.4.3 Uncertainty about Transition Probabilities 33014.4.4 Constrained Optimization 33114.5 Future Applications 33214.6 Recommendations for Additional Reading 333References 33415 Game Theory and Information Economics 337Tinglong Dai15.1 Introduction 33715.2 Key Concepts 33915.2.1 GameTheory: Key Concepts 33915.2.2 Information Economics: Key Concepts 34015.2.2.1 Nonobservability of Information 34115.2.2.2 Asymmetric Information 34115.3 Summary of Healthcare Applications 34315.3.1 Incentive Design for Healthcare Providers 34415.3.2 Quality-Speed Tradeoff 34515.3.3 Gatekeepers 34615.3.4 Healthcare Supply Chain 34615.3.5 Vaccination 34615.3.6 Organ Transplantation 34715.3.7 Healthcare Network 34715.3.8 Mixed Motives of Healthcare Providers 34715.4 Potential Applications 34815.4.1 Micro-Level applications 34815.4.2 Macro-Level Applications 34915.4.3 Meso-Level Applications 34915.5 Resources for Learners 351References 35116 Queueing Games 355Mustafa Akan16.1 Introduction 35516.1.1 Scope of the Review 35616.2 Basic QueueingModels 35616.2.1 Components of a Queueing System 35616.2.2 Performance Measures 35716.2.3 M/M/1 35816.2.4 M/G/1 35916.2.5 M/M/c 36016.2.6 Priorities 36116.2.6.1 Achievable Region Approach 36316.2.7 Networks of Queues 36416.2.8 Approximations 36416.3 Strategic Queueing 36516.3.1 Waiting as an Equilibrium Device 36616.3.2 Demand Dependent on Service Time 36716.3.3 Physician-Induced Demand 36916.3.4 Joining the Queue 37016.3.4.1 Observable Queue 37016.3.4.2 Unobservable Queue 37116.3.5 Waiting for a Better Match 37316.4 Discussion and Future Research Directions 376References 37617 EconometricMethods 381Diwas KC17.1 Introduction 38117.2 Statistical Modeling 38217.2.1 Statistical Inference 38317.2.2 Biased Estimates 38417.3 The Experimental Ideal and the Search for Exogenous Variation 38617.3.1 Instrumental Variables 38617.3.1.1 Example 1 (IV): Patient Flow through an Intensive Care Unit 38817.3.1.2 Example 2 (IV): Focused Factories 39117.3.2 Difference Estimators 39217.3.3 Fixed Effects Estimators 39417.3.3.1 Examples 3-4 (D-in-D): Process Compliance and Peer Effects of Productivity 39517.4 Structural Estimation 39517.4.1 Example 5: Managing Operating Room Capacity 39617.4.2 Example 6: Patient Choice Modeling 39717.5 Conclusion 399References 40018 Data Science 403Rema Padman18.1 Introduction 40318.1.1 Background 40418.1.2 Methods 40718.1.3 Attribute Selection and Ranking 40818.1.4 Information Gain (IG) Attribute Ranking 40818.1.5 Relief-F Attribute Ranking 40818.1.6 Markov Blanket Feature Selection 40818.1.7 Correlation-Based Feature Selection 40918.1.8 Classification 40918.2 Three Illustrative Examples of Data Science in Healthcare 41018.2.1 Medication Reconciliation 41018.2.2 Dynamic Prediction of Medical Risks 41318.2.3 Practice-Based Clinical Pathway Learning 41618.3 Discussion 41918.3.1 Challenges and Opportunities 41918.3.2 Data Science in Action 42018.3.3 Health Data ScienceWorldwide 42118.4 Conclusions 421References 422Index 429