Details

Handbook of Healthcare Analytics


Handbook of Healthcare Analytics

Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations
Wiley Series in Operations Research and Management Science 1. Aufl.

von: Tinglong Dai, Sridhar Tayur

103,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 30.07.2018
ISBN/EAN: 9781119300960
Sprache: englisch
Anzahl Seiten: 480

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Beschreibungen

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, precision 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 analytics—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.
List of Contributors xvii Preface xix Glossary of Terms xxvii Acknowledgments xxxv Part I Thrusts Macro-level Thrusts (MaTs) 1 Organizational Structure 1Jay 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 21Donald R. Fischer 2.1 Introduction 21 2.2 Goals 27 2.3 Opportunity for Action 29 3 Market Design 31Itai 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 andTheory 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 51Joel 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 79Hummy 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 andTheir 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 109Turgay 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 137Karthik 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 159Soo-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 187Bar??¸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 217Nan 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 ManagementModels 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 243Van-Anh Truong 11.1 Modeling the InpatientWard 244 11.2 InpatientWard 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 257Nadia Lahrichi, Louis-Martin Rousseau andWillem-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 theWound Healing Process 278 12.6.4 Adjustment of Capacity and Demand 279 References 280 13 ConciergeMedicine 287Srinagesh 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 ?? 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 ?? and ?? 303 13.5 Correlated Service Times andWaiting 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 HeterogeneousWaiting 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 319Alan 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 337Tinglong 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 355Mustafa 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 381Diwas KC 17.1 Introduction 381 17.2 StatisticalModeling 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 403Rema 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
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.
"Healthcare markets are in need of redesign. This timely Handbook showcases what analytics have to offer." —Alvin E. Roth, Nobel Laureate in Economics Craig and Susan McCaw Professor of Economics at Stanford University 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 society 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.

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