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E-grāmata: Handbook of Healthcare Analytics - Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations [Wiley Online]

Edited by (Johns Hopkins University), Edited by (Carnegie Mellon University)
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Handbook of Healthcare Analytics - Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations: Theoretical Minimum for Conducting 21st Century Research on Healthcare OperationsPalielināt
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"This handbook provides a broad healthcare context for operational research/management science (OR/MS) researchers with an encyclopedic account of the most vexing international healthcare issues. In addition, the handbook features a practical guide for OR/MS researchers to learn the most important quantitative research tools in conducting healthcare research, including classical OR techniques enhanced with game theory (such as queuing games); classical economics methods enhanced by operational considerations (like matching markets); econometrics; and data-science methods (from statistics and machine learning)"--

"This handbook provides a broad healthcare context for operational research/management science (OR/MS) researchers with an encyclopedic account of the most vexing international healthcare issues.  In addition, the handbook features a practical guidefor OR/MS researchers to learn the most important quantitative research tools in conducting healthcare research, including classical OR techniques enhanced with game theory (such as queuing games); classical economics methods enhanced by operational considerations (like matching markets); econometrics; and data-science methods (from statistics and machine learning). Over the past decade, a lively discussion on healthcare has touched virtually every stakeholder with the system, and three key issues have emerged from this discussion: cost, quality, and access, which are jointly referred to as the "iron triangle" of healthcare. There is an urgent need to study these three "big issues", and OR/MS researchers can contribute to this need given that so much hasbeen done in analyzing and solving supply-demand mismatch problems of virtually any scale. This book fills a current gap in the healthcare operations management literature by focusing on the incentives issues in healthcare operations from an operations management. This focus on operations-level modeling is unique and needed since the current focus has been on applications of operations research techniques to specific healthcare scenarios, such as nurse scheduling, appointment scheduling, facility design,and patient flow management. Topical coverage includes: operations research tools with healthcare applications; economics tools with healthcare applications; econometrics tools with heathcare applications; data science tools with healthcare applications;healthcare analytics for patients; healthcare analytics for policy-makers; healthcare analytics for hospitals; healthcare analytics for clinicians; healthcare analytics for global health; healthcare operations for patient outcomes; changing faces of healthcare systems; data science opportunities and emerging techniques; and quantitative teaching cases"--

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
1(20)
Jay Levine
1.1 Introduction to the Healthcare Industry
2(4)
1.2 Academic Medical Centers
6(10)
1.3 Community Hospitals and Physicians
16(3)
1.4 Conclusion
19(2)
2 Access to Healthcare
21(10)
Donald R. Fischer
2.1 Introduction
21(6)
2.2 Goals
27(2)
2.3 Opportunity for Action
29(2)
3 Market Design
31(20)
Itai Ashlagi
3.1 Introduction
31(1)
3.2 Matching Doctors to Residency Programs
31(1)
3.2.1 Early Days
31(1)
3.2.2 A Centralized Market and New Challenges
32(1)
3.2.3 Puzzles and Theory
33(2)
3.3 Kidney Exchange
35(1)
3.3.1 Background
35(1)
3.3.2 Creating a Thick Marketplace for Kidney Exchange
36(1)
3.3.3 Dynamic Matching
38(1)
3.3.4 The Marketplace for Kidney Exchange in the United States
41(1)
3.3.5 Final Comments on Kidney Exchange
43(1)
References
44(7)
Meso-level Thrusts (MeTs)
4 Competing Interests
51(28)
Joel Goh
4.1 Introduction
51(2)
4.2 The Literature on Competing Interests
53(1)
4.2.1 Evaluation of Pharmaceutical Products
53(1)
4.2.1.1 Individual Drug Classes
54(1)
4.2.1.2 Multiple Interventions
55(1)
4.2.1.3 Review Articles
56(1)
4.2.2 Physician Ownership
56(1)
4.2.2.1 Physician Ownership of Ancillary Services
57(1)
4.2.2.2 Physician Ownership of Ambulatory Surgery Centers
59(1)
4.2.2.3 Physician Ownership of Speciality Hospitals
60(1)
4.2.2.4 Physician-Owned Distributors
61(1)
4.2.3 Medical Reporting
62(1)
4.2.3.1 DRG Upcoding
63(1)
4.2.3.2 Non-DRG Upcoding
64(1)
4.3 Examples
65(1)
4.3.1 Example 1: Physician Decisions with Competing Interests
66(1)
4.3.2 Example 2: Evidence of HAI Upcoding
70(2)
4.4 Summary and Future Work
72(1)
References
73(6)
5 Quality of Care
79(30)
Hummy Song
Senthil Veeraraghavan
5.1 Frameworks for Measuring Healthcare Quality
79(1)
5.1.1 The Donabedian Model
79(1)
5.1.2 The AHRQ Framework
81(1)
5.2 Understanding Healthcare Quality: Classification of the Existing OR/MS Literature
82(1)
5.2.1 Structure
82(1)
5.2.2 Process
85(1)
5.2.3 Outcome
91(1)
5.2.4 Patient Experience
92(1)
5.2.5 Access
94(1)
5.3 Open Areas for Future Research
95(1)
5.3.1 Understanding Structures and Their Interactions with Processes and Outcomes
95(1)
5.3.2 Understanding Patient Experiences and Their Interactions with Structure
96(1)
5.3.3 Understanding Processes and Their Interactions with Outcomes
97(1)
5.3.4 Understanding Access to Care
98(1)
5.4 Conclusions
98(1)
Acknowledgments
99(1)
References
99(10)
6 Personalized Medicine
109(28)
Turgay Ayer
Quishi Chen
6.1 Introduction
109(2)
6.2 Sequential Decision Disease Models with Health Information Updates
111(1)
6.2.1 Case Study: POMDP Model for Personalized Breast Cancer Screening
113(1)
6.2.2 Case Study: Kalman Filter for Glaucoma Monitoring
116(1)
6.2.3 Other Relevant Studies
118(2)
6.3 One-Time Decision Disease Models with Risk Stratification
120(1)
6.3.1 Case Study: Subtype-Based Treatment for DLBCL
121(1)
6.3.2 Other Applications
124(1)
6.4 Artificial Intelligence-Based Approaches
125(1)
6.4.1 Learning from Existing Health Data
126(1)
6.4.2 Learning from Trial and Error
127(1)
6.5 Conclusions and Emerging Future Research Directions
128(2)
References
130(7)
7 Global Health
137(22)
Karthik V. Natarajan
Jayashankar M. Swaminathan
7.1 Introduction
137(2)
7.2 Funding Allocation in Global Health Settings
139(1)
7.2.1 Funding Allocation for Disease Prevention
139(1)
7.2.2 Funding Allocation for Treatment of Disease Conditions
143(1)
7.2.2.1 Service Settings
143(1)
7.2.2.2 Product Settings
146(1)
7.3 Inventory Allocation in Global Health Settings
147(1)
7.3.1 Inventory Allocation for Disease Prevention
147(1)
7.3.2 Inventory Allocation for Treatment of Disease Conditions
149(4)
7.4 Capacity Allocation in Global Health Settings
153(2)
7.5 Conclusions and Future Directions
155(1)
References
156(3)
8 Healthcare Supply Chain
159(28)
Soo-Haeng Cho
Hui Zhao
8.1 Introduction
159(3)
8.2 Literature Review
162(2)
8.3 Model and Analysis
164(1)
8.3.1 Generic Injectable Drug Supply Chain
164(1)
8.3.1.1 Model
166(1)
8.3.1.2 Analysis
168(3)
8.3.2 Influenza Vaccine Supply Chain
171(1)
8.3.2.1 Model
172(1)
8.3.2.2 Analysis
173(4)
8.4 Discussion and Future Research
177(3)
Appendix
180(2)
Acknowledgment
182(1)
References
182(5)
9 Organ Transplantation
187(30)
Baris Ata
John J. Friedewald
A. Cem Randa
9.1 Introduction
187(2)
9.2 The Deceased-Donor Organ Allocation system: Stakeholders and Their Objectives
189(10)
9.3 Research Opportunities in the Area
199(1)
9.3.1 Past Research on the Transplant Candidate's Problem
199(1)
9.3.2 Challenges in Modeling Patient Choice
201(1)
9.3.3 Past Research on the Deceased-donor Organ Allocation Policy
202(1)
9.3.4 Challenges in Modeling the Deceased-donor Organ Allocation Policy
206(1)
9.3.5 Research Problems from the Perspective of Other Stakeholders
206(2)
9.4 Concluding Remarks
208(1)
References
209(8)
Micro-level Thrusts (MiTs)
10 Ambulatory Care
217(26)
Nan Liu
10.1 Introduction
217(1)
10.2 How Operations are Managed in Primary Care Practice
218(1)
10.3 What Makes Operations Management Difficult in Ambulatory Care
220(1)
10.3.1 Competing Objectives
220(1)
10.3.2 Environmental Factors
221(1)
10.4 Operations Management Models
222(1)
10.4.1 System-Wide Planning
222(1)
10.4.2 Appointment Template Design
226(1)
10.4.3 Managing Patient Flow
231(3)
10.5 New Trends in Ambulatory Care
234(1)
10.5.1 Online Market
234(1)
10.5.2 Telehealth
235(1)
10.5.3 Retail Approach of Outpatient Care
236(1)
10.6 Conclusion
237(1)
References
237(6)
11 Inpatient Care
243(14)
Van-Anh Truong
11.1 Modeling the Inpatient Ward
244(1)
11.2 Inpatient Ward Policies
246(1)
11.3 Interface with ED
247(1)
11.4 Interface with Elective Surgeries
248(1)
11.5 Discharge Planning
250(1)
11.6 Incentive, Behavioral, and Organizational Issues
251(1)
11.7 Future Directions
252(1)
11.7.1 Essential Quantitative Tools
253(1)
11.7.2 Resources for Learners
253(1)
References
253(4)
12 Residential Care
257(30)
Nadia Lahrichi
Louis-Martin Rousseau
Willem-Jan van Hoeve
12.1 Overview of Home Care Delivery
257(1)
12.1.1 Home Care
258(1)
12.1.2 Home Healthcare
258(1)
12.1.2.1 Temporary Care
259(1)
12.1.2.2 Specialized Programs
259(1)
12.1.3 Operational Challenges
260(1)
12.1.3.1 Discussion of the Planning Horizon
262(1)
12.1.3.2 Home Care Planning Problem
263(1)
12.2 An Overview of Optimization Technology
263(1)
12.2.1 Linear Programming
263(1)
12.2.2 Mixed Integer Programming
264(1)
12.2.3 Constraint Programming
265(1)
12.2.4 Heuristics and Dedicated Methods
265(1)
12.2.5 Technology Comparison
266(1)
12.2.5.1 Solution Expectations and Solver Capabilities
266(1)
12.2.5.2 Development Time and Maintenance
267(1)
12.3 Territory Districting
267(1)
12.4 Provider-to-Patient Assignment
270(1)
12.4.1 Workload Measures
270(1)
12.4.2 Workload Balance
271(1)
12.4.3 Assignment Models
272(1)
12.4.4 Assignment of New Patients
273(1)
12.5 Task Scheduling and Routing
273(1)
12.6 Perspectives
276(1)
12.6.1 Integrated Decision-Making Under a New Business Model
277(1)
12.6.2 Home Telemetering Forecasting Adverse Events
277(1)
12.6.3 Forecasting the Wound Healing Process
278(1)
12.6.4 Adjustment of Capacity and Demand
279(1)
References
280(7)
13 Concierge Medicine
287(32)
Srinagesh Gavirneni
Vidyadhar G. Kulkarni
13.1 Introduction
287(1)
13.2 Model Setup
291(1)
13.3 Concierge Option-No Abandonment
293(1)
13.3.1 A Given Participation Level α
294(1)
13.3.2 How to choose d?
295(1)
13.3.2.1 All Customers Are Better Off
295(1)
13.3.2.2 Customers Are Better Off on Average
297(2)
13.3.3 Optimal Participation Level
299(2)
13.4 Concierge Option-Abandonment
301(1)
13.4.1 Choosing the Optimal α and β
;303
13.5 Correlated Service Times and Waiting Costs
304(1)
13.6 MDVIP Adoption
306(1)
13.6.1 The Data
307(1)
13.6.2 Abandonment Model Applied to MDVIP Data
308(1)
13.6.2.1 Modeling Heterogeneous Waiting Costs
309(1)
13.6.2.2 Participation in Concierge Medicine
310(1)
13.6.2.3 Impact of Concierge Medicine
310(1)
13.6.2.4 Choosing the Concierge Participation Level
312(1)
13.7 Research Opportunities
313(1)
References
316(3)
Part II Tools
14 Markov Decision Processes
319(18)
Alan Scheller-Wolf
14.1 Introduction
319(2)
14.2 Modeling
321(4)
14.3 Types of Results
325(3)
14.3.1 Numerical Results
325(2)
14.3.2 Analytical Results
327(1)
14.3.3 Insights
328(1)
14.4 Modifications and Extensions of MDPs
328(4)
14.4.1 Imperfect State Information
328(1)
14.4.2 Extremely Large or Continuous State Spaces
329(1)
14.4.3 Uncertainty about Transition Probabilities
330(1)
14.4.4 Constrained Optimization
331(1)
14.5 Future Applications
332(1)
14.6 Recommendations for Additional Reading
333(1)
References
334(3)
15 Game Theory and Information Economics
337(18)
Tinglong Dal
15.1 Introduction
337(2)
15.2 Key Concepts
339(4)
15.2.1 Game Theory: Key Concepts
339(1)
15.2.2 Information Economics: Key Concepts
340(1)
15.2.2.1 Nonobservability of Information
341(1)
15.2.2.2 Asymmetric Information
341(2)
15.3 Summary of Healthcare Applications
343(5)
15.3.1 Incentive Design for Healthcare Providers
344(1)
15.3.2 Quality-Speed Tradeoff
345(1)
15.3.3 Gatekeepers
346(1)
15.3.4 Healthcare Supply Chain
346(1)
15.3.5 Vaccination
346(1)
15.3.6 Organ Transplantation
347(1)
15.3.7 Healthcare Network
347(1)
15.3.8 Mixed Motives of Healthcare Providers
347(1)
15.4 Potential Applications
348(3)
15.4.1 Micro-Level applications
348(1)
15.4.2 Macro-Level Applications
349(1)
15.4.3 Meso-Level Applications
349(2)
15.5 Resources for Learners
351(1)
References
351(4)
16 Queueing Games
355(26)
Mustafa Akan
16.1 Introduction
355(1)
16.1.1 Scope of the Review
356(1)
16.2 Basic Queueing Models
356(9)
16.2.1 Components of a Queueing System
356(1)
16.2.2 Performance Measures
357(1)
16.2.3 M/M/1
358(1)
16.2.4 M/G/1
359(1)
16.2.5 M/M/c
360(1)
16.2.6 Priorities
361(1)
16.2.6.1 Achievable Region Approach
363(1)
16.2.7 Networks of Queues
364(1)
16.2.8 Approximations
364(1)
16.3 Strategic Queueing
365(11)
16.3.1 Waiting as an Equilibrium Device
366(1)
16.3.2 Demand Dependent on Service Time
367(2)
16.3.3 Physician-Induced Demand
369(1)
16.3.4 Joining the Queue
370(1)
16.3.4.1 Observable Queue
370(1)
16.3.4.2 Unobservable Queue
371(2)
16.3.5 Waiting for a Better Match
373(3)
16.4 Discussion and Future Research Directions
376(1)
References
376(5)
17 Econometric Methods
381(22)
Diwas K.C.
17.1 Introduction
381(1)
17.2 Statistical Modeling
382(4)
17.2.1 Statistical Inference
383(1)
17.2.2 Biased Estimates
384(2)
17.3 The Experimental Ideal and the Search for Exogenous Variation
386(9)
17.3.1 Instrumental Variables
386(1)
17.3.1.1 Example 1 (IV): Patient Flow through an Intensive Care Unit
388(1)
17.3.1.2 Example 2 (IV): Focused Factories
391(1)
17.3.2 Difference Estimators
392(2)
17.3.3 Fixed Effects Estimators
394(1)
17.3.3.1 Examples 3-4 (D-in-D): Process Compliance and Peer Effects of Productivity
395(1)
17.4 Structural Estimation
395(4)
17.4.1 Example 5: Managing Operating Room Capacity
396(1)
17.4.2 Example 6: Patient Choice Modeling
397(2)
17.5 Conclusion
399(1)
References
400(3)
18 Data Science
403(26)
Rema Padman
18.1 Introduction
403(7)
18.1.1 Background
404(3)
18.1.2 Methods
407(1)
18.1.3 Attribute Selection and Ranking
408(1)
18.1.4 Information Gain (IG) Attribute Ranking
408(1)
18.1.5 Relief-F Attribute Ranking
408(1)
18.1.6 Markov Blanket Feature Selection
408(1)
18.1.7 Correlation-Based Feature Selection
409(1)
18.1.8 Classification
409(1)
18.2 Three Illustrative Examples of Data Science in Healthcare
410(9)
18.2.1 Medication Reconciliation
410(3)
18.2.2 Dynamic Prediction of Medical Risks
413(3)
18.2.3 Practice-Based Clinical Pathway Learning
416(3)
18.3 Discussion
419(3)
18.3.1 Challenges and Opportunities
419(1)
18.3.2 Data Science in Action
420(1)
18.3.3 Health Data Science Worldwide
421(1)
18.4 Conclusions
421(1)
References
422(7)
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.
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