Healthcare Management Engineering: What Does This Fancy Term Really Mean? (eBook)
XIX, 121 Seiten
Springer New York (Verlag)
978-1-4614-2068-2 (ISBN)
This Briefs Series book illustrates in depth a concept of healthcare management engineering and its domain for hospital and clinic operations. Predictive and analytic decision-making power of management engineering methodology is systematically compared to traditional management reasoning by applying both side by side to analyze 26 concrete operational management problems adapted from hospital and clinic practice. The problem types include: clinic, bed and operating rooms capacity; patient flow; staffing and scheduling; resource allocation and optimization; forecasting of patient volumes and seasonal variability; business intelligence and data mining; and game theory application for allocating cost savings between cooperating providers.
Detailed examples of applications are provided for quantitative methods such as discrete event simulation, queuing analytic theory, linear and probabilistic optimization, forecasting of a time series, principal component decomposition of a data set and cluster analysis, and the Shapley value for fair gain sharing between cooperating participants. A summary of some fundamental management engineering principles is provided.
The goal of the book is to help to bridge the gap in mutual understanding and communication between management engineering professionals and hospital and clinic administrators.
The book is intended primarily for hospital/clinic leadership who are in charge of making managerial decisions. This book can also serve as a compendium of introductory problems/projects for graduate students in Healthcare Management and Administration, as well as for MBA programs with an emphasis in Healthcare.
This Briefs Series book illustrates in depth a concept of healthcare management engineering and its domain for hospital and clinic operations. Predictive and analytic decision-making power of management engineering methodology is systematically compared to traditional management reasoning by applying both side by side to analyze 26 concrete operational management problems adapted from hospital and clinic practice. The problem types include: clinic, bed and operating rooms capacity; patient flow; staffing and scheduling; resource allocation and optimization; forecasting of patient volumes and seasonal variability; business intelligence and data mining; and game theory application for allocating cost savings between cooperating providers. Detailed examples of applications are provided for quantitative methods such as discrete event simulation, queuing analytic theory, linear and probabilistic optimization, forecasting of a time series, principal component decomposition of a data set and cluster analysis, and the Shapley value for fair gain sharing between cooperating participants. A summary of some fundamental management engineering principles is provided. The goal of the book is to help to bridge the gap in mutual understanding and communication between management engineering professionals and hospital and clinic administrators. The book is intended primarily for hospital/clinic leadership who are in charge of making managerial decisions. This book can also serve as a compendium of introductory problems/projects for graduate students in Healthcare Management and Administration, as well as for MBA programs with an emphasis in Healthcare.
Healthcare Management Engineering: What Does This Fancy Term Really Mean? 3
Preface 7
What Is This Book About? 7
Who Is This Book For? 12
Acknowledgments 15
Contents 17
Chapter 1: Traditional Management and Management Engineering 21
Chapter 2: Dynamic Supply and Demand Balance Problems 23
2.1 Discrete Event Simulation Methodology: What Is a Discrete Event Simulation Model and How Does a Simple Model Work? 23
2.2 Queuing Analytic Theory: Its Use and Limitations 25
2.3 Capacity Problems 28
2.3.1 Outpatient Clinic: Centralized or Separate Locations? 28
2.3.1.1 Traditional Management Approach 28
2.3.1.2 Queuing Analytic Theory Application 28
2.3.1.3 Discrete Event Simulation Application 30
2.3.2 Outpatient Clinic: Nonsteady-State Operations 31
2.3.2.1 Traditional Management Approach 31
2.3.2.2 Queuing Analytic Theory Application 31
2.3.2.3 Discrete Event Simulation Application 32
2.3.3 Outpatient Clinic: Limited Queue Size with Leaving “Inpatient” Patients 33
2.3.3.1 Discrete Event Simulation Application 33
2.3.4 Outpatient Clinic: Time-Varying Arrival Rates 35
2.3.4.1 Traditional Approach 35
2.3.4.2 Queuing Analytic Theory Application 35
2.3.4.3 Discrete Event Simulation Application 36
2.3.5 “Excessive” ICU Capacity, “Improved” Efficiency, and Access to Care 37
2.3.5.1 Traditional Management Approach 37
2.3.5.2 Queuing Analytic Approach 37
2.3.5.3 Management Engineering Approach 38
2.3.6 Mixed Patient Arrival Patterns: Simultaneous Random and Scheduled Arrivals 39
2.3.6.1 Traditional Management Approach 40
2.3.6.2 Queuing Analytic Approach 40
2.3.6.3 Management Engineering Approach 40
2.3.7 Small Rural Hospital vs. Large Community Hospital: Does Size Affect Operational Efficiency? 41
2.3.7.1 Traditional Management Approach 42
2.3.7.2 Management Engineering Approach 42
2.3.8 Daily Load-Leveling (Smoothing) of Scheduled Elective Procedures 43
2.3.8.1 Traditional Management Approach 44
2.3.8.2 Queuing Analytic Approach 44
2.3.8.3 Management Engineering Approach 44
2.3.9 Separate or Interchangeable (Shared) Operating Rooms for Emergency and Scheduled Surgeries: Which Arrangement Is More Efficient? 46
2.3.9.1 Traditional Management 46
2.3.9.2 Queuing Analytic Approach 47
2.3.9.3 Management Engineering Approach 47
2.3.10 Surgical Capacity of Special Procedure Operating Rooms 51
2.3.10.1 Traditional Management 51
2.3.10.2 Management Engineering Approach 52
2.3.11 The Entire Hospital System Patient Flow: Effect of Interdependency of ED, ICU, OR, and Regular Nursing Units on System Throughput 53
2.3.11.1 Traditional Management 55
2.3.11.2 Management Engineering Approach 55
2.4 Scheduling and Staffing Problems 59
2.4.1 Scheduling Order for Appointments with Different Duration Variability 59
2.4.1.1 Traditional Management Approach 60
2.4.1.2 Management Engineering Approach 60
2.4.2 Centralized Discharge vs. Individual Units Discharges 61
2.4.2.1 Traditional Management Approach 62
2.4.2.2 Management Engineering Approach 62
2.4.3 Staffing of Hospital Receiving Center 64
2.4.3.1 Traditional Management Approach (Langabeer 2007) 64
2.4.3.2 Management Engineering Approach 65
2.4.4 Staffing of the Unit with Cross-trained Staff 66
2.4.4.1 Traditional Management Approach 67
2.4.4.2 Management Engineering Approach 67
2.4.5 Outpatient Clinic Costs and Staffing: Is Right Staff Used at the Right Time? 69
2.4.5.1 Traditional Management Approach 70
2.4.5.2 Management Engineering Approach 70
Chapter 3: Linear and Probabilistic Resource Optimization and Allocation Problems 73
3.1 Optimization of Patient Service Volumes: Keep or Drop a Service Line? 73
3.1.1 Traditional Approach 74
3.1.2 Management Engineering Approach 74
3.2 Optimization of Clinical Unit Staffing for 24/7 Three-Shift Operations: Is Staffing Cost Minimized? 77
3.2.1 Traditional Approach 77
3.2.2 Management Engineering Approach 77
3.3 Resident Physician Restricted Work Hours: Optimal Scheduling to Meet the Institute of Medicine New Workload Recommendations 81
3.3.1 Traditional Management Approach 82
3.3.2 Management Engineering Approach 82
3.3.3 Day Time Scheduling 82
3.3.4 Night Time Scheduling 86
3.4 Optimized Pooled Screening Testing 89
3.4.1 Traditional Management Approach 92
3.4.2 Management Engineering Approach 92
3.5 Projected Number of Patients Discharged from ED 93
3.5.1 Traditional Management Approach 94
3.5.2 Management Engineering Approach 94
Chapter 4: Forecasting Time Series 98
4.1 Forecasting Patient Volumes Using Time Series Data Analysis 98
4.1.1 Traditional Management Approach 99
4.1.2 The Number of Past Data Points that Have to be Used for Making a Forecast 99
4.1.3 Validation of Some Typical Forecasting Models 102
4.1.4 Management Engineering Approach 102
4.2 Forecasting Time Series with Seasonal Variation 105
4.2.1 Traditional Management Approach 106
4.2.2 Management Engineering Approach 108
Chapter 5: Business Intelligence and Data Mining 110
5.1 Multivariate Database Analysis: What Population Demographic Factors Are the Biggest Contributors to Hospital Contribution Margin? 111
5.1.1 Traditional Management Approach 111
5.1.2 Management Engineering Approach 112
5.2 Cluster Analysis: Which Zip Codes Form Distinct Contribution Margin Groups? 116
5.2.1 Traditional Management Approach 117
5.2.2 Management Engineering Approach 117
Chapter 6: The Use of Game Theory 121
6.1 Is Distributing of Savings Between Cooperating Providers Fair? The Use of the Shapley Value Concept 122
6.1.1 Traditional Management Approach 122
6.1.2 Management Engineering Approach 123
Chapter 7: Summary of Some Fundamental Management Engineering Principles 126
Chapter 8: Concluding Remarks 129
Appendix 131
References 133
| Erscheint lt. Verlag | 2.12.2011 |
|---|---|
| Reihe/Serie | SpringerBriefs in Health Care Management and Economics |
| Zusatzinfo | XIX, 121 p. 26 illus., 20 illus. in color. |
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Medizin / Pharmazie ► Allgemeines / Lexika |
| Medizin / Pharmazie ► Gesundheitswesen | |
| Medizin / Pharmazie ► Pflege | |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik ► Bauwesen | |
| Technik ► Medizintechnik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | delivery of care • Healthcare • healthcare engineering • Management • process model simulation • Process Optimization |
| ISBN-10 | 1-4614-2068-7 / 1461420687 |
| ISBN-13 | 978-1-4614-2068-2 / 9781461420682 |
| Haben Sie eine Frage zum Produkt? |
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