BOOK
Stochastic Modeling And Analytics In Healthcare Delivery Systems
Li Jingshan | Kong Nan | Xie Xiaolei
(2017)
Additional Information
Book Details
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Contents | xi | ||
| Preface | vii | ||
| Chapter 1 Patient Appointment Scheduling | 1 | ||
| Abstract | 1 | ||
| 1.1. Introduction | 2 | ||
| 1.2. Appointment Scheduling in Outpatient Clinics | 3 | ||
| 1.2.1. Appointment scheduling in outpatient clinics with simulation | 5 | ||
| 1.3. Surgery Scheduling | 11 | ||
| 1.3.1. Methodologies | 11 | ||
| 1.3.2. Surgery scheduling with SAA method | 14 | ||
| 1.4. Summary | 22 | ||
| References | 26 | ||
| Chapter 2 A Simulation Model of French Emergency Medical Service | 31 | ||
| Abstract | 31 | ||
| 2.1. Introduction | 32 | ||
| 2.2. The Use of Simulation in the EMS Literature | 34 | ||
| 2.3. The DES Model of SAMU 94 | 36 | ||
| 2.3.1. The SAMU 94 process description | 36 | ||
| 2.3.2. Data collection and analysis | 40 | ||
| 2.3.3. DES model design | 42 | ||
| 2.3.4. DES model validation | 45 | ||
| 2.4. Analysis of DES Model Results | 45 | ||
| 2.4.1. Simulation strategy design | 45 | ||
| 2.4.2. Simulation results | 48 | ||
| 2.5. Conclusions and Perspectives | 51 | ||
| References | 52 | ||
| Chapter 3 Modeling and Simulation of the Emergency Department of an Italian Hospital\r | 57 | ||
| Abstract | 57 | ||
| 3.1. Introduction | 58 | ||
| 3.2. Literature Review | 59 | ||
| 3.3. Problem Description | 61 | ||
| 3.3.1. Stakeholders | 61 | ||
| 3.3.2. The context | 61 | ||
| 3.3.3. Process of ED activities | 62 | ||
| 3.4. Simulation Model | 66 | ||
| 3.4.1. Activities of ED in the normal situation | 67 | ||
| 3.4.2. Activities of ED in the major accident situation | 68 | ||
| 3.5. Simulation Experiments | 69 | ||
| 3.5.1. Data collection | 70 | ||
| 3.5.2. Validation and verification | 71 | ||
| 3.5.3. Design of experiments | 72 | ||
| 3.5.4. Improvements and suggestions for the hospital | 77 | ||
| 3.6. Conclusion | 80 | ||
| References | 80 | ||
| Chapter 4 Stochastic and Dynamic Programming for Improving the Reservation Process of MRIExaminations\r | 83 | ||
| Abstract | 83 | ||
| 4.1. Introduction | 84 | ||
| 4.2. Literature Review | 88 | ||
| 4.3. The Determination of CTS and the Optimal Patient Assignment Policy | 90 | ||
| 4.3.1. Model formulation | 91 | ||
| 4.3.2. Exploration of the optimal patient assignment policy via MDP | 92 | ||
| 4.3.3. Contract optimization via Monte Carlo approximation | 95 | ||
| 4.4. Joint Patient Assignment and Advance CTS Cancellation | 97 | ||
| 4.4.1. One-day advance CTS cancellation | 97 | ||
| 4.4.2. Joint patient assignment and one-day and two-day advance CTS cancellation policies | 100 | ||
| 4.5. Implementation Strategies | 106 | ||
| 4.6. Conclusions and Future Perspectives | 109 | ||
| Acknowledgment | 111 | ||
| References | 111 | ||
| Chapter 5 Simulation Modeling of Hospital Discharge Process | 113 | ||
| Abstract | 113 | ||
| 5.1. Introduction | 115 | ||
| 5.2. Methods | 117 | ||
| 5.2.1. The discharge process | 117 | ||
| 5.2.1.1. The SW/CM workflow | 118 | ||
| 5.2.1.2. The RPH workflow | 120 | ||
| 5.2.1.3. The transportation workflow | 121 | ||
| 5.2.2. The simulation model | 121 | ||
| 5.2.3. Data collection | 122 | ||
| 5.2.4. Model validation | 124 | ||
| 5.2.5. Test design | 124 | ||
| 5.3. Results | 125 | ||
| 5.3.1. RPH intervention rate | 125 | ||
| 5.3.2. Reducing RPH working time | 125 | ||
| 5.3.3. Reducing SW/CM working time | 126 | ||
| 5.3.4. Reducing the time of “wait for physician’s order” | 126 | ||
| 5.3.5. Reducing the time of “wait for others” | 127 | ||
| 5.4. Discussions | 128 | ||
| 5.5. Conclusions | 129 | ||
| Acknowledgement | 131 | ||
| Competing Interests | 131 | ||
| References | 131 | ||
| Chapter 6 Predictive Modeling of Care Demand and Transition | 135 | ||
| Abstract | 135 | ||
| 6.1. Background and Introduction | 136 | ||
| 6.2. A Classification Study for 30-Day Hospital Readmission Prediction | 139 | ||
| 6.2.1. Summary of the study | 139 | ||
| 6.2.2. Current landscape in practice | 139 | ||
| 6.2.3. State of the art in academic research | 140 | ||
| 6.2.4. Data description | 142 | ||
| 6.2.5. Data modeling methodology | 142 | ||
| 6.2.5.1. Data preparation | 143 | ||
| 6.2.5.2. Ad-hoc conditional logistic regression modeling | 146 | ||
| 6.2.6. Analysis results | 148 | ||
| 6.3. A Bayesian Modeling Study of Community Dwelling Duration Prior to Long-Term Care | 149 | ||
| 6.3.1. Summary of the study | 149 | ||
| 6.3.2. Current landscape in practice | 150 | ||
| 6.3.3. State-of-the-art academic research | 151 | ||
| 6.3.4. Data description | 152 | ||
| 6.3.5. Data modeling methodology | 152 | ||
| 6.3.6. Analysis results | 154 | ||
| 6.4. Conclusions and Future Work | 157 | ||
| Acknowledgments | 158 | ||
| References | 158 | ||
| Chapter 7 A Multi-agent-based Simulation Model to Analyze Patients’ Hospital Selection in Hierarchical Healthcare Systems | 167 | ||
| Abstract | 167 | ||
| 7.1. Introduction | 168 | ||
| 7.2. Model Description | 171 | ||
| 7.2.1. Patients’ preference | 172 | ||
| 7.2.2. Patients’ decision model | 175 | ||
| 7.3. Case Study | 177 | ||
| 7.3.1. Input parameter | 177 | ||
| 7.3.2. Simulation analysis | 179 | ||
| 7.3.3. Two incentive policies | 183 | ||
| 7.3.3.1. Reducing the outpatient cost of CHCs | 183 | ||
| 7.3.3.2. Improving the lowest quality of the CHC | 184 | ||
| 7.4. Conclusion | 185 | ||
| Acknowledgement | 186 | ||
| References | 186 | ||
| Chapter 8 Forecasting Recipient Outcomes of Deceased Donor Livers | 189 | ||
| Abstract | 189 | ||
| 8.1. Introduction | 190 | ||
| 8.2. Existing Work and Motivation | 191 | ||
| 8.2.1. Liver transplantation | 191 | ||
| 8.2.2. Donor liver availability and utilization | 192 | ||
| 8.2.3. Transplant recipient outcomes | 192 | ||
| 8.2.4. Methodology overview | 193 | ||
| 8.3. Statistical Models | 194 | ||
| 8.3.1. Recipient characteristics | 194 | ||
| 8.3.2. Donor characteristics | 196 | ||
| 8.3.3. Cold ischemia time | 198 | ||
| 8.4. Simulation Model | 198 | ||
| 8.4.1. Model description | 199 | ||
| 8.4.2. Model validation | 200 | ||
| 8.5. Results | 201 | ||
| 8.5.1. Proportional hazard model | 201 | ||
| 8.5.2. D-MELD | 205 | ||
| 8.6. Summary and Discussion | 206 | ||
| 8.6.1. Limitations | 206 | ||
| 8.6.2. Future work | 207 | ||
| Acknowledgments | 207 | ||
| References | 207 | ||
| Chapter 9 Internet of Hearts — Large-Scale Stochastic Network Modeling and Analysis of Cardiac Electrical Signals | 211 | ||
| Abstract | 211 | ||
| 9.1. Introduction | 212 | ||
| 9.2. Background | 216 | ||
| 9.3. Analytical Modules | 219 | ||
| 9.3.1. Real-time spatiotemporal visualization and feature extraction | 220 | ||
| 9.3.2. Optimal model-based representation | 222 | ||
| 9.3.3. Stochastic network modeling and online diagnosis | 229 | ||
| 9.4. MESH Design | 237 | ||
| 9.4.1. Wearable sensing device | 239 | ||
| 9.4.2. MESH database | 240 | ||
| 9.4.3. MESH smartphone application | 242 | ||
| 9.5. Discussion | 245 | ||
| 9.6. Summary | 246 | ||
| Acknowledgment | 247 | ||
| References | 248 | ||
| Chapter 10 Using Agent-Based Interpersonal Influence Simulation to Study the Formation of Public Opinion | 253 | ||
| Abstract | 253 | ||
| 10.1. Introduction and Background | 254 | ||
| 10.2. Computational Models of Social Contagion and Influence | 257 | ||
| 10.3. An ABM Approach | 259 | ||
| 10.4. Simulation Results and Discussions | 263 | ||
| 10.4.1. Simulation results | 265 | ||
| 10.4.2. Discussion on policy implications | 270 | ||
| 10.5. Conclusions and Future Work | 272 | ||
| Appendix | 273 | ||
| References | 275 | ||
| Chapter 11 Growth Curves of American Children Differ Significantly from CDC Reference Standards | 281 | ||
| Abstract | 281 | ||
| 11.1. Introduction | 282 | ||
| 11.2. Methods | 283 | ||
| 11.2.1. Data sources | 284 | ||
| 11.2.1.1. NorthShore University HealthSystem \rEnterprise Data Warehouse | 284 | ||
| 11.2.1.2. National Health and Nutrition Examination Survey | 286 | ||
| 11.2.2. Inclusion criteria | 286 | ||
| 11.2.3. Exclusion criteria | 286 | ||
| 11.2.4. Data statistics | 287 | ||
| 11.3. Results | 287 | ||
| 11.3.1. Stature-for-age | 294 | ||
| 11.3.2. Weight-for-age | 294 | ||
| 11.3.3. BMI-for-age | 294 | ||
| 11.3.4. LMS statistics | 295 | ||
| 11.3.5. Curve analysis | 297 | ||
| 11.4. Conclusions | 299 | ||
| 11.4.1. Significance | 300 | ||
| 11.4.2. Long-term goal | 302 | ||
| 11.4.3. Limitations | 302 | ||
| Acknowledgment | 303 | ||
| References | 303 | ||
| Index | 307 |