Applying mathematical models to surgical patient planning

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Chapter 1 Introduction 1. General introduction and research motivation European countries face an ageing population and rising health care costs (OECD, 2008). At the same time, the general public demands the latest technologies, combined with short waiting and access times. This growing demand for cure and care provided by hospitals puts a strong focus on effectiveness and efficiency. Hospital management is challenged to deal with the seemingly conflicting objectives of low costs, high quality of care, and high quality of labor. Health care logistics can potentially make a major contribution to maintaining high level care and affordable costs. One of the major challenges of health care logistics in hospitals is to improve processes related to surgical case scheduling. Surgical scheduling is a complex task in hospitals, and a popular topic among academic researchers. Typically, the focus is on maximizing operating room utilization and revenues. However, an operating room department is not a stand-alone unit within a hospital. Studies on academic models and real implementations of strategies that optimize surgical scheduling taking into account the other partners in the care chain. This thesis describes the concept of master surgical scheduling in a 7-stepwise approach. This approach cyclically executes a master schedule of surgical case types. A master surgical schedule not only allows for optimization of operating room utilization, it creates robustness and minimizes overtime while it also makes resource demands on other departments, such as wards, more predictable. Master surgical scheduling is a generic framework that helps hospitals to improve their logistics. This thesis provides the reader with organizational prerequisites, mathematical models, managerial aspects, and practical insights into the suggested master surgical scheduling approach. Dealing with emergency arrivals in operating room departments is for surgeons and staff one of the most demanding tasks. Within a master surgical scheduling approach the thesis provides insights into how to deal with emergency surgical patients arriving during day and night times. The proposed models may be integrated in the master surgical scheduling approach, but can function in conjunction with other elective scheduling approaches as well. Together with the master surgical scheduling approach this thesis provides a logistical approach to improve both the operational and tactical level of operating room management. 1.1. Organizational aspects Hospital organizations are organizations with a complex structure. An operating room department is one of those places where many workers cooperate to deliver complex and
4 Introduction high-level care to patients. About sixty percent of the inpatient hospital admissions are surgery related. Hence, often one of the biggest concerns of hospital management is the manageability of surgical scheduling processes. Any logistical problem in surgical scheduling has a substantial impact on a hospital organization. Moreover, such disruptions due to logistical problems cause both frustrations of staff and inefficiency. Many actors are involved in the process of surgical case scheduling. This can easily lead to inefficiencies in information transfer and unnecessarily repeated work. Hospital management wants to keep the surgical scheduling processes efficient, and at the work floor level easy and transparent. Sometimes patients are scheduled months ahead, sometimes they are scheduled just minutes in advance. Hence, flexibility in hospital organizations is a prerequisite for providing optimal care to patients. The argument that flexibility is required to schedule emergency patients is particularly used by physicians. However, the argument is often misused to claim more resources than are necessary. For example, requests by surgical departments for operating room capacity often substantially exceed the amount of operating room time that is actually needed. This leads to underutilization of expensive and scarce operating room capacity. Finding a good balance between efficiency and flexibility to deal with emergencies is hence of paramount importance. Physicians are high-level educated professionals who work with individual patients and who perform multiple tasks, ranging from seeing patients at outpatient clinics and performing surgery to research and managerial tasks. From an organizational perspective, physicians have a substantial amount of formal and informal power. Any change in a logistical process should therefore be accounted by this group of actors. Therefore, to be applicable, any logistical model should take account of the autonomy and particularities of this group of actors. Therefore any logistical improvement project needs to discuss these limits carefully. Operations research models can help by providing analytical insights for such discussions. Personnel at wards and operating room departments are employed by hospitals. Strict labor rules apply for this group of actors in hospitals such that work schedules of nurses are highly regulated. For example, changing staffing levels of regularly employed staff can only be done weeks in advance. It is therefore important that forecasts on the required demand are close to the actual demand, because deviations generally increase costs. Hence, stability of patient flows improves efficient usage of this scarce resource. 1.2. Operations Research aspects Operations Research can generate models to improve patient planning. However, improving hospital logistics by such models is only possible when they actually get implemented. A prerequisite for such models is a good trade-off between a correct representation of reality and understandability. Models that include huge numbers of
Page 1 and 2: JEROEN VAN OOSTRUM Applying mathema
Page 4 and 5: Applying Mathematical Models to Sur
Page 6 and 7: Acknowledgements This thesis is a p
Page 8 and 9: Contents iii Contents Acknowledgeme
Page 10 and 11: Contents v 5.2. Modeling resource d
Page 12 and 13: Contents vii 4.4. Step 4: Define a
Page 14: Chapter 1 Introduction Parts appear
Page 19 and 20: 6 Introduction The OR department is
Page 21 and 22: 8 Introduction elective surgeries (
Page 23 and 24: 10 Introduction central planner dec
Page 25 and 26: 12 Introduction 5. A 7-stepwise app
Page 27 and 28: 14 Introduction The organization ma
Page 29 and 30: 16 Introduction room-days to a mast
Page 31 and 32: 18 Introduction Hans, E., Wullink,
Page 33 and 34: 20 Introduction
Page 36 and 37: Chapter 2 Improving operating room
Page 38 and 39: Chapter 2 25 Unit1 Unit 2 Unit 3 Un
Page 40 and 41: Chapter 2 27 2.3. Planned slack and
Page 42 and 43: Chapter 2 29 surgery. Surgical depa
Page 44 and 45: Chapter 2 31 3. Results Applying th
Page 46 and 47: Chapter 2 33 4. Discussion The stud
Page 48 and 49: Chapter 2 35 8. Marcon E, Kharraja
Page 50: Chapter 3 A method for clustering s
Page 53 and 54: 40 A method for clustering surgical
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Chapter 4 A master surgical schedul
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Chapter 4 55 Jebali et al. 2006) an
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Chapter 4 57 ponents. This principl
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Chapter 4 59 procedures on an OR-da
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Chapter 4 61 This model has two mai
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Chapter 4 63 The dual constraints o
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Chapter 4 65 beds for all days with
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Chapter 4 67 Cycle length Number of
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Chapter 4 69 Initialization heurist
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Chapter 4 71 scheduling of OR depar
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Chapter 4 73 Van den Akker JM, Hoog
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Chapter 5 Fewer intensive care unit
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78 Fewer intensive care unit refusa
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Chapter 6 Implementing a master sur
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Chapter 6 91 centers encompass all
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Chapter 6 93 approach. Creating suc
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Chapter 6 95 We define a master sur
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Chapter 6 97 difference existed bet
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Chapter 6 99 4.5. Step 5: Construct
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Chapter 6 101 unknown. However, whe
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Chapter 6 103 McLaughlin, C.P., Yan
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Chapter 7 Requirements for a full s
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Chapter 7 Requirements for a full s
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Chapter 7 109 Erasmus MC deals with
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Chapter 7 111 on in an OR that is d
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Chapter 7 113 2.4. Modeling the OR
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Chapter 7 115 Total number of guara
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Chapter 7 117 Mean number of OR tea
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Chapter 7 119 Not guaranteeing all
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Chapter 7 121 schedules are properl
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Chapter 8 Closing emergency operati
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Chapter 8 Closing emergency operati
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Chapter 8 127 Figure 1: Visualizati
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Chapter 8 129 Interrupting the exec
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Appendix to Chapter 8 Letter 1: non
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Chapter 9 A simulation model for de
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Chapter 9 A simulation model for de
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Chapter 9 137 performed at least ei
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Chapter 9 Case durations wwere draw
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Chapter 9 141 Scenarios with fewer
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Chapter 9 143 Figure 2: Proportions
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Chapter 9 145 4. Discussion A simul
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Chapter 9 147 References 1. Simoens
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Chapter 10 Concluding remarks and i
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Chapter 10 Concluding remarks and i
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Chapter 10 153 processes. Ideally,
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Nederlandse samenvatting (Summary i
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157 binnen operatiekamercomplexen w
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Curriculum Vitae Jeroen van Oostrum
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ERASMUS RESEARCH INSTITUTE OF MANAG
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Brumme, W.-H., Manufacturing Capabi
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Gilsing, V.A., Exploration, Exploit
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Iwaarden, J.D. van, Changing Qualit
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Le-Duc, T., Design and Control of E
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Mom, T.J.M., Managers’ Exploratio
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Quak, H.J., Sustainability of Urban
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Szkudlarek, B.A., Spinning the Web
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Wielemaker, M.W., Managing Initiati
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Erasmus Research Institute of Manag
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Applying mathematical models to surgical patient planning

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