Emergency surgeries are serviced by three main forms of capacity: dedicated operating room time reserved for emergency surgeries, alternative (on call) capacity, and lastly, canceling of elective surgeries. The objective of this research is to model capacity implications of meeting wait time targets for multiple priority levels in the context of emergency surgeries. Initial attempts to solve the capacity evaluation problem were made using a non-linear optimisation model, however, this model was intractable. A simulation model was then used to examine the trade-off between additional dedicated operating room capacity (and consequent idle capacity) versus increased re-scheduling of elective surgeries while keeping reserved time for emergency surgeries low. Considered performance measures include utilization of operating room time, elective re-scheduling, and wait times by priority class. Finally, the instantaneous utilization of different types of downstream beds is determined to aid in capacity planning. The greatest number of patients seen within their respective wait time targets is achieved by a combination of additional on call capacity and a variation of the rule allowing low priority patients to utilize on call capacity. This also maintains lower cancelations of elective surgeries than the current situation. Although simulation does not provide an optimum solution it enables a comparison of different scenarios. This simulation model can determine appropriate capacity levels for servicing emergency patients of different priorities with different wait time targets.

Emergency surgeries are serviced by three main forms of capacity: dedicated operating room time reserved for emergency surgeries, alternative (on call) capacity, and lastly, canceling of elective surgeries. The objective of this research is to model capacity implications of meeting wait time targets for multiple priority levels in the context of emergency surgeries. Initial attempts to solve the capacity evaluation problem were made using a non-linear optimisation model, however, this model was intractable. A simulation model was then used to examine the trade-off between additional dedicated operating room capacity (and consequent idle capacity) versus increased re-scheduling of elective surgeries while keeping reserved time for emergency surgeries low. Considered performance measures include utilization of operating room time, elective re-scheduling, and wait times by priority class. Finally, the instantaneous utilization of different types of downstream beds is determined to aid in capacity planning. The greatest number of patients seen within their respective wait time targets is achieved by a combination of additional on call capacity and a variation of the rule allowing low priority patients to utilize on call capacity. This also maintains lower cancelations of elective surgeries than the current situation. Although simulation does not provide an optimum solution it enables a comparison of different scenarios. This simulation model can determine appropriate capacity levels for servicing emergency patients of different priorities with different wait time targets.

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

abstract: Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model is used to evaluate how scheduling heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared to current practice. Next, a bi-criteria Genetic Algorithm is used to determine if better solutions can be obtained for this single day scheduling problem. The efficacy of the bi-criteria Genetic Algorithm, when surgeries are allowed to be moved to other days, is investigated. Numerical experiments based on real data from a large health care provider are presented. The analysis provides insight into the best scheduling heuristics, and the tradeoff between patient and health care provider based criteria. Second, a multi-stage stochastic mixed integer programming formulation for the allocation of surgeries to ORs over a finite planning horizon is studied. The demand for surgery and surgical duration are random variables. The objective is to minimize two competing criteria: expected surgery cancellations and OR overtime. A decomposition method, Progressive Hedging, is implemented to find near optimal surgery plans. Finally, properties of the model are discussed and methods are proposed to improve the performance of the algorithm based on the special structure of the model. It is found simple rules can improve schedules used in practice. Sequencing surgeries from the longest to shortest mean duration causes high expected overtime, and should be avoided, while sequencing from the shortest to longest mean duration performed quite well in our experiments. Expending greater computational effort with more sophisticated optimization methods does not lead to substantial improvements. However, controlling daily procedure mix may achieve substantial improvements in performance. A novel stochastic programming model for a dynamic surgery planning problem is proposed in the dissertation. The efficacy of the progressive hedging algorithm is investigated. It is found there is a significant correlation between the performance of the algorithm and type and number of scenario bundles in a problem instance. The computational time spent to solve scenario subproblems is among the most significant factors that impact the performance of the algorithm. The quality of the solutions can be improved by detecting and preventing cyclical behaviors. / Dissertation/Thesis / Ph.D. Industrial Engineering 2010

Emergency surgeries are serviced by three main forms of capacity: dedicated operating room time reserved for emergency surgeries, alternative (on call) capacity, and lastly, canceling of elective surgeries. The objective of this research is to model capacity implications of meeting wait time targets for multiple priority levels in the context of emergency surgeries. Initial attempts to solve the capacity evaluation problem were made using a non-linear optimisation model, however, this model was intractable. A simulation model was then used to examine the trade-off between additional dedicated operating room capacity (and consequent idle capacity) versus increased re-scheduling of elective surgeries while keeping reserved time for emergency surgeries low. Considered performance measures include utilization of operating room time, elective re-scheduling, and wait times by priority class. Finally, the instantaneous utilization of different types of downstream beds is determined to aid in capacity planning. The greatest number of patients seen within their respective wait time targets is achieved by a combination of additional on call capacity and a variation of the rule allowing low priority patients to utilize on call capacity. This also maintains lower cancelations of elective surgeries than the current situation. Although simulation does not provide an optimum solution it enables a comparison of different scenarios. This simulation model can determine appropriate capacity levels for servicing emergency patients of different priorities with different wait time targets.

This work has its focus on decision support in operational business situations and especially on the very short-term decisions in Online Surgery Scheduling, which has the goal of efficient and structured operations in the Operating Room area at minimal costs. This use case includes all intra-day decisions needed to ensure the execution of all planned and unplanned surgeries of the surgery schedule, with all of the concomitant uncertainties like unexpected events, delays, cancellations and emergency patients. This so far barely considered problem needs research for decision support, since few approaches are available that relieve the OR manager through tool support and reduce the informational, communicational and cognitive workloads needed to ensure efficient and seamless operations. With the strong growth of generated data and the digitization of business processes that make previously unobtrusive business elements become more visible, and their combination with large-scale data processing technologies and intelligent methods of the fields of AI or Analytics, new opportunities for data-driven real-time Decision Support Systems become evident. The objective of this research is the development of an approach that supports the operational decision processes in Operating Room Management and Online Surgery Scheduling, like facilitating the information collection and reducing the cognitive effort for decision-making by providing predictive information or alternative actions. In order to achieve this goal, a decision support approach is developed that utilizes streaming data of medical and surgical devices in a Situation Detection Subsystem, a Prediction Subsystem and a Rescheduling Subsystem. These components combine intelligent methods and scalable data processing technologies, consequently contributing a data-driven Decision Support System for Online Surgery Scheduling. The scientific contribution relates to the field of Business and Decision Analytics with its main challenges of increasing complexity and dynamics of today’s business decisions. This work provides a novel DSS approach, innovative models and concepts which consider exactly these problems with regards to the characteristics of OSS.:Table of Contents ................................ I List of Figures .................................. III List of Tables ................................... IV List of Abbrevations ............................. V 1 Introduction.................................... 1 1.1 Motivation ................................... 1 1.2 Research Objective and Questions ............. 2 1.3 Research Methodology ......................... 4 1.4 Outline ...................................... 7 2 Background ..................................... 9 2.1 Operational Decisions and Decision Support Systems .................................. 9 2.1.1 Decisions and Decision-making .............. 9 2.1.2 Operational Decision-making ................ 11 2.1.3 Decision Support Systems ................... 13 2.1.4 Business Value and Benefits ................ 18 2.2 Business Analytics ........................... 19 2.2.1 Characterization and Definition ............ 19 2.2.2 Delimitation of Areas ...................... 20 2.2.3 Types of Business Analytics ................ 21 2.2.4 Methods and Technologies ................... 23 3 Use-Case: Operational Decisions in Operating Room Management .................................. 29 3.1 Preliminary Considerations ................... 29 3.2 Online Surgery Scheduling .................... 31 3.2.1 Mapping of Decision Theory and Online Surgery Scheduling ............................... 32 3.2.2 Information Demands ........................ 33 3.2.3 State of the Art in Decision Support Systems 36 4 Motivation and Requirements .................... 38 4.1 Development of an Information System Architecture for Online Surgery Scheduling ....... 38 4.2 Summary ...................................... 49 5 Evaluation of Big Data Processing Frameworks.... 51 5.1 Evaluating new Approaches of Big Data Analytics Frameworks ............................. 51 5.2 Summary ...................................... 63 6 Stream Processing for Intra-surgical Phase Detection ........................................ 64 6.1 Method for Intra-surgical Phase Detection by Using Real-time Medical Device Data .............. 64 6.2 Summary ...................................... 71 7 Real-time Predictive Analytics in Operating Room Management ....................................... 72 7.1 A Big Data Architecture for Intra-surgical Remaining Time Predictions ....................... 72 7.2 Summary ...................................... 81 8 Data-driven Online Surgery Rescheduling ........ 83 8.1 Online Surgery Rescheduling - A Data-driven Approach for Real-time Decision Support .......... 83 8.2 Summary ...................................... 92 9 Prototypical Implementation: Decision Support in Online Surgery Scheduling ........................ 93 9.1 Implementation of a Situation Aware and Real-time Approach for Decision Support in Online Surgery Scheduling ............................... 93 9.2 Summary ...................................... 99 10 Conclusion .................................... 100 10.1 Summary and Contributions ................... 100 10.2 Limitations and Future Work ................. 103 Bibliography ..................................... VII Appendix ......................................... XXV Wissenschaftlicher Werdegang ..................... XXXI Selbständigkeitserklärung ........................ XXXII

Improved utilization of scarce resources such as health care personnel is necessary to address well-known problem of long waiting times within the health care. Implementing mathematically modeled scheduling in the operating theatre has the potential to result in more efficient allocation of resources and financial gains. Despite the promising results, the adoption rate of such models is low. This thesis examines the impact of a mixed-integer linear programming model using an overlapping strategy. We perform a computational experiment where both sequential and parallel schedules are produced with real surgery data from an orthopedic department at a Swedish university hospital. The generated schedules are compared against each other in measurements of cost productivity. Statistical analysis shows that there is a statistical significant difference between the two schedules, favoring the optimized schedule. The results further suggest that three operating rooms and four surgery teams is the most optimal combination of the 18 combinations analyzed, where operating rooms and surgery teams varies between 1-4 and 1-6, respectively.