In the Department of Pathology and Laboratory Medicine, at the beginning of each month, the clinical managers use expert knowledge to assign pathologists to expected daily specimens based on the criteria of workload restrictions, clinical sub-specialties, and availability. Since the size of the pathologists’ assignment problem is large, finding a feasible assignment manually is a very time-consuming process that takes a number of iterations over a number of days to complete. Moreover, every time there is a need to make a revision, a new assignment needs to be developed taking into account all the above criteria. The goal of this research is to develop an optimization model and a decision support tool that will help with monthly staffing of pathologists based on the criteria outlined above. The developed model is rooted in the classical operations research assignment problem and it is extended to account for the following requirements: each pathologist should be assigned to a similar specimen type throughout a week; for a given pathologist, there should be a rotation of the specimen types between the weeks; and the clinical managers’ preferences in terms of assigning a particular specimen type to a particular pathologist on a specific day need to be considered. A monthly assignment model covering 36 pathologists and 26 specimen types was solved using IBM ILOG CPLEX Optimization Studio. It is embedded in a decision support tool that helps clinical managers to make staffing decisions. The decision support tool has been validated using data from The Ottawa Hospital (TOH).

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.

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.

The Ottawa Hospital cancels hundreds of elective surgeries every year due to a lack of beds, and has an average weekday occupancy rate above 100%. Our approach to addressing these issues, by way of informing administrators of resource needs, was to model the flow of patients coming and going from the hospital. We used administrative data from the Ottawa Hospital to build a time-series model of emergency department admissions, and studied models that would predict next-day discharge of patients currently taking up hospital beds. In the latter, we considered population-averaged models for groups of patients based on their primary medical condition, as well as subject-specific models. We included the random effects from subject-specific variation to improve on predictive accuracy over the population- averaged approach. The result was a model that provided more realistic probabilities of discharge, and stable predictive accuracy over patient length of stay.

The Ottawa Hospital cancels hundreds of elective surgeries every year due to a lack of beds, and has an average weekday occupancy rate above 100%. Our approach to addressing these issues, by way of informing administrators of resource needs, was to model the flow of patients coming and going from the hospital. We used administrative data from the Ottawa Hospital to build a time-series model of emergency department admissions, and studied models that would predict next-day discharge of patients currently taking up hospital beds. In the latter, we considered population-averaged models for groups of patients based on their primary medical condition, as well as subject-specific models. We included the random effects from subject-specific variation to improve on predictive accuracy over the population- averaged approach. The result was a model that provided more realistic probabilities of discharge, and stable predictive accuracy over patient length of stay.

The Ottawa Hospital cancels hundreds of elective surgeries every year due to a lack of beds, and has an average weekday occupancy rate above 100%. Our approach to addressing these issues, by way of informing administrators of resource needs, was to model the flow of patients coming and going from the hospital. We used administrative data from the Ottawa Hospital to build a time-series model of emergency department admissions, and studied models that would predict next-day discharge of patients currently taking up hospital beds. In the latter, we considered population-averaged models for groups of patients based on their primary medical condition, as well as subject-specific models. We included the random effects from subject-specific variation to improve on predictive accuracy over the population- averaged approach. The result was a model that provided more realistic probabilities of discharge, and stable predictive accuracy over patient length of stay.

The Ottawa Hospital cancels hundreds of elective surgeries every year due to a lack of beds, and has an average weekday occupancy rate above 100%. Our approach to addressing these issues, by way of informing administrators of resource needs, was to model the flow of patients coming and going from the hospital. We used administrative data from the Ottawa Hospital to build a time-series model of emergency department admissions, and studied models that would predict next-day discharge of patients currently taking up hospital beds. In the latter, we considered population-averaged models for groups of patients based on their primary medical condition, as well as subject-specific models. We included the random effects from subject-specific variation to improve on predictive accuracy over the population- averaged approach. The result was a model that provided more realistic probabilities of discharge, and stable predictive accuracy over patient length of stay.