Emergency department (ED) overcrowding has been a common complaint in Emergency Medicine in Canada for many years. Its adverse effects of prolonged waiting times cause patient dissatisfaction and unsafety. Previous studies indicate that adding a physician in triage (PIT) can increase accuracy and efficiency in the initial process of patient evaluation. However, the scientific evidence of the PIT impact on ED is far away from sufficient before its widespread implementation. This research is to search solutions using PIT to identify areas of improvement for the ED patient flow, based upon a validated discrete-event simulation (DES) model. As an efficient decision-making tool, the DES model also helps to develop an understanding of the current ED performance and quantitatively test various design alternatives for ED operations. / February 2017

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.