Reducing emergency department (ED) wait times are a major priority for the Ontario Government. Overcrowded EDs, cumulative effects of the delays in hospital processes and lack of resources are manifested in the phenomenon of long wait times. This thesis aims to estimate in real-time, a minimum wait time confidence interval for urgent abdominal pain patients on weekdays based on ED operations, waiting room status and ED census indicators through multivariate backwards stepwise regression modeling. The ED wait times model accurately predicted a 95% wait time confidence interval for patients. Common underlying factors attributed to long wait times include the total number of emergent and urgent patients in the waiting room, the total number of patient waiting for a consultation and the number of patients not seen within the Ontario Government’s target times. This information is useful in managing patient expectations and appropriately allocating resources to improve wait times.

Reducing emergency department (ED) wait times are a major priority for the Ontario Government. Overcrowded EDs, cumulative effects of the delays in hospital processes and lack of resources are manifested in the phenomenon of long wait times. This thesis aims to estimate in real-time, a minimum wait time confidence interval for urgent abdominal pain patients on weekdays based on ED operations, waiting room status and ED census indicators through multivariate backwards stepwise regression modeling. The ED wait times model accurately predicted a 95% wait time confidence interval for patients. Common underlying factors attributed to long wait times include the total number of emergent and urgent patients in the waiting room, the total number of patient waiting for a consultation and the number of patients not seen within the Ontario Government’s target times. This information is useful in managing patient expectations and appropriately allocating resources to improve wait times.

No description available.

This thesis is composed of three studies pertaining to the quality of care for radical cystectomy in Ontario between 1992 and 2004. In the first paper, the associations between provider volume and both operative and overall mortality were assessed. In the second paper, potential factors that could explain the association between volume and outcome were explored. In the final paper, the impact of waiting for cystectomy on survival outcomes was evaluated. Methods: A total of 3296 patients undergoing cystectomy for bladder cancer in Ontario between 1992 and 2004 were identified using the Canadian Institute for Health Information Discharge Abstract Database and the Ontario Cancer Registry. The effects of hospital and surgeon volume on operative mortality and overall survival were assessed using random effects logistic regression and marginal Cox Proportional Hazards modeling, respectively. To elucidate the factors underlying the volume-outcome association, the ability of a number of structure and process of care variables to attenuate the impact of volume was assessed. The effect of waiting for care, from transurethral resection to cystectomy, on overall survival was also assessed using marginal Cox models. Results: Neither hospital nor surgeon volume was significantly associated with operative mortality; however, both were associated with overall mortality. Of the measured structure/process measures, hospital factors caused the greatest attenuation of the volume hazard ratios, albeit to a limited degree. The wait time between the decision for surgery and cystectomy was also significantly associated with overall survival. The impact of delayed care was greatest for patients with lower stage disease. The data suggested a maximum wait time of 40 days for cystectomy. Conclusions: In this thesis, gaps in the quality of care for radical cystectomy in Ontario were identified. Patients treated by low volume hospitals and surgeons or those with long wait times all experienced worse outcomes. Since the underlying measures responsible for provider volume remain elusive, additional work is required to understand what these factors are. Initiatives to decrease wait times, however, are under way in Ontario. Whether these interventions decrease wait times and benefit patients remains to be seen.

This thesis is composed of three studies pertaining to the quality of care for radical cystectomy in Ontario between 1992 and 2004. In the first paper, the associations between provider volume and both operative and overall mortality were assessed. In the second paper, potential factors that could explain the association between volume and outcome were explored. In the final paper, the impact of waiting for cystectomy on survival outcomes was evaluated. Methods: A total of 3296 patients undergoing cystectomy for bladder cancer in Ontario between 1992 and 2004 were identified using the Canadian Institute for Health Information Discharge Abstract Database and the Ontario Cancer Registry. The effects of hospital and surgeon volume on operative mortality and overall survival were assessed using random effects logistic regression and marginal Cox Proportional Hazards modeling, respectively. To elucidate the factors underlying the volume-outcome association, the ability of a number of structure and process of care variables to attenuate the impact of volume was assessed. The effect of waiting for care, from transurethral resection to cystectomy, on overall survival was also assessed using marginal Cox models. Results: Neither hospital nor surgeon volume was significantly associated with operative mortality; however, both were associated with overall mortality. Of the measured structure/process measures, hospital factors caused the greatest attenuation of the volume hazard ratios, albeit to a limited degree. The wait time between the decision for surgery and cystectomy was also significantly associated with overall survival. The impact of delayed care was greatest for patients with lower stage disease. The data suggested a maximum wait time of 40 days for cystectomy. Conclusions: In this thesis, gaps in the quality of care for radical cystectomy in Ontario were identified. Patients treated by low volume hospitals and surgeons or those with long wait times all experienced worse outcomes. Since the underlying measures responsible for provider volume remain elusive, additional work is required to understand what these factors are. Initiatives to decrease wait times, however, are under way in Ontario. Whether these interventions decrease wait times and benefit patients remains to be seen.

The basis for this thesis involved a four month Accelerate Canada internship at the Grand River Hospital Emergency Department in Kitchener, Ontario. The Emergency Department (ED) Process Committee sought insight into strategies that could potentially reduce patient length of stay in the ED, thereby reducing wait times for emergency patients. This thesis uses discrete event simulation to model the overall system and to analyze the effect of various operational strategies within the fast track area of the emergency department. It discusses the design and development process for the simulation model, proposes various operational strategies to reduce patient wait times, and analyzes the different scenarios for an optimal fast track strategy. The main contribution of this thesis is the use of simulation to determine an optimal fast track strategy that reduces patient length of stay, thereby reducing patient wait times. Wait times were most significantly reduced when there was an increased physician presence/availability towards the fast track system. This had the greatest impact on the total time spent in the ED and also on queue length. The second most significant reduction to the performance measures occurred when an additional emergency nurse practitioner was supplemented to the fast track system. Accordingly, the nurse practitioner’s percent utilization increased. There was only one two-way interaction effect that was statistically significant in reducing the primary performance measure of wait times; however, the effect did not change the queue length, a secondary performance measure, by a significant amount. Finally, the implementation of a See-and-treat model variant for fast track had a negligible effect on both the average length of stay and queue length.

No description available.

The basis for this thesis involved a four month Accelerate Canada internship at the Grand River Hospital Emergency Department in Kitchener, Ontario. The Emergency Department (ED) Process Committee sought insight into strategies that could potentially reduce patient length of stay in the ED, thereby reducing wait times for emergency patients. This thesis uses discrete event simulation to model the overall system and to analyze the effect of various operational strategies within the fast track area of the emergency department. It discusses the design and development process for the simulation model, proposes various operational strategies to reduce patient wait times, and analyzes the different scenarios for an optimal fast track strategy. The main contribution of this thesis is the use of simulation to determine an optimal fast track strategy that reduces patient length of stay, thereby reducing patient wait times. Wait times were most significantly reduced when there was an increased physician presence/availability towards the fast track system. This had the greatest impact on the total time spent in the ED and also on queue length. The second most significant reduction to the performance measures occurred when an additional emergency nurse practitioner was supplemented to the fast track system. Accordingly, the nurse practitioner’s percent utilization increased. There was only one two-way interaction effect that was statistically significant in reducing the primary performance measure of wait times; however, the effect did not change the queue length, a secondary performance measure, by a significant amount. Finally, the implementation of a See-and-treat model variant for fast track had a negligible effect on both the average length of stay and queue length.

Thesis (M.S.H.P.) / BACKGROUND: Emergency department (ED) visits for psychiatric issues have grown at a disproportionately higher rate than other visits. This has been attributed to factors including severe cuts in mental health (MH) services and identified as a culprit in ED overcrowding. Little is known, however, about how mental health reason-for-visit (MHRFV) interacts with patient and hospital characteristics to affect wait times to see an ED provider. OBJECTIVE: To determine if wait time (WT) to see a provider at the ED differs for those presenting with MHRFV and how various patient and hospital-level characteristics interact to affect it. METHODS: Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) for visits to EDs throughout the United States. We examined data for patients ≥ 18 years of age who visited an ED in years 2009 and 2010. Patient weights were used to generate national estimates. Patients’ primary reasons-for-visit were used to identify the MH group for analysis and comparison to all other RFVs. Predictors of WT were chosen based on the Andersen Behavioral and ED overcrowding models. WTs were log-transformed for initial bivariate and final multivariate regression models to assure a more normal distribution. RESULTS: Mean WT was 56.5 and 55.8 minutes for MHRFV and all others respectively with a shared median of 31 minutes. As expected with our large sample (n = 47,831), all variables of interest were significantly associated with WT. Adjusting for patient and hospital level characteristics, a multivariate regression revealed that MHRFV prolonged WT by about 50%. After adjustment for independent variables, interactions with MHRFV were tested as the main outcomes of interest. Blacks with MHRFV had WTs 62% longer, patients age 41-64 31% longer, payer status of Medicare/Medicaid or no coverage had WTs about 24% and 14% longer than private insurance. Conversely, patients at government owned hospitals had WTs 145%, and non-profits 42%, lower than private hospitals. CONCLUSIONS: This is the first time that ED WT has been examined in this depth with a sample of patients presenting with MH issues. The results indicate that disparities are more pronounced in this subgroup of ED patients. / 2031-01-01

Reducing ED wait times is a top health care priority for the Ontario government and hospitals in Ontario are incentivised to meet provincial ED wait time targets. In this study, we considered the costs and benefits associated with increasing the number of acute-care beds to reduce the time an admitted patient spends boarding in the ED. A shorter hospital LOS has often been cited as a potential benefit associated with shorter ED wait times. We derived a multivariable Cox regression model to examine this association. We found no significant association between ED boarding times and the time to discharge. Using a Markov model, we estimated an increased annual operating cost of $2.1m to meet the prescribed wait time targets. We concluded that increasing acute-care beds to reduce ED wait times would require significant funding from hospitals and would have no effect on total length of stay of hospitalized patients.