Reporting and evaluating wait times for urgent hip fracture surgery in Ontario, Canada

Abstract

Background: Although a delay of 24 hours for hip fracture repair is associated with medical complications and costs, it is unknown how long patients wait for surgery for hip fracture. We describe novel methods for measuring exact urgent and emergent surgical wait times (in hours) and the factors that influence them.

Methods: Adults aged 45 years and older who underwent surgery for hip fracture (the most common urgently performed procedure) in Ontario, Canada, between 2009 and 2014 were eligible. Validated data from linked health administrative databases were used. The primary outcome was the time elapsed from hospital arrival recorded in the National Ambulatory Care Reporting System until the time of surgery recorded in the Discharge Abstract Database (in hours). The influence of patient, physician and hospital factors on wait times was investigated using 3-level, hierarchical linear regression models.

Results: Among 42 230 patients with hip fracture, the mean (SD) wait time for surgery was 38.76 (28.84) hours, and 14 174 (33.5%) patients underwent surgery within 24 hours. Variables strongly associated with delay included time for hospital transfer (adjusted increase of 26.23 h, 95% CI 25.38 to 27.01) and time for preoperative echocardiography (adjusted increase of 18.56 h, 95% CI 17.73 to 19.38). More than half of the hospitals (37 of 72, 51.4%), compared with 4.8% of surgeons and 0.2% of anesthesiologists, showed significant differences in the risk-adjusted likelihood of delayed surgery.

Interpretation: Exact wait times for urgent and emergent surgery can be measured using Canada's administrative data. Only one-third of patients received surgery within the safe time frame (24 h). Wait times varied according to hospital and physician factors; however, hospital factors had a larger impact.

Figure 1:

Cumulative percentage of patients with hip fracture by time elapsed (in h) from arrival at the emergency department (ED) to hospital admission (green line), hospital admission to undergoing surgery (red line) and arrival at the ED to undergoing surgery (blue line). One-third of patients (n = 14 174, 33.5%) underwent surgery within the safe time frame (24 h). The inset shows the exact proportion of patients receiving surgery by the time elapsed, illustrating that wait times for surgery for hip fracture follow a sinusoidal distribution.

Figure 2:

(A) Mean differences (in h, with 95% confidence intervals [CIs]) for each hospital from the average surgical delay in the cohort was estimated in a 3-level linear regression model, adjusted for patient case mix and surgeon random effects. We classified hospitals that were significantly more likely to have early surgery performed as “low” outliers (green) and those that were significantly more likely to have delayed surgery performed as “high” outliers (red). More than half of the hospitals (37 of 72, 51.4%) showed significant differences in the likelihood of delayed surgery not attributable to patient case mix (1 hospital fell outside the graph area (estimate = +90.1 h, 95% CI 77.2 to 103.0). We conducted the analysis for 42 025 patients (missing observations were excluded). (B) Mean differences (in h, with 95% CIs) for each surgeon from the average surgical delay in the cohort was estimated in a 3-level linear regression model, adjusted for patient case mix and hospital random effects. We classified surgeons who were significantly more likely to perform early surgery as “low” outliers (green) and those who were significantly more likely to perform delayed surgery as “high” outliers (red). Only 4.8% of the surgeons (25 of 522) showed significant differences in the likelihood of delayed surgery not attributable to patient case mix or hospital random effects. We conducted the analysis for 42 025 patients (missing observations were excluded). (C) Mean differences (in h, with 95% CIs) for each anesthesiologist from the average surgical delay in the cohort was estimated in a multilevel linear regression model, adjusted for patient case mix and hospital random effects. We classified anesthesiologists who were significantly more likely to enable early surgery as “low” outliers (green) and those who were significantly more likely to enable delayed surgery as “high” outliers (red). Only 0.2% of anesthesiologists (2 of 963) showed significant differences in the likelihood of delayed surgery not attributable to patient case mix or hospital random effects. We conducted the analysis for 11 343 patients who had preoperative anesthesia consultations (missing observations were excluded).