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Preoperative waiting time quartiles associated with day-surgery no-show risk in 79,516 patients

Preoperative waiting time quartiles associated with day-surgery no-show risk in 79,516 patients
Photo by Ksenia Yakovleva / Unsplash
Key Takeaway
Note that preoperative waiting time quartiles may associate with day-surgery no-show risk in observational data.

This retrospective cohort study analyzed data from 79,516 patients undergoing day surgery. The primary exposure was preoperative waiting time, categorized into four quartiles (Q1-Q4). The main outcome measured was the risk of day-surgery no-shows. The study design is observational, which limits the ability to establish causal links between waiting duration and patient attendance.

Specific numerical results, p-values, and confidence intervals were not reported in the provided data. Consequently, the magnitude of the association between waiting time quartiles and no-show risk remains qualitative. Safety data, including adverse events or discontinuations, were not reported for this exposure. The study setting was day surgery, and the population consisted of patients scheduled for procedures in this specific context.

Key limitations include the lack of reported follow-up duration and the absence of specific numerical outcomes. As an observational study, residual confounding may exist. The findings suggest a potential association but require cautious interpretation regarding clinical practice. Further research with quantitative data is needed to define the precise impact of waiting times on no-show rates.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundNo-shows in day surgery represent a global challenge that undermines healthcare efficiency, leading to substantial resource waste and increased operational costs. Preoperative waiting time is a key factor influencing patient attendance. This study investigated the association between preoperative waiting time and the risk of day-surgery no-shows.MethodsThis retrospective cohort study included 79,516 day-surgery patients. Demographic characteristics, surgical information, and preoperative waiting time were extracted from the electronic medical record system. Patients were categorized into quartiles according to waiting time: Q1 (0–
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