Key result
A predictive model for preoperatively identifying patients at risk for same day cancellation of elective surgery was developed, though accurate prediction remains challenging.
Why the study?
Same-day surgical cancellations adversely affect patients and healthcare systems, but relatively little is known about risk factors compared to cancellation reasons.
Does a predictive model accurately identify patients at risk for same day cancellation of elective surgery?
Observational
Does a predictive model accurately identify patients at risk for same day cancellation of elective surgery?
Predictive models may aid in the early identification of patients at risk for same day cancellation of elective surgery.
Hypothesis-generating for preoperative cancellation risk models; prospective validation needed before clinical consideration.
Same day cancellations of surgery have adverse effects on both patients and health care systems. To date, the majority of research has evaluated reasons for same day cancellation, and relatively little is known about risk factors for cancellation. The aim of this study is to develop and evaluate the accuracy of a model for preoperatively predicting which patients are at risk for experiencing same day cancellation. While accurately predicting which patients are likely to experience same day cancellation remains challenging, predictive models may aid in the early identification of patients at risk for cancellation. Future studies are required to assess whether the use of predictive analytics leads to reduced cancellation rates in practice.
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Turcotte et al. (2023) conducted an observational in Elective surgery. Predictive model for same day cancellation was evaluated on Same day cancellation. A predictive model for preoperatively identifying patients at risk for same day cancellation of elective surgery was developed, though accurate prediction remains challenging.
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