A Bayesian prediction method accurately estimated operating room times to within 2% and predicted the probability of one case taking longer than another to within 0.7%.
Observational (n=65,661)
No
Does a Bayesian method accurately predict operating room times and prediction bounds for procedures with few or no historic data?
A Bayesian method accurately predicts operating room times and prediction bounds even when historic data for a specific surgeon and procedure are sparse.
BACKGROUND: Lower prediction bounds (e.g., for fasting), upper prediction bounds (e.g., to schedule delays between sequential surgeons), comparisons of operating room (OR) times (e.g., when sequencing cases among ORs), and quantification of case uncertainty (e.g., for sequencing a surgeon's list of cases) can be done accurately for combinations of surgeon and scheduled procedure(s) by using historic OR times. The authors propose that when there are few or no historic data, the predictive distribution of the OR time of a future case be centered at the scheduled OR time, and its proportional uncertainty be based on that of other surgeons and procedures. When there are a moderate or large number of historic data, the historic data alone are used in the prediction. When there are a small number of historic data, a weighted combination is used. METHODS: This Bayesian method was tested with all 65,661 cases from a hospital. RESULTS: Bayesian prediction bounds were accurate to within 2% (e.g., the 5% lower bounds exceeded 4.9% of the actual OR times). The predicted probability of one case taking longer than another was estimated to within 0.7%. When sequencing a surgeon's list of cases to reduce patient waiting past scheduled start times, both the scheduled OR time and the variability in historic OR times should be used together when assessing which cases should be done first. CONCLUSIONS: The authors validated a practical way to calculate prediction bounds and compare the OR times of all cases, even those with few or no historic data for the surgeon and the scheduled procedure(s).
Dexter et al. (Fri,) conducted a observational in Surgical cases (n=65,661). Bayesian prediction method was evaluated on Accuracy of prediction bounds. A Bayesian prediction method accurately estimated operating room times to within 2% and predicted the probability of one case taking longer than another to within 0.7%.