title: Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data.
abstract: 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).
authors.affiliation: Division of Management Consulting, Department of Anesthesia, The University of Iowa, Iowa City, IA 52242, USA. franklin-dexter@uiowa.edu
authors.forename: Franklin
authors.initials: F
authors.lastname: Dexter
authors.forename: Johannes
authors.initials: J
authors.lastname: Ledolter
mesh_terms: D000465:Algorithms; D001071:Appointments and Schedules; D001499:Bayes Theorem; D006785:Hospitals, University; D009873:Operating Rooms / Q000458:organization & administration*; D011336:Probability; D013514:Surgical Procedures, Operative / Q000706:statistics & numerical data*