title: Predicting case volume from the accumulating elective operating room schedule facilitates staffing improvements.
abstract: BACKGROUND
  Precise estimates of final operating room demand can only be made 1 or 2 days before the day of surgery, when it is harder to adjust staffing to match demand. The authors hypothesized that the accumulating elective schedule contains useful information for predicting final case demand sufficiently in advance to readily adjust staffing.
  
  
  METHODS
  The accumulated number of cases booked was recorded daily, from which a usable dataset comprising 146 consecutive surgical days (October 10, 2011 to May 7, 2012, after removing weekends and holidays), and each with 30 prior calendar days of booking history, was extracted. Case volume prediction was developed by extrapolation from estimates of the fraction of total cases booked each of the 30 preceding days, and averaging these with linear regression models, one for each of the 30 preceding days. Predictions were verified by comparison with actual volume.
  
  
  RESULTS
  The elective surgery schedule accumulated approximately three cases per day, settling at a mean ± SD final daily volume of 117 ± 12 cases. The model predicted final case counts within 8.27 cases as far in advance as 14 days before the day of surgery. In the last 7 days before the day of surgery, the model predicted the case count within seven cases 80% of the time. The model was replicated at another smaller hospital, with similar results.
  
  
  CONCLUSIONS
  The developing elective schedule predicts final case volume weeks in advance. After implementation, overly high- or low-volume days are revealed in advance, allowing nursing, ancillary service, and anesthesia managers to proactively fine-tune staffing up or down to match demand.
authors.affiliation: From the Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, Tennessee.
authors.forename: Vikram
authors.initials: V
authors.lastname: Tiwari
authors.forename: William R
authors.initials: WR
authors.lastname: Furman
authors.forename: Warren S
authors.initials: WS
authors.lastname: Sandberg
mesh_terms: D000465:Algorithms; D001071:Appointments and Schedules; D003627:Data Interpretation, Statistical; D005544:Forecasting; D006801:Humans; D016014:Linear Models; D009870:Operating Room Information Systems; D009873:Operating Rooms / Q000458:organization & administration*; D010561:Personnel Staffing and Scheduling; D014193:Trauma Centers; D000078329:Workforce