title: Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and "instant messaging" updates from anesthesia providers.
abstract: BACKGROUND
  Operating room (OR) whiteboards (status displays) communicate times remaining for ongoing cases to perioperative stakeholders (e.g., postanesthesia care unit, anesthesiologists, holding area, and control desks). Usually, scheduled end times are shown for each OR. However, these displays are inaccurate for predicting the time that remains in a case. Once a case scheduled for 2 h has been on-going for 1.5 h, the median time remaining is not 0.5 h but longer, and the amount longer differs among procedures.
  
  
  METHODS
  We derived the conditional Bayesian lower prediction bound of a case's duration, conditional on the minutes of elapsed OR time. Our derivations make use of the posterior predictive distribution of OR times following an exponential of a scaled Student t distribution that depends on the scheduled OR time and several parameters calculated from historical case duration data. The statistical method was implemented using Structured Query Language (SQL) running on the anesthesia information management system (AIMS) database server. In addition, AIMS workstations were sent instant messages displaying a pop-up dialog box asking for anesthesia providers' estimates for remaining times. The dialogs caused negotiated interruptions (i.e., the anesthesia provider could reply immediately, keep the dialog displayed, or defer response). There were no announcements, education, or efforts to promote buy-in.
  
  
  RESULTS
  After a case had been in the OR longer than scheduled, the median remaining OR time for the case changes little over time (e.g., 35 min left at 2:30 pm and also at 3:00 pm while the case was still on-going). However, the remaining time differs substantially among surgeons and scheduled procedure(s) (16 min longer [10th percentile], 35 min [50th], and 86 min [90th]). We therefore implemented an automatic method to estimate the times remaining in cases. The system was operational for >119 of each day's 120 5-min intervals. When instant message dialogs appearing on AIMS workstations were used to elicit estimates of times remaining from anesthesia providers, acknowledgment was on average within 1.2 min (95% confidence interval [CI] 1.1-1.3 min). The 90th percentile of latencies was 6.5 min (CI: 4.4-7.0 min).
  
  
  CONCLUSIONS
  For cases taking nearly as long as or longer than scheduled, each 1 min progression of OR time reduces the median time remaining in a case by <1 min. We implemented automated calculation of times remaining for every case at a 29 OR hospital.
authors.affiliation: Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, IA 52242, USA. Franklin-Dexter@UIowa.edu
authors.forename: Franklin
authors.initials: F
authors.lastname: Dexter
authors.forename: Richard H
authors.initials: RH
authors.lastname: Epstein
authors.forename: John D
authors.initials: JD
authors.lastname: Lee
authors.forename: Johannes
authors.initials: J
authors.lastname: Ledolter
mesh_terms: D000758:Anesthesia; D001071:Appointments and Schedules; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D005544:Forecasting; D006282:Health Personnel / Q000706:statistics & numerical data; D006801:Humans; D019451:Information Management; D009873:Operating Rooms / Q000458:organization & administration* / Q000706:statistics & numerical data*; D012984:Software; D013514:Surgical Procedures, Operative / Q000706:statistics & numerical data*; D013997:Time Factors