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Predicting Daily Surgical Case Volume

Predicting Daily Surgical Case Volume. March 28, 2014 Association of Anesthesia Clinical Directors Nashville, TN Vikram Tiwari, Ph.D . William R Furman, MD Warren S Sandberg, MD, Ph.D. Department of Anesthesiology, Vanderbilt University Nashville, Tennessee.

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Predicting Daily Surgical Case Volume

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  1. Predicting Daily Surgical Case Volume March 28, 2014 Association of Anesthesia Clinical Directors Nashville, TN Vikram Tiwari, Ph.D. William R Furman, MD Warren S Sandberg, MD, Ph.D. Department of Anesthesiology, Vanderbilt University Nashville, Tennessee

  2. Predicting Case Volume from the Accumulating Elective Operating Room Schedule Facilitates Staffing Improvements Vikram Tiwari, Ph.D., William R. Furman, M.D., and Warren S. Sandberg, M.D., Ph.D. (Accepted for publication: Anesthesiology)

  3. Research Motivation • Resources needed for DoS are planned usually weeks in advance • Variability in daily surgical case volume sub-optimizes resources day

  4. Daily surgical volume time-series

  5. How to predict the daily volume? • Elective case booking pattern – can that provide a signal?

  6. Research Questions Working from the elective schedule as it develops over time, whether: • surgical case volume can be predicted, if so, • with what confidence • how many days in advance • and, if the predictions could be used to flex staff up or down

  7. Linear trend of case bookings

  8. Days-Out Model

  9. Results / Model Output

  10. Putting the output to use – Daily Case Report For the first 15 days of July (excluding weekends), the model’s predictions (at TMinus5) were within +/- 7 cases of the final volume 80% of the time, whereas the budgeted volume was within +/- 7 cases 40% of the time.

  11. Early wins! • May – 2013 • identified volume shortfall 12 days in advance, arising due to all surgeons of a service attending a conference, without prior knowledge of the OR managers • July 5th, 2013 (Friday) • identified 4 weeks in advance that volume would be like a typical Friday, and no usual shortfall

  12. Spring Break Week of March 17, 2014

  13. How is the model’s output used?

  14. Replication • Children’s Hospital. Similar results. • Let’s collaborate to replicate this!

  15. Questions?

  16. Backup • An Empirical Approach to Predicting Case Volume from the Accumulating Elective Operating Room Schedule Facilitates Operationally Useful Staffing Improvements

  17. Research Motivation

  18. Methods: Linear models • Model 2  Linear trend model • discarded • Model 3  Percentage of final count • <brief description / figure 4?> • Model 4  Days-out model • <description, keep Figure 6 in backup>

  19. Methods: predicting daily volume • Model 1: time-series of daily surgical volume

  20. Distribution of Case Bookings from 30 days prior to DoS

  21. Model 3: Percentage of Final Count

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