Predicting Daily Surgical Case Volume

March 28, 2014Association of Anesthesia Clinical Directors

Nashville, TN

Vikram Tiwari, Ph.D. William R Furman, MD

Warren S Sandberg, MD, Ph.D.

Department of Anesthesiology, Vanderbilt UniversityNashville, Tennessee

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

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Research MotivationMonth Budg. Actual Diff. % Diff.Aug-12 2924 3051 127 4.3%Sep-12 2448 2489 41 1.7%Oct-12 2813 2912 99 3.5%Nov-12 2673 2694 21 0.8%Dec-12 2565 2605 40 1.6%Jan-13 2813 2833 20 0.7%Feb-13 2491 2558 67 2.7%Mar-13 2678 2601 -77 -2.9%Apr-13 2837 2826 -11 -0.4%May-13 2754 2915 161 5.8%Jun-13 2688 2802 114 4.2%Jul-13 2815 2884 69 2.5%

Aug-13 2879 3019 140 4.9%

• Resources needed for DoS are planned usually weeks in advance

• Variability in daily surgical case volume sub-optimizes resources day

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Daily surgical volume time-series

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How to predict the daily volume?

• Elective case booking pattern – can that provide a signal?

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Research Questions

Working from the elective schedule as it develops over time, whether: 1. surgical case volume can be predicted, if so, 2. with what confidence3. how many days in advance4. and, if the predictions could be used to flex

staff up or down

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Linear trend of case bookings

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Days-Out Model

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Results / Model Output

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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.

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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

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Spring Break Week of March 17, 2014

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How is the model’s output used?

Purpose Organization

Supply & Processes

1. Case cart preparation center2. Inpatient bed management

Staffing3. Anesthesiology Dept.4. OR Managers5. Surgical Pathology Labs6. Case cart preparation center

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Replication

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

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Questions?

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Backup

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

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Research Motivation

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• 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>

Methods: Linear models

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Methods: predicting daily volume

• Model 1: time-series of daily surgical volume

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Distribution of Case Bookings from 30 days prior to DoS

5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100% Dist.

30 39% 72% 92% 99% 60%29 29% 62% 86% 95% 66%28 22% 53% 77% 94% 99% 77%27 45% 69% 88% 95% 50%26 36% 61% 85% 91% 99% 63%25 ~20% 34% 57% 79% 90% 99% 100% 65%24 27% 49% 73% 89% 98% 71%23 43% 68% 86% 98% 55%22 29% 56% 83% 94% 64%21 49% 72% 90% 99% 49%20 38% 63% 86% 95% 99% 61%19 30% 52% 80% 92% 99% 69%18 ~20% 25% 45% 73% 90% 99% 100% 74%17 34% 66% 88% 97% 63%16 21% 56% 82% 96% 99% 78%15 40% 68% 87% 99% 59%14 20% 51% 77% 91% 98% 78%13 34% 66% 84% 97% 63%12 21% 51% 76% 92% 99% 78%11 ~20% 40% 68% 86% 98% 99% 100% 59%10 25% 54% 82% 97% 99% 73%9 38% 72% 92% 98% 60%8 21% 51% 78% 94% 99% 78%7 32% 58% 86% 98% 66%6 19% 38% 68% 95% 100% 76%5 24% 51% 85% 99% 75%4 ~20% 38% 75% 92% 54%3 25% 60% 84% 99% 73%2 30% 71% 97% 66%1 11% 53% 96% 85%

100%

% of the Final Count

Num

ber o

f da

ys b

efor

e th

e da

y of

sur

gery

(Tm

inus

30 =

30

days

bef

ore

the

day

of s

urge

ry)

~20%

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Model 3: Percentage of Final Count

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