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

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)
2

Research Motivation
• Resources needed for DoS are planned usually weeks in advance
• Variability in daily surgical case volume suboptimizes resources day
Month Budg.
Actual Diff.
% Diff.
Aug-12 2924 3051 127 4.3%
Sep-12 2448 2489
Oct-12 2813 2912
41
99
1.7%
3.5%
Nov-12 2673 2694
Dec-12 2565 2605
21
40
0.8%
1.6%
Jan-13 2813 2833
Feb-13 2491 2558
20
67
0.7%
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%
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Daily surgical volume time-series
4

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

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 confidence
3. how many days in advance
4. and, if the predictions could be used to flex staff up or down
6

Linear trend of case bookings
7

Days-Out Model
8

Results / Model Output
9

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.
10

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 5 th , 2013 (Friday)
– identified 4 weeks in advance that volume would be like a typical Friday, and no usual shortfall
11

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 center
2. Inpatient bed management
Staffing
3. Anesthesiology Dept.
4. OR Managers
5. Surgical Pathology Labs
6. Case cart preparation center
13

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

Questions?
15

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

Research Motivation
17

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>
18

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

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10
9
8
21
20
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16
15
5
4
7
6
3
2
1
30
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25
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Distribution of Case Bookings from
30 days prior to DoS
% of the Final Count
~20%
~20%
~20%
~20%
~20%
39% 72% 92% 99%
29% 62% 86% 95%
22% 53%
45%
36%
34%
27%
77%
69%
61%
57%
49%
94%
88%
85%
79%
73%
99%
95%
91%
90%
89%
99%
99%
98%
43% 68% 86% 98%
29% 56% 83% 94%
100%
100%
Dist.
60%
66%
49% 72% 90% 99%
38% 63% 86% 95% 99%
30% 52% 80% 92% 99%
25% 45% 73% 90% 99%
34% 66% 88% 97%
21% 56% 82% 96% 99%
40% 68% 87% 99%
20% 51% 77% 91% 98%
34% 66% 84% 97%
21% 51%
40%
25%
76%
68%
54%
92%
86%
82%
100%
99%
98%
97%
99%
99%
38% 72% 92% 98%
21% 51% 78% 94% 99%
100%
32% 58% 86% 98%
19% 38% 68% 95%
24% 51% 85% 99%
38% 75% 92%
100%
66%
76%
75%
54%
25% 60% 84% 99% 73%
30% 71% 97% 66%
11% 53% 96% 85%
78%
63%
78%
59%
73%
60%
78%
49%
61%
69%
74%
63%
78%
59%
77%
50%
63%
65%
71%
55%
64%
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Model 3: Percentage of Final Count
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