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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.
Aug-12 2924
Sep-12 2448
Oct-12 2813
Nov-12 2673
Dec-12 2565
Jan-13 2813
Feb-13 2491
Mar-13 2678
Apr-13 2837
May-13 2754
Jun-13 2688
Jul-13 2815
Aug-13 2879
Actual
3051
2489
2912
2694
2605
2833
2558
2601
2826
2915
2802
2884
3019
Diff.
127
41
99
21
40
20
67
-77
-11
161
114
69
140
% Diff.
4.3%
1.7%
3.5%
0.8%
1.6%
0.7%
2.7%
-2.9%
-0.4%
5.8%
4.2%
2.5%
4.9%
3
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 5th, 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
12
How is the model’s output used?
Purpose
Organization
Supply &
Processes
1. Case cart preparation center
2. Inpatient bed management
Staffing
3.
4.
5.
6.
Anesthesiology Dept.
OR Managers
Surgical Pathology Labs
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
Distribution of Case Bookings from
30 days prior to DoS
~20%
~20%
100%
99%
95%
91%
90%
89%
86%
83%
72%
63%
52%
45%
34%
21%
99%
99%
98%
98%
94%
90%
86%
80%
73%
66%
56%
40%
20%
100%
99%
95%
92%
90%
88%
82%
68%
51%
34%
21%
99%
99%
99%
97%
96% 99%
87% 99%
77% 91%
66% 84%
51% 76%
40% 68%
25% 54%
38%
21%
100%
98%
97%
92%
86%
82%
72%
51%
32%
19%
99%
98%
97%
92%
78%
58%
38%
24%
99%
99%
98%
94%
86%
68%
51%
38%
25%
100%
99%
98%
95%
85%
75%
60%
30%
100%
99%
92%
84%
71%
11%
99%
97%
53%
100%
95%
90%
85%
80%
75%
70%
65%
99%
95%
94%
88%
85%
79%
73%
68%
56%
49%
38%
30%
25%
60%
92%
86%
77%
69%
61%
57%
49%
43%
29%
55%
72%
62%
53%
45%
36%
34%
27%
50%
39%
29%
22%
45%
40%
~20%
35%
~20%
20%
10%
15%
~20%
30%
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
25%
Number of days before the day of surgery
(Tminus30 = 30 days before the day of surgery)
5%
% of the Final Count
Dist.
60%
66%
77%
50%
63%
65%
71%
55%
64%
49%
61%
69%
74%
63%
78%
59%
78%
63%
78%
59%
73%
60%
78%
66%
76%
75%
54%
73%
66%
96% 85%
20
Model 3: Percentage of Final Count
21
22
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