Reducing Start Time Delays in Operating Rooms

Abstract: Inefficient utilization of operating rooms is a major problem in hospitals worldwide. A study of 13 hospitals in Belgium and the Netherlands showed that surgery began consistently late. Two hospitals were selected to record the start times for the first..

Figures

FIGURE 2. CTQ Flow Down.

FIGURE 4. Pareto Chart of the First Operations Stratified by Anesthesia Technique.

FIGURE 5. Two-Way Pareto Chart of Anesthesia Technique Used by Specialty.

FIGURE 11. Alternative Box–Cox Transformation for One-Way ANOVA Anesthesia Techniques.

FIGURE 1. Overview Process Map for a Surgical Procedure.

FIGURE 6. Time Series Plot of the Delay in Start Times for All Nine Operating Rooms at RCH.

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Reducing start time delays in operating rooms
Does, R.J.M.M.; Vermaat, T.M.B.; Verver, J.P.S.; Bisgaard, S.; van den Heuvel, J.
Publication date
2009
Published in
Journal of Quality Technology
Link to publication
Citation for published version (APA):
Does, R. J. M. M., Vermaat, T. M. B., Verver, J. P. S., Bisgaard, S., & van den Heuvel, J.
(2009). Reducing start time delays in operating rooms.
Journal of Quality Technology
,
41
(1),
95-109. http://www.asq.org/pub/jqt/
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CASE STUDIES
Edited by Patrick D. Spagon
Reducing Start Time Delays in
Operating Rooms
RONALDJ.M.M.DOESandTHIJSM.B.VERMAAT
Institute for Business and Industrial Statistics (IBIS UvA), University of Amsterdam
Plantage Muidergracht 24, 1018 TV Amsterdam, The Netherlands
JOHN P. S. VERVER
Red Cross Hospital, PO Box 1074, 1940 EB Beverwijk, The Netherlands
SØREN BISGAARD
Institute for Business and Industrial Statistics (IBIS UvA), University of Amsterdam
Plantage Muidergracht 24, 1018 TV Amsterdam, The Netherlands, and
Isenberg School of Management, University of Massachusetts, Amherst, MA
JAAP VAN DEN HEUVEL
Canisius Wilhelmina Hospital, PO Box 9015, 6500 GS Nijmegen, The Netherlands
Problem: Health care today is facing serious problems: quality of care does not meet patients’ needs
and costs are exploding. Inefficient utilization of expensive operating rooms is one of the major problems
in many hospitals worldwide. A benchmark study of 13 hospitals in the Netherlands and Belgium showed
that, for a variety of reasons, surgery consistently started too late.
Approach: For a short and a somewhat longer period, two selected hospitals from the benchmark
study agreed to record the start times of the first operation each day for each of their operating rooms.
In addition to start times, the improvement team also recorded potential influence factors (covariates, or
X’s). Statistical techniques used during the project were statistical graphics, Pareto charts, histograms, box
plots, time-series plots, Box–Cox transformations, and ANOVA to determine possible influential factors.
Results: It is shown that anesthesia technique and specialty are influence factors. However, the poor
planning and scheduling process turned out to be the most important factor in the delay of start times.
After introducing a new planning process, the hospitals involved were able to gain substantial cost savings,
increased efficiency, and substantial reductions of the delay in start times for surgery.
Key Words: ANOVA; Box–Cox transformation; Health Care Quality; Lean Six Sigma; Pareto Charts.
Vol. 41, No. 1, January 2009 95 www.asq.org

96 RONALD J. M. M. DOES, ET AL.
Process Description
H
EALTH CARE worldwide is facing serious prob-
lems. Costs are escalating and quality of care
often fails to meet expectations, see, e.g., Institute
of Medicine (2001). Improving quality while reduc-
ing costs is, or should be, a major strategic prior-
ity for health care organizations. It may strike the
uninitiated as a contradiction, but quality improve-
ment projects applied to health care processes can
simultaneously produce reductions in costs while in-
creasing quality. This study provides an example of
this empirical finding.
In this case study, we describe how Six Sigma
projects carried out at two hospitals helped to im-
prove the efficiency of operating rooms (see the brief
introduction of Six Sigma in the Appendix). The first
hospital was the Red Cross Hospital (RCH) in Bever-
wijk, the Netherlands, a 384-bed medium size hospi-
tal with a staff of 1,250 and a yearly budget of $95
million. In addition to being a general hospital, the
RCH is also the site of a national burn care center
with 25 beds that provides specialized services to all
of the Netherlands. The RCH introduced Six Sigma
in 2002 and five groups, each of about 15 green belts
(GBs), were trained during the first 3 years; see Van
den Heuvel et al. (2005). The second hospital was the
Canisius Wilhemina Hospital (CWH) in Nijmegen,
the Netherlands. This is a much larger hospital, with
653 beds, employing 3,200, with a yearly budget of
$185 million. The CWH started to implement Six
Sigma in early 2005. The introduction of Six Sigma
at CWH was guided by the same leadership team
previously responsible for the implementation at the
RCH. Initially, two groups of 20 GBs were trained.
In 2006, a second wave of GBs were trained; see Van
den Heuvel et al. (2006). At both hospitals, the GB
Dr. Does is Managing Director of IBIS UvA and Profes-
sor in Industrial Statistics. He is a Fellow of ASQ. His email
address is r.j.m.m.does@uva.nl.
Dr. Vermaat was Senior Consultant in Statistics at IBIS
UvA. His email address is thijs.vermaat@tntpost.nl.
Mr. Verver was Master Black Belt at the Red Cross Hos-
pital. His email address is jps.verver@gmail.com.
Dr. Bisgaard is Professor in Integrative Studies and Indus-
trial Statistics. He is a Fellow of ASQ. His email address is
bisgaard@som.umass.edu.
Dr. Van den Heuvel was Chairman of the Board of the
Canisius Wilhelmina Hospital. He is a member of ASQ. His
email address is Heuvel@rdgg.nl.
training included instructions in the use of the statis-
tical software package Minitab. Thus, the statistical
analyses and graphs shown below are all prepared
with Minitab.
Operating rooms are expensive and capacity lim-
iting facilities in hospitals. Their optimal utilization
is key to efficient hospital management. The RCH
and the CWH have 9 and 13 operating rooms, re-
spectively. To illustrate the business case for focusing
on operating rooms, suppose the official start time is
8:00 am but the actual average start time is 8:40 am.
An average of 40 minutes may not sound extraor-
dinary, but for a hospital with 13 operating rooms
and an average of 250 days in a year, this adds up
to about 2,150 lost hours or 270 full days that could
be used for productive work. In the Netherlands, the
cost of an operating room is estimated to be approx-
imately $1,500 per hour. Hence, 2,150 lost hours is
equivalent to $3.2 million per year and more than
the full capacity of one entire operating room. Fur-
thermore, operating rooms in modern hospitals are
capital intensive units, staffed by highly skilled and
expensive staff. Starting too late means considerable
wait time for staff and patients. Waste and ineffi-
ciency on such a scale when there are waiting lists
for surgery ought not be tolerated.
For those reasons, both hospitals decided to im-
prove the efficiency of their operating rooms. In the
following, we mainly report on the statistical aspects
of the project at the RCH without dwelling on de-
tails that otherwise may be relevant for discussing a
Six Sigma case or for more general issues related to
the application of Six Sigma in health care.
One of the first steps in an improvement project
is to describe the process with a process map or flow
chart. Figure 1 shows a much simplified process map
describing the major process steps a typical patient
undergoing surgery goes through.
Note that an operation is defined here to start
after anesthesia. Hence, the operational definition of
start time is at the time of the incision.
Data Collection
The measure phase starts with the selection of
critical-to-quality (CTQ) characteristics; see, e.g., De
Mast et al. (2006). A commonly used tool to guide a
team from the project definition to specific and mea-
surable CTQs is the CTQ flow down (CTQF); see,
e.g., De Koning and De Mast (2007). The CTQF
helps structure the logic underlying a project. Fur-
Journal of Quality Technology Vol. 41, No. 1, January 2009

REDUCING START TIME DELAYS IN OPERATING ROOMS 97
FIGURE 1. Overview Process Map for a Surgical Procedure.
thermore, it shows how CTQs relate to higher level
goals, such as performance indicators, and helps the
team align the project with the organization’s strate-
gic goals. At a lower level of the hierarchy, it shows
how the CTQs are related to measurements. Figure
2 shows the CTQF for this project.
The primary stakeholder in this case is the hospi-
tal and the strategic goal is the overall reduction of
the cost of running the hospital. Further, the CTQF
shows that efficiency (i.e., the key performance indi-
cator, KPI) can be divided into the number of op-
erations and the amount of unused time of an oper-
ating room; in Figure 2, we denote these quantities
by PI (performance indicators). The PI “amount of
unused time” is related to two one-dimensional mea-
surements (the CTQs). In the present project, the
CTQs “start time of the first operation” and “chang-
ing time of operations” are used. In what follows, we
focus on the start time of the first operation.
The next step of the measure phase is to develop a
precise description of the measurement plan. For each
operation, we collected a number of time stamps and
important characteristics (covariates, or X’s), such
as type of anesthesia technique and type of specialty.
For each operating room and for each first operation,
we recorded the following:
1. Official start time (i.e. the target time of the
incision).
2. Time of arrival at the front door of the operat-
ing room of the first patient.
3. Time of arrival in the operating room of the
first patient.
4. Time anesthesia starts.
5. Time incision starts.
6. Time surgery ends.
7. Time patient leaves operating room.
FIGURE 2. CTQ Flow Down.
Vol. 41, No. 1, January 2009 www.asq.org

98 RONALD J. M. M. DOES, ET AL.
FIGURE 3. Pareto Chart of the First Operations Stratified by Specialty.
8. Anesthesia technique.
9. Specialty.
The delay in start time was defined as the start
time of the incision minus the official start time. With
this operational definition in place, we see that the
overriding purpose of the project is to decrease this
delay. The measurement unit is minutes. As we indi-
cate below, occasionally some of the characteristics
were not recorded. Such cases were in the subsequent
data cleaning process labeled as “missing”.
Analysis and Interpretation
Before we engage in the analysis of the start
times, it is useful to perform a Pareto analysis of
the data collected. The first surgeries can be cate-
gorized according to the methods of anesthesia used
and the medical specialty performing the operation.
The method of anesthesia involved 10 different types
and the operations were performed by 11 different
specialist departments. Figure 3 shows a Pareto chart
of the specialties.
We see that more than 30% of all first operations
were performed by the Surgery Department. Plas-
tic surgery is another large category, with more than
20%, and the Orthopedic Department contributed
about 12%. The remaining approximately 38% of the
(first) operations came from eight other departments,
each contributing a relatively low volume of patients.
Thus, any effort to improve start time should initially
be focused on working with the large volume depart-
ments.
A second Pareto analysis shown in Figure 4 pro-
vides a break down of the first surgeries on the type
of anesthesia technique used.
From this chart, we see that the overwhelming
majority (63%) of first operations used total (com-
plete) anesthesia. Another approximately 20% used
spinal anesthesia. The remaining 17% of first oper-
ations used a variety of seven other methods. Thus,
again it would be useful to focus effort on improving
start time on total anesthesia and spinal anesthesia.
Finally, Figure 5 shows a two-way Pareto chart of
anesthesia technique used by specialty.
The main observation from Figure 5 is that the
two major specialties, surgery and plastic surgery,
primarily use full anesthesia, whereas the orthopedic
department rarely uses full anesthesia but primarily
prefers to use spinal anesthesia. Also, we see that
ophthalmology is, with few exceptions, the only user
of bulbar.
We now turn to an analysis of the actual start
time. The data collected showed that the average
start times were 8:35 am and 8:55 am at the RCH
and the CWH, respectively. The target start time of
the incision at the RCH was 8.00 am and 8.30 am at
the CWH. Figure 6 is a time series plot of the delay
in start times at RCH during a period of 6 months
in all nine operating rooms.
A visual screening of the data in Figure 6 reveals
a nonsymmetrical distribution of the times, with a
tendency to extreme outliers on the high side—a phe-
nomenon typical of waiting times. This distributional
Journal of Quality Technology Vol. 41, No. 1, January 2009

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Reducing start time delays in operating rooms" ?

In this paper, the authors used the Six Sigma approach with the accompanying thorough data analysis to reduce the classical `` blaming the other party '' problem that often derails problem solving processes. 

Q2. What is the first subtlety that the authors have often seen overlooked in (Six Sigma)?

The first subtlety that the authors have often seen overlooked in (Six Sigma) teaching is that, to apply the Box–Cox transformation algorithm for the determination of λ, the authors need to carefully consider the expected value of the model the authors want to estimate; the transformation is applied to transform the error, not the response, to approximate normality. 

Q3. How long did the participants collect data?

To stimulate the discussion, the authors asked the participants to collect data for a period of 4 weeks on start times of the operating rooms in their hospitals. 

Q4. How many green belts were trained at the RCH?

The RCH introduced Six Sigma in 2002 and five groups, each of about 15 green belts (GBs), were trained during the first 3 years; see Van den Heuvel et al. (2005). 

Q5. Why is analysis of variance a special case?

Because analysis of variance is a special case of regression analysis where the X’s are indicator variables (see e.g., Draper and Smith (1998)), the authors can create indicator variables for the 11 specialties and the 10 anesthesia techniques. 

Q6. How many other departments contributed to the first operations?

The remaining approximately 38% of the (first) operations came from eight other departments, each contributing a relatively low volume of patients. 

Q7. What is the main observation from Figure 5?

The main observation from Figure 5 is that the two major specialties, surgery and plastic surgery, primarily use full anesthesia, whereas the orthopedic department rarely uses full anesthesia but primarily prefers to use spinal anesthesia. 

Q8. What is the simplest way to compute the geometric average?

Vol. 41, No. 1, January 2009 www.asq.orgNote that a simple way to compute the geometric average ẏ = (y1y2 · · · yn)1/n is by using ẏ = exp{ln(ẏ)}, where ln(ẏ) = n−1 ∑ni=1 ln(yi). 

Q9. How many zeros are excluded from the data?

Note that the authors have excluded the 20 zeros out of 4,318 data points (3 from hospital 3, 2 from hospital 8, and 15 from hospital 10; in the data set, they are denoted by 0.01).