Re-engineering the operating room using variability methodology to improve health care value.

1. Re-Engineering the Operating Room Using Variability
Methodology to Improve Health Care Value
C Daniel Smith, MD, FACS, Thomas Spackman, MD, Karen Brommer, RN, Michael W Stewart, MD,
Michael Vizzini, MBA, James Frye, MBA, William C Rupp, MD
BACKGROUND: Variability in flow of patients through operating rooms has a dramatic impact on a hospital’s
performance and finances. Natural variation (uncontrollable) and artificial variation (control-
lable) differ and require different resources and management. The aim of this study was to use
variability methodology for a hospital’s surgical services to improve operational performance.
STUDY DESIGN: Over a 3-month period, all operations at a referral center were classified as either scheduled
(artificial variation) or unscheduled (natural variation). Data regarding patient flow were
collected for all cases. From these data, mathematical models determined explicit resources to
be allocated for scheduled and unscheduled cases, with isolation of the 2 flow streams.
Services were allocated block time based on 80% prime time use, and scheduled cases were
capped at 5:00 PM. Guidelines for operating room access were implemented to smooth the
daily schedule and minimize artificial variation on the day of surgery. After implementation
of this redesign, 12 months of data were compared with the previous 12-month period.
Metrics analyzed included prime time use, overtime minutes, access for urgent or emergent
cases, the number of room changes to the elective schedule on the day of surgery, and
variation of daily schedules.
RESULTS: Surgical volume and surgical minutes increased by 4% and 5%, respectively. Prime time use
increased by 5%. Overtime staffing decreased by 27%. Day-to-day variability decreased by
20%. The number of elective schedule same day changes decreased by 70%. Staff turnover rate
decreased by 41%. Net operating income and margin improved by 38% and 28%, respectively.
CONCLUSIONS: Variability management results in improvement in operating room operational and financial
performance. This optimization may have a significant impact on a hospital’s ability to adapt
to health care reform. (J Am Coll Surg 2013;216:559e570. Ó 2013 by the American
College of Surgeons)
Our current health care system is heavily leveraged to
deliver complex care through a hospital system. This
model of care is inefficient, expensive, and unsustainable
in its current form. Preventative and predictive medicine
promise to improve an individual’s overall health while
moving much of this care out of hospitals and into
outpatient settings or even patients’ homes. Although
exciting, it will take decades before this promise can be
fully realized, and until then we will remain dependent
on our hospitals as substantial care delivery platforms.
The heavy dependence on hospitals is especially true
for the delivery of surgical care. Surgical care generates
substantial revenue for hospitals, but it is also one of
the largest drivers of cost within the hospital, and health
care reform’s mandate to cut costs while simultaneously
providing care to millions of currently uninsured Ameri-
cans will significantly affect our hospitals’ operating
rooms and surgical services.
Over the years, numerous studies have looked at
improving the efficiency of an operating room’s perfor-
mance.1-5
Most have tried to identify and eliminate waste
to improve throughput without increasing resources; put
simply, if cases start on time and room turnover time
is decreased, more cases will be completed in a day.
Disclosure Information: Dr Smith served as a consultant for the Institute
for Healthcare Optimization, a not-for-profit entity, and received hono-
raria for helping teach others the methodology used as part of the study
detailed in this article. All other authors have nothing to declare.
Presented at the Southern Surgical Association 124th Annual Meeting,
Palm Beach, FL, December 2012.
Received December 12, 2012; Accepted December 12, 2012.
From the Departments of Surgery (Smith), Anesthesiology (Spackman),
Ophthalmology (Stewart), and Medicine, Division of Oncology (Rupp);
Nursing Administration (Brommer); and Administration and Finances
(Vizzini, Frye); Mayo Clinic in Florida, Jacksonville, FL.
Correspondence address: C Daniel Smith, MD, FACS, Department of
Surgery, Mayo Clinic Florida, 4200 San Pablo Rd, Jacksonville,
FL 32224. email: smith.c.daniel@mayo.edu
559
ª 2013 by the American College of Surgeons ISSN 1072-7515/13/$36.00
Published by Elsevier Inc. http://dx.doi.org/10.1016/j.jamcollsurg.2012.12.046

2. Although these efforts remain important, and some
would argue that improving patient flow through an indi-
vidual operating room remains the holy grail of operating
room efficiency, managing the flow of surgical patients
into hospitals and operating rooms is a relatively unex-
plored area that could yield significant gains in operating
room performance. Tools used outside of health care help
industries such as manufacturing and telecommunica-
tions predict demand on resources and optimally manage
flow into a system to allow consistent product delivery.6-8
These efforts focus largely on understanding and
managing variability in demands on a system. Using these
concepts and tools is a promising way to redesign our
hospital management strategies and deliver high value
care consistent with health care reform mandates.7,9-11
To meet an increased demand for surgical services at
the Mayo Clinic Florida practice, construction of addi-
tional operating rooms was being seriously considered.
However, baseline data suggested that operating rooms
were being underused during regular working hours
(prime time), despite the incurrence of considerable over-
time. We hypothesized that by using operations manage-
ment principles and variability theory, we could expand
the capacity of our hospital’s operating rooms and
increase surgical throughput without adding infrastruc-
ture or expense. The aim of this project was to test this
hypothesis by designing and implementing a new oper-
ating room management strategy. Herein we present
a case study of this work with 1-year results.
METHODS
This project was undertaken in collaboration with the
Institute for Healthcare Optimization (Boston, MA,
www.ihoptimize.org), an independent not-for-profit
research, education, and service organization that uses
operations management principles and variability meth-
odology to help design strategies to manage patient
flow through hospitals. With the direction and full
support of the hospital’s CEO, goals with measurable
endpoints were established (Table 1). The focus of the
project was to manage the flow of surgical patients into
the hospital and operating rooms to optimize the use of
existing resources. This initiative was designated as the
“Managing Variability Program (MVP),” with the
“Program” designation to indicate its enduring presence
as opposed to a “project,” which is of finite duration.
The executive group that managed the day-to-day
operations of the operating rooms formed the program
team (Table 2). This is a subcommittee of the Surgical
Committee, which is composed of the chairs of all the
surgical departments and divisions and provides gover-
nance and approval for all activities related to the hospital’s
surgical services. Beginning in November 2009 and
extending through the implementation phase and first
year of management, this executive team met twice weekly
to design and implement the operating room redesign.
The program development was broken into 3 separate
but inter-related components: design, implementation,
and management. Each was pursued concurrently to
implement the redesign on November 1, 2010 and assess
its impact after 1 full year.
Design
Overall concepts of model
Design features are detailed in Table 3. The redesign of
the management of the operating rooms relied on under-
standing and defining variability in surgical patient flow.
Variability theory defines 2 types of variation: natural
variation (over which we have no control) and artificial
variation (which can be controlled). An example of
natural variation (an emergency or unscheduled case)
would be a patient presenting with an acute abdomen
requiring urgent surgery. Appropriate resources must be
available at all times to care for these unscheduled cases.
In contrast, artificial variation results from uneven sched-
uling of elective operations. This creates artificially light
and busy surgical schedules, which may vary considerably
from day to day. The scheduling of an elective case can be
managed according to pre-set clinical criteria, allowing
flexibility in creating an operating room schedule to
Table 1. Study Goals (Endpoints)
Primary goals (endpoints)
Increased surgical volume (no. of cases and minutes of surgery)
Decreased overtime (nonprime time minutes of surgery)
Maintain appropriate access for emergency surgery (classification
compliance)
Secondary endpoints
Predictable elective operating room schedule (no. of same day
changes to elective case schedule)
Assure surgeons work with their primary team (block utilization)
Staff satisfaction (staff turnover rate)
Financial impact (net operating income)
Table 2. Operating Room Redesign Team
Chair, Surgical Committee e Chair, Department of Surgery
Vice Chair, Surgical Committee e Chair, Department of
Anesthesiology
Member Surgical Committee e Chair, Department of
Ophthalmology
Associate Administrator, Surgery and Procedure Operations
Director, Surgical Services
Director, Systems and Procedures
Financial Analysts
Institute for Healthcare Optimization Team Members
560 Smith et al Operating Room Optimization J Am Coll Surg

3. best match fixed resources with the needs of the patient
and surgeon.
Unscheduled and scheduled surgical patients compete
for the same resources, but each represents a uniquely
different demand on hospital resources and patient
flow. Despite the distinctly different needs for each of
these cases, their scheduling into operating rooms is often
determined by patient and surgeon preference, and on the
day of surgery, managed on the fly by a “board runner.”
This leads to mixing of the scheduled and unscheduled
flow streams and resources, resulting in significant unpre-
dictability and unnecessary variability. To compensate for
this, operating rooms become chronically under- or over-
staffed and under- or overused, creating an expensive and
poorly used resource that leads to significant staff and
surgeon dissatisfaction.
At a very high level, once this definition of patient flow
variability is established and understood, a careful
accounting and analysis of a hospital’s specific case types
(scheduled vs unscheduled) and volume can be collected
and used for mathematical modeling of resource alloca-
tion (eg, rooms, staff, equipment). This allows separation
of the unscheduled cases and their resources from those
needed for elective cases.
This isolation of unscheduled from scheduled surgical
cases is an essential component of variability method-
ology. At its core, variability methodology involves iden-
tification, quantification, and elimination of artificial
variability so that the flow of elective patients can be
managed to optimize the operating rooms’ performance
and produce a smooth day-to-day schedule that is
predictable and reliable. If unscheduled cases are allowed
to blend into the elective schedule, the predictability and
reliability are lost, and if scheduled cases blend into the
resource allocated for unscheduled cases, access for those
unscheduled cases becomes blocked, inducing unaccept-
able delays for emergent care.
To fully model and redesign a surgical practice,
a comprehensive characterization of the surgical volume
is needed to do the mathematical modeling required for
decisions about resource allocation. In its most basic
form, this includes classifying all cases as either scheduled
or unscheduled, establishing urgency classifications for
unscheduled cases (Table 4), measuring the start and
end times of each case, and defining prime time (when
the regular work day starts and stops). With these data,
mathematical modeling creates numerous probability sce-
nariosdeach uniquely dependent on how resources are
allocateddwith risks calculated for each allocation
strategy. Risk is defined as not being able to accommo-
date all emergency surgery within urgency classifications
or bumping of elective cases. Examples of several risk
scenarios are shown in Table 5. Selection of an allocation
model must only be done once the various probability
and risk scenarios are considered. Once an allocation
model has been selected, case scheduling tools, daily
management strategies, and metrics with reporting tools
can be developed to facilitate implementation and
management of the program.
Mayo Hospital Specific Design
We defined an urgent/emergent case as one that must be
performed within 24 hours for clinical reasons. We sub-
divided the urgent/emergent case classification into 5
distinct urgency classifications (Table 4). When posting
an urgent/emergent case, the surgeon was asked to declare
the urgency classification. Cases that could wait more
than 24 hours, but needed to be completed within 5
days, were classified as work-in cases. Work-in cases
were further classified according to either clinical need
(eg, gallstone pancreatitis needing cholecystectomy before
discharge) or administrative reasons (eg, surgeon or
patient required surgery within 5 days but not for clinical
reasons). All other cases were classified as elective. We
defined prime time as 7:30 AM to 5:00 PM Monday
through Friday.
Data were then collected for 3 months (Table 6). No
changes to how the operating rooms were assigned or
managed were made during this 3-month data collection
phase. These data allowed characterization of the practice
Table 3. DesignFeaturesforOperatingRoomRe-Engineering
1. Understand and define variability in patient flow.
2. Isolate natural variation from artificial variation.
3. Define prime time and establish a “hard stop” to an operative
day.
4. Establish urgency classifications.
5. Collect prospective data based on preliminary definitions and
assumptions.
6. Use mathematical modeling and test probability scenarios to
decide on room allocations for elective and emergent cases.
7. Allocate service blocks based on actual service-specific needs.
8. Assign block time to effect smoothing of volumes throughout
week.
9. Re-evaluate staffing levels and tie to block time allocations.
10. Set expectations around block utilization thresholds to gain or
lose block.
Table 4. Urgency Classifications for Urgent and Emergent
Cases
A e must start within 45 min
B e must start within 2 h
C e must start within 4 h
D e must start within 8 h
E e must start within 24 h
Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 561

4. according to the categories and definitions used for the
redesign and future management.
After data collection was complete, modeling was per-
formed and probability scenarios, as outlined above and
depicted in Table 5, were considered. Rooms (including
staff, equipment, instruments, and supplies) were allo-
cated for urgent/emergent, work-in, or elective cases
(Fig. 1). These data were also used to allocate elective
rooms to the various surgical services as elective block
time. Sufficient operating room block time was assigned
to meet 125% of each service’s current demand. Put
differently, a service that continued its current volume
of work would use 80% of its elective block room alloca-
tion. After determining each service’s operating room
requirements, elective block was assigned to assure that
cases were evenly distributed throughout the week to
avoid disparate peaks and valleys in daily surgical volume
(ie, the weekly volume was “smoothed” to allow a more
predictable end to each elective day). This resulted in
an overall redesign of the operating room resource alloca-
tions based on understanding and managing variability,
to increase prime time capacity and use, and smooth
the weekly volume of elective surgery performed.
Implementation
Concurrent with the redesign efforts, tactics for effective
implementation of the redesign were developed. Although
beyond the scope of this manuscript, implementation
followed principles of quality improvement and change
management. All existing policies related to the day-to-
day function of the operating rooms were reviewed and
revised to be consistent with anticipated redesign. Consid-
eration was given to staged implementation by selected
services vs simultaneous implementation by all services.
To maximize the flexibility and impact of the redesign, it
was decided to implement the program for the entire
surgical practice at the same time. All policy changes and
resource allocations were vetted and approved by the
Surgical Committee (composed of the chairs of all surgical
departments and divisions) and the Executive Operations
Team of Mayo Clinic in Florida. The new program was
implemented on November 1, 2010.
Management
The design team served as the management team
(Table 2). Dashboards for day-to-day, weekly, monthly,
and rolling quarterly data were used (Fig. 2). Decisions
trees were developed to help manage conflicts and facili-
tate real-time decision-making regarding access to the
operating rooms. Anesthesia and Certified Registered
Nurse Anesthetist board runners were educated regarding
the principles of the program. Consistent with quality
improvement and change management principles,
changes in the program (based on feedback and data
analysis) were considered after 3 months.
Table 5. Examples of Probability Scenarios Used for Risk
Modeling and Choice of Redesign Plans
Cases included Variables
Scenario 3 - weekday prime time*
All urgent/emergent, rooms needed, n 1
Average room use, % 51.4
Case urgency classification Average waiting
time, min
A - within 45 min 92
B - within 2 h 107
C - within 4 h 132
D - within 8 h 171
E - within 24 h 268
Frequency of classification A bumps 1 every 2.7 wk
(30% of all A cases)
Scenario 1 - weekday prime timey
All urgent/emergent, rooms needed, n 3
Average room use, % 17.1
Case urgency classification Average waiting
time, min
A - within 45 min 1
B - within 2 h 1
C - within 4 h 1
D - within 8 h 1
E - within 24 h 1
Frequency of classification A bumps 1 every 167 wk
Scenario 15 - weekday prime timez
All urgent/emergent þ kidney
transplant, rooms needed, n 2
Average room utilization, % 33.5
Case urgency classification Average waiting
time, min
A - within 45 min 16
B - within 2 h 17
C - within 4 h 19
D - within 8 h 22
E - within 24 h 28
Frequency of classification A bumps 1 every 8 wk
*Isolating 1 room for all urgent/emergent cases.
y
Isolating 3 rooms for all urgent/emergent cases.
z
Isolating 2 rooms for all urgent/emergent cases.
Table 6. Preliminary Data Collection (3 Months)
Type of case e elective or urgent/emergent
Urgency classification if urgent/emergent: A, B, C, D, E
Urgent/emergent case request time
Wheels in time: time patient entered operating room
Wheels out time: time patient left operating room
562 Smith et al Operating Room Optimization J Am Coll Surg

5. RESULTS
Results are summarized in Table 7. One year after imple-
mentation of the redesign, both surgical volume (þ4%)
and surgical minutes (þ5%) had increased. Prime time
use increased by 5%, while overtime staffing decreased
by 27%. Day-to-day variability in case volumes and
minutes of surgery decreased by 20% and 22%, respec-
tively (Fig. 3), indicating a smoothing of the surgical
schedule. The number of same day changes to the elective
surgical schedule decreased by 70% (Fig. 4). A 41%
decrease in staff turnover suggested improved job satisfac-
tion (Fig. 5). These results were accompanied by
improvements in net operating income and net operating
margin (38% and 28%, respectively).
DISCUSSION
Optimizing the function of a hospital’s operating rooms is
critical to delivering safe, cost-effective surgical care. For
many, the focus of optimizing the performance of oper-
ating rooms has centered on increasing efficiency.5,12,13
Most attempts have tried to shorten the duration of oper-
ating room processes (eg, room turnover time) to create
capacity for additional surgical cases. LEAN and Six Sigma
are commonly used managerial techniques to eliminate
waste and improve efficiency.1,2
Although these improve-
ments are important, emerging concepts from nonhealth
care sectors centered around variability methodology
promise to expand capacity beyond what can be gained
by efficiently running the operating room. Variability
methodology aims to manage the flow of patients into
a hospital’s operating rooms and surgical services, as
opposed to flow through the operating rooms themselves.
To effect improvements, the required methodology,
processes, and metrics are vastly different from those that
improve efficiency within a single operating room. For
example, efficiency efforts geared to improving in-room
operating room performance include strategies such as
parallel processing, use of induction rooms, on-time starts,
and shortened room turn-over times.3–5
In contrast, vari-
ability methodology aims to isolate scheduled cases (artifi-
cial variation) from unscheduled cases (natural variation),
distribute scheduled cases throughout the week to smooth
the weekly volumes, and allocate appropriate resources for
unscheduled cases to avoid access restrictions.6,7,12,14,15
The
predictability and subsequent operational gains achieved
with this methodology create capacity otherwise consumed
by unmanaged artificial variation. This allows greater over-
all throughput (more surgical cases) without the addition of
incremental resources. Ideally, the 2 efforts, operating
room efficiency and variability management, are both
pursued and optimized.
This project and case study explored the use of variability
methodology to achieve the stated goals. The work encom-
passed not only application of this methodology, but also
the design, re-engineering, implementation, and subse-
quent impact of these concepts. To date, this is the most
comprehensive application of these concepts to a hospital’s
surgical services, and through this effort and experience,
significantly positive results were achieved.
The results regarding performance outcomes are self-
evident. Throughput was increased without incremental
expense, overtime was reduced, staff satisfaction was
improved, and the same day changes to scheduled cases
were significantly decreased, all while maintaining appro-
priate operating room access for urgent and emergent
Figure 1. Breakdown of room allocations for final redesign. CTS, cardiothoracic surgery; GS,
general surgery; H/L, heart/lung; NS, neurosurgery; Ortho, orthopedic surgery; Tx, transplant.
Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 563

6. cases. When taken together, these results led to improved
financial performance. Though not directly measured,
one could argue that this redesign should also lead to safer
surgery. Increasing prime time, service-specific block utili-
zation means surgeons are consistently working with their
usual teams, thereby enhancing team work. Furthermore,
by limiting the number of same-day changes to the elective
schedule, fewer cases are rerouted to rooms and teams not
previously expecting these cases, limiting the errors than
can accompany multiple “handoffs.”
Other significant consequences of this work not readily
evident from these data, and beyond the scope of this
manuscript, deserve mention. Many of these concern
the cultural change required to implement such
a program. Although the management concepts devel-
oped and used may appear obvious and simple to
someone knowledgeable and versed in these principles,
and the data are certainly compelling, the actual day-to-
day application of variability methodology is counterintu-
itive to how surgical practices and hospital systems have
Figure 2. Example of dashboard used for reporting metrics to leaders of surgical practice. MVP, managing vari-
ability program.
564 Smith et al Operating Room Optimization J Am Coll Surg

7. been structured. At its core, the cultural change asks
providers to transition from managing their practices,
and especially their surgical schedules, from what is best
for the surgeon and patient, to what is best for the
hospital. That’s not to say that the patient ceases to be
a focus of this redesign concept because the entire model
is built around defining the patient’s clinical needs and
assuring appropriate resources are available to meet those
needs. One could argue that this concept is very patient
centric in assuring the availability of the right team at
the right time to meet the patient’s surgical needs.
However, hospitals generally cater to the surgeon’s
Figure 2. Continued
Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 565

8. Table 7. Results: Changes in Operational Performance of Operating Room
Variable Pre-redesign Postredesign Change, %
Surgical cases, n 11,874 12,367 4
Surgical min 1,757,008 1,844,479 5
Prime time OR use, % 61 64 5
Number of overtime full time employees, average, n 7.4 5.4 À27
Staff turnover rate, % 20.3 11.5 À43
Daily case volume variation (upper-lower control limit) 55.24 44.06 À20
Daily case minutes variation (upper-lower control limit) 6,531 5,124 À22
Daily elective room changes, average/mo 80 25 À69
Daily elective room changes, % 8 2 À70
Cost/case (added 15 OR staff full time employees), $ 1,062 1,070 0
Cost/min of surgery (added 15 OR staff full time employees), $ 7.18 7.26 1
Staff turnover cost (millions), $ 2.47 1.40 À43
Overtime cost savings, $ 111,488
Total OR net revenue (fee increase adjusted), $ 93,929,569 98,686,693 5
Net operating income, $ 15,877,986 21,957,708 38
Net operating margin, % 17 22 28
OR, operating room.
Figure 3. Control charts showing change in variability after implementation of oper-
ating room redesign. LCL, lower confidence limit; UCL, upper confidence limit.
566 Smith et al Operating Room Optimization J Am Coll Surg

9. schedule to facilitate that surgeon delivering surgical care
in that hospital. This business model has worked for all
parties because it generally meets the patient’s needs,
enhances the surgeon’s ability to deliver care even when
faced with several competing demands, and keeps high-
revenue surgical care at the given hospital. The hospital’s
revenue has been sufficiently favorable to allow a “what-
ever, whenever” culture for the surgeon and still maintain
good operating margins.
Today, those favorable margins enjoyed by hospitals
are evaporating, forcing hospitals to cut costs while main-
taining high-quality outcomes. At the same time, these
quality-focused outcomes are becoming increasingly scru-
tinized and will soon factor into reimbursement formulas.
Hospitals across the country are aggressively pursuing
cost-cutting strategies, and the high-value, high-cost envi-
ronment of the operating room is a prime target for cost
reduction. Applying variability methodology swings the
pendulum for access to the hospital’s operating rooms
from “whatever and whenever” the surgeon wants, to
what is best for the hospital. Put more directly, in this
model, the surgeon is asked to compromise to meet the
hospital’s financial needs. The resultant tension between
a surgeon and hospital administration can become intense
and was certainly present during the redesign and imple-
mentation detailed in this case study. Before embarking
on such a program and applying variability methodology,
it is critical that a detailed assessment of the hospital’s
culture, its providers, and their willingness to accept
change be performed. Process improvement and change
management strategies and tools should be assessed and
liberally applied because gains like those demonstrated
here may take considerable time to realize. Software
and information technology tools to help schedule
surgical cases within the redesign goals, and reporting
tools within a quantitative dashboard are essential to facil-
itate adoption of this program. Transparency regarding
leadership decisions and frequent feedback to all
providers about performance improvements should be
emphasized. Change management and analytics support
should be identified either internally or pursued exter-
nally before starting such a program.
Finally, the more commonly pursued efficiency efforts
remain an essential component to realizing the gains
possible with variability methodology. Perfect manage-
ment of the flow of patients into the surgical practice
without an efficient and well-run operating room will
produce suboptimal results. The perfect schedule that is
theoretically predictable and reliable will disintegrate if
patients cannot enter and exit operating rooms in an
expeditious and efficient manner. The methods described
Figure 4. Number of changes to elective surgical schedule on the
day of surgery before and after implementation of operating room
redesign. MVP, managing variability program.
Figure 5. Staff turnover rate and cost over first 12 months after implementation of operating room redesign.
Vol. 216, No. 4, April 2013 Smith et al Operating Room Optimization 567

10. here do not replace these important elements of a well-
managed suite of operating rooms.
CONCLUSIONS
In summary, we have shown that redesigning operating
room management around variability theory and meth-
odology allows increased throughput while increasing
prime time use, decreasing overtime, and improving staff
satisfaction. At the same time, day-to-day variability in
case volume and within-day changes to the elective
schedule are decreased, resulting in a more predictable
and reliable flow of cases through the operating rooms.
Overall, these improvements result in better financial
performance and support the hypothesis that more
surgical cases can be performed without incrementally
increasing the cost of delivering that care. This strategy
holds great promise for helping hospitals and surgeons
adapt to the challenges created by impending health
care reform.
Author Contributions
Studyconceptionanddesign:Smith,Spackman, Brommer,
Stewart, Rupp
Acquisition of data: Brommer, Vizzini, Frye
Analysisand interpretationofdata:Smith,Stewart,Vizzini,
Frye
Drafting of manuscript: Smith
Critical revision: Smith, Stewart, Vizzini
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Discussion
DR JULIE A FREISCHLAG (Baltimore, MD): I actually had
spoken to Dr Smith quite a bit when we, too, became involved in
this process and used the Institute for Healthcare Optimization to
improve our operating room variability at Johns Hopkins. We
also knew we were going to be moving into a new hospital and we
wanted to do things better before we moved. The 3 key things we
found in our process were preparation and buy-in of all the staff,
nursing, anesthesia, and surgery, and that months of collecting
data and meetings were very important. We have to have champions
in each area. And you have to stay focused on what’s best for the
patient, because, frankly, the way we run the OR is what’s best for
the surgeon. When are you available? When do you have clinic?
When do you have research? When are you out of town? We do
a lot of negotiation about whether or not the patient needs surgery
right away or not, and we have to be really transparent about the
real urgency of the case. Is the surgeon really available? You know,
most of us put in a slip and go do something else for 8 hours because
it’s never going to get on. And what were the reasons that the case
didn’t go as planned? A third of the time, it is the surgeon; a third
of the time, the patient; a third of the time, anesthesia and others.
And this is not for the faint of heart. Dr Smith and I have both
taken major body blows for doing this kind of process in an oper-
ating room. And when you redesign the culture and take away
block time, you can imagine how painful that will be. We, too,
now do 6 to 8 more cases a day. We did that even before we got
into the new operating room with the new capacity. And we
have seen similar decreases in costs and more efficiency and less
pain in getting that elective case not interrupted, that urgent case
on, and even work-ins, of which we have a lot.
Our elective rooms started off with more than 80% use, and
now it’s close to 95%. We have more block time to offer to others,
and we do take it away. The minute you’re under 80%, your block
time goes, because we have to have 95% use of block time. We have
5 emergent rooms that run about 60%.
568 Smith et al Discussion J Am Coll Surg