The American Journal of Surgery 187 (2004) 309 –315
Surgical education
Multiservice laparoscopic surgical training using the daVinci
surgical system
Eric J. Hanly, M.D.a,b,c,d, Michael R. Marohn, D.O.a,b,c, Sharon L. Bachman, M.D.d,
Mark A. Talamini, M.D.d, Sander O. Hacker, V.M.D.e, Robin S. Howard, M.A.f,
Noah S. Schenkman, M.D.a,b,*
a
Department of Surgery, Walter Reed Army Medical Center, 6900 Georgia Ave., NW, Washington, DC 20307-5001, USA
b
Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
c
Department of Surgery, Malcolm Grow Medical Center, Andrews AFB, MD, USA
d
Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
e
Department of Veterinary Surgery, Walter Reed Army Institute of Research, Bethesda, MD, USA
f
Division of Biostatistics, Department of Clinical Investigations, Walter Reed Army Medical Center, Washington, DC, USA
Manuscript received October 28, 2002; revised manuscript March 3, 2003
Abstract
Background: The daVinci surgical system affords surgeons a magnified three-dimensional videoscopic view of the operative field and
precise articulating laparoscopic instruments. The learning curve for this advanced surgical robotics system is poorly characterized.
Methods: Twenty-three surgeons representing seven surgical subspecialties participated in a surgical robotics training program consisting
of standardized daVinci system training (phase 1) followed by self-guided learning in a porcine model (phase 2).
Results: The average number of recorded procedures performed per surgeon during phase 2 was 5.5. The mean daVinci system set-up time
was 45 minutes and decreased by an average of 56.1% by the third successive set-up (r ⫽ ⫺0.702, P ⬍0.005). Operative times decreased
39.0% by the third successive practice operation (r ⫽ ⫺0.860, P ⬍0.0005).
Conclusions: New use of the daVinci robot is associated with a rapid learning curve and preclinical animal model training is effective in
developing surgical robotics skills. © 2004 Excerpta Medica, Inc. All rights reserved.
Keywords: Surgery; Education; Robotics; Laparoscopy; Learning curve; daVinci surgical system
The advantages of minimally invasive surgery are well
accepted. Shorter hospital stays, decreased postoperative
pain, rapid return to preoperative activity, decreased postoperative ileus, and preserved immune function are among
the benefits of the laparoscopic approach [1–7]. However,
conventional videoendoscopic surgery does have shortcomings. The surgeon’s two-dimensional view of a three-dimensional operative field and limited freedom of movement
within the abdominal or thoracic cavity pose unique challenges for surgeons and unique dangers for patients [8]. The
modified “chopstick” instruments of laparoscopy afford surgeons limited precision and poor ergonomics, and their use
is associated with a significant learning curve [9 –15].
* Corresponding author. Tel.: ⫹1-202-782-0615; fax: ⫹1-202-7824118.
E-mail address: noah.schenkman@na.amedd.army.mil
Nevertheless, the introduction of the laparoscopic approach marks one of the great advances in surgery. Conventional laparoscopy is excellent for basic procedures that
require minimal reconstruction. Virtually every general surgeon, young and old, has become adept at exploiting basic
laparoscopic skills for simple organ removal, such as in
laparoscopic cholecystectomy. Some surgeons have developed advanced laparoscopic skills including complex bimanual manipulation, suturing, and knot-tying, and are thus
able to perform advanced reconstructive surgery using minimally invasive approaches. The amount of time and energy
necessary to develop and maintain such advanced laparoscopic skills is not insignificant, and thus the majority of
advanced laparoscopic cases are performed by a small number of surgeons [8].
The daVinci surgical system (Intuitive Surgical, Mountain View, California) allows all laparoscopists to perform
0002-9610/04/$ – see front matter © 2004 Excerpta Medica, Inc. All rights reserved.
doi:10.1016/j.amjsurg.2003.11.021
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E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
Fig. 1. The daVinci surgical system consists of three main components: the
console, the laparoscopic tower, and the patient-side robot. Here a surgeon
practices an upper abdominal procedure in a pig during the self-guided
learning phase (phase 2) of the training protocol.
advanced laparoscopic procedures with greater ease. Dual
offset video cameras provide a three-dimensional view of
the operative field with adjustable magnification. Robotic
articulating laparoscopic instruments move with the same
number of degrees of freedom as human hands in open
surgery, and provide motion scaling and elimination of
surgeon tremor allowing an unparalleled level of operative
precision.
The daVinci surgical system consists of three main components: the console, the laparoscopic tower, and the patient-side robot (Fig. 1). The surgeon console provides the
interface between the surgeon and the robot. The surgeon
views the two video images converged in the viewfinder,
controls the instruments with hand controls, and operates
the camera and energy devices using foot pedals. The system’s configuration allows the primary surgeon to be in
direct control of three devices—two instruments and the
camera—whereas in conventional laparoscopy, the surgeon
can control a maximum of two devices. As depicted in Fig.
2, the system translates the natural movements of the surgeon’s hands into precise laparoscopic instrument movements inside the patient. The tower houses a monitor that
allows surgical assistants to view the operation, the light
sources for the cameras, the harmonic scalpel generator, the
insufflators, and the camera controls. The patient-side cart
or “robot” has three mechanically driven robotic arms: the
camera arm and two instrument arms.
The potential advantages of surgical robotic systems
like daVinci include making advanced laparoscopic surgical procedures accessible to surgeons who do not have
advanced videoendoscopic training and broadening the
scope of surgical procedures that can be performed using
the laparoscopic method. However, the learning curve associated with the introduction of a surgical robotic
system into a surgeon’s armamentarium is unknown. The
hypothesis of our study was that systematic training on
a new surgical robotic system in an animal model would
Fig. 2. The daVinci surgical system translates the natural movements of the
surgeon’s hands into precise laparoscopic instrument movements inside the
patient.
result in measurable improvement in robotic surgical
skills, and that surgeons would benefit from such preclinical
training. We sought to characterize the learning curve associated with new use of the daVinci surgical system by
surgeons from multiple surgical disciplines in a porcine
model.
Methods
Phase 1: daVinci system training
Surgeons from Walter Reed Army Medical Center, Uniformed Services University of the Health Sciences, Malcolm Grow Medical Center, and The Johns Hopkins University School of Medicine interested in receiving surgical
robotics training using the daVinci surgical system were
recruited for this educational protocol. Twenty-three surgeons were enrolled in the study and completed the first
phase of the training— daVinci system training. The daVinci system training is a Food and Drug Administration
mandated 2-day course for all users of the robot taught by
instructors from the makers of daVinci, Intuitive Surgical.
Topics covered in this training include system basics, draping procedures, console instruction, patient-side instruction,
patient positioning, and port placement. Participants engage
in both inanimate practice and an animal laboratory where
they discover the grip strength of each of the daVinci
instruments and practice two-handed dissection and manipulation of tissues, ligation and transection of vessels, and
intracorporeal suturing and knot-tying. The students are
tested on system troubleshooting and emergency conversion
(to open surgery) procedures in a hands-on practical examination, and must pass a 50-question multiple-choice written
examination to complete the course.
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E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
Table 1
Surgical subspecialty training background of participating surgeons in each study phase: number of procedures performed by each subspecialty
(percentage of total cases performed in parentheses)
Surgical subspecialty
Surgical subspecialty
Phase 1 surgeons
Phase 2 surgeons*
Number of procedures
General
4
3
16 (37%)
Urology
3
3
13 (30%)
Totals
CT
5
4
8 (19%)
Gyn
5
4
6 (14%)
ENT
2
—
—
Plastic
2
—
—
Neuro
2
—
—
7
23
14
43
* Excludes surgeons who engaged in self-guided practice without recording data.
CT ⫽ cardiothoracic; Gyn ⫽ gynecology; ENT ⫽ otolaryngology; Neuro ⫽ neurosurgery.
Phase 2: self-guided learning
The second phase of training was the unique portion of
our training protocol—self-guided learning in a porcine
model. In this phase of the training protocol, surgeons
practiced procedures that they anticipated later performing
in humans. Endpoints hypothesized to be associated with
learning and effective skills development were evaluated to
measure each surgeon’s progress. Each surgeon prospectively recorded their daVinci system set-up and operative
times, the number of instrument exchanges (ie, the number
of times that an instrument arm was disengaged to replace
the active instrument with a different instrument), the number of accessory ports used (in addition to the standard three
daVinci ports), and the number and description of complications for each procedure performed. In Fig. 1, a surgeon
is shown practicing an upper abdominal procedure in a pig
during this portion of the protocol.
set-up time, operative time, number of instrument exchanges, and number of accessory ports was analyzed using
Pearson’s correlation coefficient. The logarithmic transformation of both time variables (set-up and operative times)
was used in the analysis. Differences were considered significant when P ⱕ0.05, and all tests for significance were
two-tailed. Analyses were performed using SPSS (SPSS
Inc., Chicago, Illinois) and Excel (Microsoft Corp., Redmond, Washington) software.
Results
Participating surgeon demographics
All procedures were part of a protocol reviewed and
approved by both the Walter Reed Army Institute of Research (WRAIR) Institutional Animal Care and Use Committee and the Walter Reed Army Medical Center, Department of Clinical Investigations Institutional Review Board.
Yorkshire pigs, 30 to 150 kg in size, were housed in cages
where standard feed and water were available ad libitum.
The pigs were fasted for 12 hours prior to procedures. The
WRAIR veterinary team provided anesthesia according to
accepted guidelines. Pneumoperitoneum for abdominal procedures was achieved by insufflating the peritoneal cavity
with CO2 at a pressure of 9 to 10 mm Hg. Postoperatively,
animals were euthanized by lethal intravenous injection
using a commercially available euthanasia solution (eg,
Euthasol, 100 mg/kg) while still under general anesthesia.
The 23 surgeons who participated in phase 1 of the
training protocol (daVinci system training) represented
seven surgical subspecialties: cardiothoracic surgery, gynecology, general surgery, urology, otolaryngology, plastic
surgery, and neurosurgery (Table 1). Of these surgeons, 17
subsequently participated in self-guided practice in a pig
model (phase 2). Three of these 17 surgeons did not record
any data, and therefore their participation was excluded
from the final analysis. The average number of procedures
performed by the 14 surgeons from whom data was collected was 5.5. A total of 43 practice operations were
performed during phase 2 of the training (Table 1). Of these
43 procedures, 11 were performed by single surgeons, 30 by
two-surgeon teams, and 2 by three-surgeon teams. The
procedures performed included the following (number in
parentheses): cholecystectomy (10), prostatectomy (8), internal mammary artery take-down (6), tubal anastomosis
(6), Nissen fundoplication (3), bowel anastomosis (2), pericardial window (2), cystoureterostomy (2), adrenalectomy
(1), cystotomy repair (1), nephrectomy (1), and pyeloplasty
(1).
Data analysis
Learning curve parameters
To enable comparison of operative times for different
procedures and daVinci system set-up times for differentsize set-up teams, data for these parameters were expressed
as a percentage of a specific surgeon’s or team’s first attempt. The association between the number of attempts and
The daVinci system set-up time ranged from 120 minutes for a single-person set-up to 8 minutes for a threemember team on their fourth set-up. The mean system
set-up time was 45 minutes and the median set-up time was
40 minutes. Consecutive set-up events for same-member
Animal care
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E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
Fig. 3. Surgeons from several surgical subspecialties participated in a
surgical robotics training program with standardized daVinci system training (phase 1) followed by self-guided learning in a porcine model (phase
2). Phase 2 times for consecutive team-specific set-ups (diamonds) and
procedure- and surgeon-specific operations (squares) are expressed as
percentages of the first times in their respective series. Logarithmic regression lines for set-up times (black line) and operative times (gray line) are
shown. Both set-up and operative times decrease significantly (r ⫽
⫺0.702, P ⬍0.005; and r ⫽ ⫺0.860, P ⬍0.005, respectively) after a
limited number of attempts, indicating rapid learning among surgeons new
to surgical robotics.
teams (ie, teams of surgeons, nurses, and technicians with
the same members for each practice session) were analyzed
to elucidate changes in set-up times over the course of
training. Set-up times decreased by an average of 29.2% by
the second and 56.1% by the third successive set-up for each
same-member team’s successive set-up (r ⫽ ⫺0.702, P
⬍0.005). Operative times for consecutive surgeon- and procedure-specific practice cases were compared with evaluate
changes in operative speed over the course of training.
Operative times decreased by an average of 26.6% by the
second and 39.0% by the third successive practice operation
(r ⫽ ⫺0.860, P ⬍0.0005). Set-up and operative times
expressed as the average percent of surgeons’ first set-up
and first operative times plotted over the number of consecutive cases are shown in Fig. 3. Logarithmic regression lines
for these data demonstrate downward sloping curves for
both parameters indicating correlation between experience
and speed. The set-up and operative time outliers at the top
of Fig. 3 correspond, respectively, to instances in which a
team’s second set-up occurred a month after their first
set-up (while their first set-up occurred much closer to their
initial training), and in which a surgeon reported encountering a “gallbladder with atypical anatomy” during his third
robotic cholecystectomy.
The number of instrument exchanges (ie, the number of
times that a robotic arm was disengaged for removal and
replacement of a robotic instrument) was recorded for each
practice procedure. The number of instrument exchanges
per case ranged from 0 to 16 with a mean of 4.5 and median
of 4. There was no correlation between the number of
instrument exchanges per procedure and the amount of
daVinci-specific surgeon experience or the operative time of
consecutive cases. Accessory laparoscopic ports (beyond
the daVinci’s standard three) were often necessary to allow
a patient-side surgeon to assist the primary surgeon controlling the robot. The number of accessory ports used for each
procedure ranged from 0 to 3 with a mean of 1.2 and a
median of 1. There was no correlation between the number
of accessory ports necessary to perform each procedure and
the amount of daVinci-specific surgeon experience or the
operative time of consecutive cases. Linear regression analysis of instrument exchange and accessory port data revealed nearly flat slopes indicating little to no change in
these parameters over successive cases.
Complications were prospectively divided into five categories, and participating surgeons were asked to record the
number of each type of complication and to annotate each
complication. The five complication categories consisted of
(1) computer (problems attributable to the robot’s system
software); (2) robotic (problems attributable to the robot’s
hardware); (3) operative (problems attributable to the surgeon); (4) anesthetic (problems attributable to anesthesia);
and (5) material (problems attributable to lack of supplies).
Table 2 shows the number of complications reported in each
category for each of the two halves of the training period. In
each category of complication (except computer complications, of which there was only one), the number of complications was fewer during the second half of cases than
during the first half of cases.
During the 43 cases performed, there were a total of 10
operative complications. One unintended cystotomy, one
inadvertent ureteral ligation, and one inferior vena caval
injury were repaired laparoscopically using the robot, and
one episode of broad ligament bleeding was controlled with
the daVinci’s hook electrocautery. One case was delayed
when the pig was not properly positioned prior to connect-
Table 2
Number of complications reported in each category for each of the two halves of the training period
Complications
Computer
Robotic
Operative
Anesthetic
Material
Total
Entire training period
First half of training
Second half of training
1
0
1
6
4
2
10
7
3
6
4
2
5
3
2
28
18
10
Computer ⫽ problems attributable to the robot’s system software; robotic ⫽ problems attributable to the robot’s hardware; operative ⫽ problems
attributable to the surgeon; anesthetic ⫽ problems attributable to anesthesia; material ⫽ problems attributable to lack of supplies.
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E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
Table 3
Robotic-unique complications
Complication
Number of occurrences
Solution
Resultant Time Delay
System failure
“Frozen arm”
Malfunctioning grasper
Robotic arm failure
1
4
1
1
Replace motherboard
Reposition arm or master
Exchange instrument
Replace arm
6 hours
0–5 minutes
1 minute
24 hours
ing the robot to the laparoscopic trochars. In one case, a
needle was broken during bimanual robotic manipulation of
the needle. In two other cases, solid organ bleeding ensued
after excessive force application to patient-side organ retractors. Finally, two cardiac injuries occurred as a result of
traumatic thoracic trochar insertion in small pigs (approximately 20 kg each).
A total of seven complications considered unique to the
robotic aspect of the surgery being performed occurred
during the study (Table 3). Computer system failure on one
occasion prevented the start-up of the robot at the beginning
of a training day. This failure required replacement of the
system’s motherboard, which was accomplished by the Intuitive Surgical engineer within six hours. When a surgeon
attempts to move a robotic arm past its limit, the arm will
sometimes “freeze up” temporarily until the arm or the
control master is brought back into the normal range. Four
such events were reported and each was resolved in less
than five minutes. During one case, a training instrument
that had been used for more than the manufacturer’s recommended number of 10 uses began to malfunction (the
grasping mechanism became “sticky”). This instrument was
exchanged for a new instrument in less than one minute. On
one occasion early in the training protocol, one of the
robotic arms was damaged during set-up necessitating replacement of that arm, which was accomplished by the
Intuitive Surgical engineer within 24 hours.
The costs associated with both phases of our training
protocol totaled $10,355. This included $6,480 in animal
procurement costs, $932 in animal housing costs, and
$2,943 in supplies. This total does not include the indirect
costs of the protocol borne by the Walter Reed Army Institute of Research for veterinary and operating room support.
The total also does not include the cost of the daVinci
system itself.
Comments
While characterization of the learning curve associated
with standard laparoscopic instrumentation has been well
described [9 –15], descriptions of the learning curve associated with use of an advanced surgical robotics system are
relatively few in the surgical literature. Prasad et al [16]
compared learning using the Zeus robot (Computer Motion)
to learning using standard laparoscopic instruments. While
this study used standardized manual skills drills performed
in inanimate models and thus lacks direct applicability to
the clinical setting, it did demonstrate an early phase of
greater learning with robotic systems. Other researchers
have reported initially steep learning curves for specialtyand procedure-specific use of surgical robots [17–19]—a
finding that certainly warrants further investigation.
Our study sought to characterize the learning curve associated with new use of an advanced surgical robotics
system among surgeons from multiple surgical disciplines
in a more clinically relevant setting. Given the complexity
of this system and its need for significant preoperative
preparation, we felt it important to measure the time taken
for surgical teams to prepare the robot for surgery. In all but
one instance, same-member healthcare teams progressively
improved their set-up times— on average, by almost 30%
each time they prepared the system. While the average
set-up time in our study was 45 minutes— clearly a significant amount of time to add to “in-room” time in the clinical
arena— experienced teams were routinely setting up the
system in 20 minutes or less near the end of our training
period.
While set-up times are important from an operating room
cost perspective, patients are more directly affected by their
surgeons’ operative times. To analyze global changes in
surgeon operative times (rather than operative times for
specific procedures), surgeon- and procedure-specific operative times were normalized to each surgeon’s first operative time for that specific procedure. Surgeons clearly benefited from participation in our study as their operative
times decreased substantially (more than 20% each time
they practiced) throughout the training period, and the absolute operative times achieved by many of the surgeons
near the end of the training protocol were excellent. For
example, the average operative times for the last three
cholecystectomies and the last three prostatectomies performed during the training period were 37 minutes and 100
minutes, respectively.
Because learning curves for manual skills development
generally follow logarithmic kinetics [20], we chose to
analyze both set-up and operative times using logarithmic
regression analysis. The correlation between the log of the
times for these parameters over successive cases was noteworthy with correlation coefficients (absolute value) greater
than 0.7 for both parameters (0.860 for set-up time and
0.702 for operative time). Furthermore, the reduction in
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E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
both set-up and operative times for consecutive cases was
highly significant (P ⬍0.005 for set-up time and P ⬍0.0005
for operative time). Together these results indicate that
learning in our protocol as indicated by reduced set-up and
operative time was both predictable and significant.
We had hypothesized that as surgeons became more
experienced using a robotic surgical system they would
learn to save time and maximize economy of movement by
minimizing the number of instrument exchanges used for
each case. However, our data revealed poor correlation
between the number of robotic instrument exchanges and
operative time. Furthermore, increased daVinci-specific surgeon experience did not reduce the number of instrument
exchanges. We attribute these findings to the fact that in
each surgeon’s early experience with the robot, instrument
exchanges are considered significant events—that is, they
are viewed as time-consuming hassles. However, as surgeons grow familiar with the relatively simple instrument
exchange procedure, the process becomes less intimidating.
An experienced patient-side assistant can exchange a robotic instrument almost as quickly as conventional laparoscopic instruments can be exchanged from conventional
ports. Thus any reduction in instrument exchange rate that
might have occurred as a result of learning may have been
masked by decreasing mental resistance to instrument exchange on the part of the surgeon. An alternative explanation is that because the successful completion of every
surgical procedure requires that an inherent number of steps
be performed— each requiring a minimum number of instrument exchanges—surgeons may have used their standard steps in performing their robotic procedures thus predetermining the number of instrument exchanges that would
be required. The number of accessory laparoscopic ports
used by surgeons remained relatively constant, and, therefore, did not correlate with surgeon experience, or operative
time.
In an effort to obtain a detailed picture of problems
encountered during training, surgeons were encouraged to
be liberal in their inclusion of “complications.” For example, surgeons were encouraged to report instances of bleeding even when the bleeding was quickly controlled, and
surgeons were asked to report even minor “glitches” encountered when using the robot (eg, a malfunctioning instrument that was easily exchanged). Therefore, our absolute complication rate is much higher than would be the case
if standard clinical criteria had been used. We included an
anesthetic complication category to distinguish surgeonand robot-related problems from anesthesia misadventures,
and a material complication category primarily as a means
of identifying supplies that were in short supply in the
animal laboratory.
While the absolute number of complications recorded in
our protocol has little clinical relevance, a number of important observations can be made from the detailed complication data. Of the 43 cases attempted during our training
protocol, only two had to be abandoned because of robotic
equipment failure. Given the complexity of the daVinci
system, this seems to be an excellent reliability rate. If
robotic equipment fails in the clinical arena, procedures can
always be completed using standard laparoscopic or open
methods. Of the 10 operative complications, four are worthy
of discussion because they outline robot-specific issues of
which surgeons need to be aware to achieve safe and successful clinical use of the system. In one case, a surgeon was
delayed when he had to disconnect the robot from the
laparoscopic trochars in order to place the patient in Trendelenberg position. This case emphasizes the need to perform patient positioning prior to the engagement of the
relatively cumbersome daVinci robot. In a second case, a
surgeon broke a needle as he attempted bimanual manipulation of the needle. In two other cases, solid organ bleeding
(liver in one case, spleen in another case) ensued after
excessive force application to the patient-side organ retractor from the inadvertent bumping of the proximal portion of
a robotic instrument (out of the surgeon’s view) against the
retractor. While the broken needle in our study was simply
removed and replaced, and while retractor-related solid organ injury would be expected to be less likely in the clinical
arena where patient-side assistants hold and monitor retractors (because of manpower limitations in the laboratory, we
generally fixed retractors to the table), these latter three
complications underscore one of the most significant limitations of current surgical robotic systems—a lack of haptic
feedback for robotic arms possessing great strength. While
researchers from Intuitive Surgical and a great many others
are pursuing ways to provide surgeons with effective haptic
feedback, for now, safe use of the daVinci system necessitates that the surgical team pay careful attention to this
issue.
The direct costs associated with our training protocol
totaled $10,355. By delaying clinical use of the robotic
system purchased by our department for the purpose of
engaging in in-house animal model training, we were able to
significantly reduce the $63,250 cost associated with daVinci system training for 23 surgeons at designated Intuitive
Surgical Training Centers ($5,500 for two surgeons). Our
model of “on-site” training resulted in a cost savings of
approximately $52,895 for our medical center and provided
improved training opportunities for our surgeons (ie, the
entire second phase of our training protocol). While the cost
savings would not be as great for hospitals without dedicated animal operating room and veterinary support services, we nevertheless recommend similar training protocols for institutions implementing clinical surgical robotics
programs. Subsequent to the completion of our training
protocol, we have introduced our department’s daVinci system into clinical practice at Walter Reed. While it is still
very early in our institution’s experience with the system,
our surgeons have greatly appreciated the opportunities they
had to practice with the robot prior to clinical use. Our
systematic training program seems to have afforded our
E.J. Hanly et al / The American Journal of Surgery 187 (2004) 309 –315
department a smooth transition into the world of surgical
robotics.
Because the cost of complex surgical robotic systems
like daVinci is high (approximately $1,000,000), our hospital was eager to have the robot used in the clinical arena
as early as possible. Thus our study was somewhat limited
regarding the duration of the second phase of the protocol:
our study did not observe surgeons or surgical teams long
enough to determine at what point operative and set-up
times plateau. However, our study does suggests that hospitals implementing clinical surgical robotics programs that
institute preclinical surgical robotics training programs similar to ours can expect to enjoy a 40% reduction in preclinical operative time and a 50% reduction in preclinical set-up
time by allowing their surgeons and surgical teams to practice only three times.
In conclusion, new use of an advanced surgical robotic
system is associated with a rapid learning curve among
experienced surgeons from multiple surgical disciplines.
Surgeons can quickly learn the skills necessary to take
advantage of the daVinci robot’s benefits. Preclinical animal model training is effective in developing such skills and
allows surgeons the opportunity to refine their surgical robotic technique prior to human application. As the field of
robotic surgery continues to grow, graduate medical education and continuing medical education programs that address the surgical robotic learning needs of residents and
practicing surgeons need to be developed.
Acknowledgments
The specific daVinci system used in this study was purchased outright by Walter Reed Army Medical Center
(WRAMC), and the study was funded entirely by the
WRAMC Department of Surgery with veterinary support
from the Walter Reed Army Institute of Research. The
authors of this study have no conflicting financial or other
interests in Intuitive Surgical, the makers of the daVinci
robot.
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