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Background: Use of robotic systems for minimally invasive surgery has rapidly increased during the last decade. Understanding the causes of adverse events and their impact on patients in robot-assisted surgery will help improve systems and operational practices to avoid incidents in the future. Methods: By developing an automated natural language processing tool, we performed a comprehensive analysis of the adverse events reported to the publicly available MAUDE database (maintained by the U.S. Food and Drug Administration) from 2000 to 2013. We determined the number of events reported per procedure and per surgical specialty, the most common types of device malfunctions and their impact on patients, and the potential causes for catastrophic events such as patient injuries and deaths. Results: During the study period, 144 deaths (1.4% of the 10,624 reports), 1,391 patient injuries (13.1%), and 8,061 device malfunctions (75.9%) were reported. The numbers of injury and death events per procedure have stayed relatively constant (mean = 83.4, 95% confidence interval (CI), 74.2-92.7 per 100,000 procedures) over the years. Surgical specialties for which robots are extensively used, such as gynecology and urology, had lower numbers of injuries, deaths, and conversions per procedure than more complex surgeries, such as cardiothoracic and head and neck (106.3 vs. 232.9 per 100,000 procedures, Risk Ratio = 2.2, 95% CI, 1.9-2.6). Device and instrument malfunctions, such as falling of burnt/broken pieces of instruments into the patient (14.7%), electrical arcing of instruments (10.5%), unintended operation of instruments (8.6%), system errors (5%), and video/imaging problems (2.6%), constituted a major part of the reports. Device malfunctions impacted patients in terms of injuries or procedure interruptions. In 1,104 (10.4%) of all the events, the procedure was interrupted to restart the system (3.1%), to convert the procedure to non-robotic techniques (7.3%), or to reschedule it (2.5%). Conclusions: Despite widespread adoption of robotic systems for minimally invasive surgery in the U.S., a non-negligible number of technical difficulties and complications are still being experienced during procedures. Adoption of advanced techniques in design and operation of robotic surgical systems and enhanced mechanisms for adverse event reporting may reduce these preventable incidents in the future.
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The following manuscript is on analysis of adverse events in robotic surgical systems during the
14 year period of 2000–2013. This is an update to our analysis which was originally presented
at the 50
Annual Meeting of the Society of Thoracic Surgeons in January 2013. Please see
Appendix for more detailed results, discussions, and related work.
Adverse Events in Robotic Surgery:
A Retrospective Study of 14 Years of FDA Data
Homa Alemzadeh
, Ravishankar K. Iyer
, Zbigniew Kalbarczyk
, Nancy Leveson
, Jai Raman
University of Illinois at Urbana-Champaign - {alemzad1, rkiyer, kalbarcz}
Massachusetts Institute of Technology -
Rush University Medical Center -
Meeting Presentation: J. Maxwell Chamberlain Memorial Paper for adult cardiac surgery at the annual
meeting of The Society of Thoracic Surgeons (STS)
Keywords: Robotics, Minimally invasive surgery, Patient safety, Surgery complications, Surgical
Corresponding Author:
Jai Raman, MD FRACS PhD
1725 W Harrison St, Suite 1156
Rush University Medical Center, Chicago, Illinois 60612
Cell: 1-773-919-0088
Fax: 312 942 3666
Word Count: 3,000
Copyright © 2015: Authors. !
Importance: Understanding the causes and patient impacts of surgical adverse events will help improve
systems and operational practices to avoid incidents in the future.
Objective: To determine the frequency, causes, and patient impact of adverse events in robotic
procedures across different surgical specialties.
Methods: We analyzed the adverse events data related to robotic systems and instruments used in
minimally invasive surgery, reported to the U.S. Food and Drug Administration (FDA) MAUDE database
from January 2000 to December 2013. We determined the number of events reported per procedure and
per surgical specialty, the most common types of device malfunctions and their impact on patients, and
the causes for catastrophic events such as major complications, patient injuries, and deaths.
Results: During the study period, 144 deaths (1.4% of the 10,624 reports), 1,391 patient injuries (13.1%),
and 8,061 device malfunctions (75.9%) were reported. The numbers of injury and death events per
procedure have stayed relatively constant since 2007 (mean=83.4, 95% CI, 74.2–92.7). Surgical
specialties, for which robots are extensively used, such as gynecology and urology, had lower number of
injuries, deaths, and conversions per procedure than more complex surgeries, such as cardiothoracic and
head and neck (106.3 vs. 232.9, Risk Ratio = 2.2, 95% CI, 1.9-2.6). Device and instrument malfunctions,
such as falling of burnt/broken pieces of instruments into the patient (14.7%), electrical arcing of
instruments (10.5%), unintended operation of instruments (8.6%), system errors (5%), and video/imaging
problems (2.6%), constituted a major part of the reports. Device malfunctions impacted patients in terms
of injuries or procedure interruptions. In 1,104 (10.4%) of the events, the procedure was interrupted to
restart the system (3.1%), to convert the procedure to non-robotic techniques (7.3%), or to reschedule it to
a later time (2.5%).
Conclusions: Despite widespread adoption of robotic systems for minimally invasive surgery, a non-
negligible number of technical difficulties and complications are still being experienced during
procedures. Adoption of advanced techniques in design and operation of robotic surgical systems may
reduce these preventable incidents in the future.
Copyright © 2015: Authors. !
The use of robotic systems for minimally invasive surgery has exponentially increased during the last
decade. Between 2007 and 2013, over 1.74 million robotic procedures were performed in the U.S., of
which over 1.5 million (86%) were performed in gynecology and urology, while the number of
procedures in other surgical specialties altogether was less than 250,000 (14%)
. Several previous studies
on the outcomes and rates of complications during robotic procedures in the areas of gynecology, urology,
and general surgery have been done. Yet no comprehensive study of the safety and reliability of surgical
robots has been performed.
Our study focuses on analysis of all the adverse events related to robotic surgical systems, collected by
the FDA MAUDE database
during the 14-year period of 20002013.! It covers the events experienced
during the robotic procedures in six major surgical specialties: gynecology, urology, general, colorectal,
cardiothoracic, and head and neck surgery. We analyzed the safety-related incidents, including deaths,
injuries, and device malfunctions, to understand their causes and measure their impact on patients and on
the progress of the surgery.
There have been several reports by different surgical institutions on occasional software-related,
mechanical, and electrical failures of system components and instruments during robotic procedures
. A
few studies analyzed the FDA MAUDE reports related to robotic surgical systems
(see Tables 1 and 2
in Appendix). However, most of the previous work targeted only two common robotic surgical specialties
of gynecology and urology, or only analyzed small subsets or specific types of device failure modes (e.g.,
electro-cautery failures, electrosurgical injuries, instrument failures).
An important question is whether the evolution of the robotic systems with new technologies and features
over the years has improved the safety of robotic systems and their effectiveness across different surgical
specialties. Our goal is to use the knowledge gained from this analysis to provide insights on design of
future surgical systems that by taking advantage of advanced safety mechanisms, improved human
Copyright © 2015: Authors. !
machine interfaces, and regulated operational practices can minimize the adverse impact on both the
patients and surgical teams.
Data Sources
The Manufacturer and User Facility Device Experience (“MAUDE”) database is a publicly available
collection of suspected medical device-related adverse event reports, submitted by mandatory (user
facilities, manufacturers, and distributors) and voluntary (health care professionals, patients, and
customers) reporters to the FDA
. Manufacturers and the FDA regularly monitor these reports to detect
and correct device-related safety issues in a timely manner. Each adverse event report contains
information such as Device Name; Manufacturer Name; Event Type (“Malfunction,” “Injury,” “Death,” or
“Other”); Event Date; Report Date; and human-written Event Description and Manufacturer Narrative
fields, which provide a short description of the incident, as well as any comments made or follow-up
actions taken by the manufacturer to detect and address device problems
While the MAUDE database, as a spontaneous reporting system, suffers from underreporting and
, it provides valuable insights on real incidents that occurred during the robotic
procedures and impacted patient safety. We treated the reported data on deaths, injuries, and device
malfunctions provided by the MAUDE as a sample set to estimate the lower bounds on prevalence of
adverse events and identify examples of their major causes and patient impacts (see eMethods for more
Data Analysis Methods
We extracted all the reports related to the systems and instruments used in robotic surgery by searching
for related keywords in the Device Name and Manufacturer Name fields of the MAUDE records posted
between January 2000 and December 2013. In addition to the structured information that was directly
Copyright © 2015: Authors. !
available from the reports, we extracted further information from the unstructured human-written
descriptions of events by natural language parsing of the Event Description and Manufacturer Narrative
fields. We did so by creating several domain-specific dictionaries (e.g., for patient complications, surgery
types, surgical instruments, and malfunction types) and pattern-matching rules as well as parts-of-speech
(POS) and negation taggers to interpret the semantics of the event descriptions (Figure 1 in Appendix).
The results generated by the automated analysis tools were manually reviewed for accuracy and validity.
We extracted the following information:
Patient injury (such as burns, cuts, or damage to organs) and death events that were reported
under another Event Type, such as “Malfunction” or “Other”.
Surgical specialty and type of robotic procedure during which the adverse events occurred.
Major types of device or instrument malfunctions (e.g., falling of burnt/broken pieces of
instruments into patientsbodies or electrical arcing of instruments)
Adverse events that caused an interruption in the progress of surgery, by leading the surgical team
to troubleshoot technical problems (e.g., restarting the system), convert the procedure to non-
robotic surgical approaches (such as laparoscopy or open surgery), or abort the procedure and
reschedule it to a later time.
We compared the number of adverse events (in general) and injury/death events and procedure
conversions (in particular) per 100,000 procedures across different surgical specialties. The rate of events
was estimated by dividing the number of adverse events that occurred in each year (based on the Event
Date) by the annual number of robotic procedures performed in the U.S. The total number of procedures
per year was extracted from the device manufacturer’s reports
for 20042013 (see Figure 2 in
Appendix). The annual number of procedures per surgical specialty was available only for gynecology,
urology, and general surgery after 2007. So we estimated a combined annual number of procedures for
cardiothoracic and head and neck surgery by assuming that the majority of the remaining procedures
Copyright © 2015: Authors. !
(other than genecology, urology, and general) were related to these specialties, as, according to the
manufacturer reports, they are the only other specialties for which the robot has been used
We assumed that the rate of underreporting for injury and death events are low and are independent from
the type of surgery, because the device manufacturers are required and monitored by the FDA to report
serious injury and death events to the MAUDE database. However, due to possible changes in the
reporting rates during the years, the total number of events per procedure in the whole study period was
compared across different surgical specialties. The 2-sided P values (< 0.05) and 95% confidence
intervals were used to determine the statistical significance of the results.
To characterize the major causes to which injury and death events were attributed, we performed a
manual review of event descriptions for all the reports made before 2013. The cumulative number of
malfunctions per procedure was used to evaluate the trends in malfunction rates over 20042013.
Copyright © 2015: Authors. !
We extracted a total of 10,624 events related to the robotic systems and instruments, reported over 2000
2013. About 98% of the events were reported by device manufacturers and distributors, and the rest (2%)
were voluntary reports.
Data included 1,535 (14.4%) adverse events with significant negative patient impacts, including injuries
(1,391 cases) and deaths (144 cases), and over 8,061 (75.9%) device malfunctions. For the rest of the
events (1,028 cases), the Event Type information either was not available or was indicated as “Other.” We
identified 160 adverse events (1.5%) that included some kind of patient injuries but were reported as a
Malfunctionor Other.”
Trends in Adverse Event Reports: Figure 1 shows the overall trends in the annual numbers of reports and
the estimated rates of events per 100,000 procedures over 20042013:
The absolute number of reports has significantly increased (about 32 times) since 2006, reaching
58 deaths, 938 patient injuries, and 4,124 malfunctions in 2013.
While the annual average number of adverse events was about 550 per 100,000 procedures (95%
confidence interval (CI), 410–700) between 2004 and 2011, in 2013 it peaked at about 1,000
events per 100,000 procedures.
The numbers of injury and death events per procedure have stayed relatively constant since 2007
(mean=83.4, 95% CI, 74.292.7).
Copyright © 2015: Authors. !
Figure 1. Annual Numbers of Adverse Event Reports and Rates of Events per Procedure
The left Y-axis corresponds to the bars showing the absolute numbers of adverse events (based on the year that
reports were received by the FDA). The right Y-axis corresponds to the trend lines showing (in logarithmic scale)
the annual number of adverse events per 100,000 procedures (based on the year the events occurred). Numbers on
the bars indicate number of deaths reported per year. Error bars represent 95% confidence intervals for the
proportion estimates. Because of the small number of injury and death events reported for 2004 and 2005, a
combined rate was calculated for 20042006. Note that of all the events, 40 were reported as part of the articles or
the legal disputes received by the manufacturing company.
Adverse Events across Different Surgical Specialties: Table 1 shows the numbers of adverse events
reported in different surgical specialties and their impact on patients (injuries or deaths) and progress of
surgery (procedure conversion or rescheduling). The last row shows examples of the most common types
of procedures reported in each specialty.
The majority of reports were related to gynecology (30.1%), urology (14.7%), and cardiothoracic
(3.7%) surgeries, such as hysterectomy (2,331), prostatectomy (1,291), and thoracic (110)
procedures, respectively.
Cardiothoracic and head and neck surgeries involved a higher number of deaths per adverse event
report (6.4% and 19.7%) than gynecology and urology (1.4 and 1.9%).
The highest number of procedure conversions per adverse event was for cardiothoracic (16.8%)
and urology (13.5%), and the highest rates of procedure rescheduling were for urology (9.5%),
general (3.0%), and cardiothoracic (2.8%) surgeries.
Copyright © 2015: Authors. !
Table 1. Adverse events in different surgical specialties:
Deaths, injuries, malfunctions, procedure conversion or rescheduling, common types of surgery
No. (%) [95% Confidence Interval]
Event Type
Mitral valve
Low anterior
Coronary artery
bypass (23)
Percentages are over all the adverse event reports (n = 10,624).
Percentages are over the total adverse events reported for a surgical specialty.
The higher percentage of adverse events reported in gynecology and urology could be due to the higher number of these
procedures performed.
Copyright © 2015: Authors. !
Of all the reports, only 5,721 (53.8%) indicated the class and type of surgery involved. However, the
majority of reports with missing information on the type of surgery were related to device malfunctions
and “Other” events (97.6%). In order to compare the rate of adverse events across different specialties, we
focused only on reports related to injuries, deaths, and procedure conversions. For the majority of these
events (92.2% of injury reports, 95.1% of deaths, and 72.2% of procedure conversions), the surgery type
information was available and the rest (with ‘N/A’ surgical specialty) were removed from our analysis. In
order to estimate the rate of events per procedure, we regrouped the events into four major categories of
“Gynecology, “Urology, “General, and “Cardiothoracic and Head and Neck, according to the
manufacturers reports
. The “General” category includes both colorectal and general specialties.
As shown in Table 2, for cardiothoracic and head and neck surgery, the rates of injuries, deaths, and
procedure conversions have been significantly higher than other specialties. During 2007-2013, the
estimated rate of deaths have been 52.2 per 100,000 procedures for cardiothoracic and head and neck
specialties vs. 5.7 in gynecology, urology, and general surgeries (RR = 9.23, 95% CI, 6.3513.40, P <
0.0001). Also, the rate of injuries and procedure conversions in these specialties have been 91.0 and 89.7
per 100,000 procedures vs. 71.5 (RR = 1.27, 95% CI, 0.991.63, P < 0.052) and 29.2 (RR = 3.07, 95% CI,
2.383.97, P < 0.0001) in the other surgical categories.
Table 2. Comparsion of adverse events rates in different surgical specialities (2007 - 2013)
No. (rate per 100,000 procedures)
[95% CI]
Head and Neck,
Cardiothoracic and Head and Neck
Gynecology, Urology, and General
Total Procedures
Total Adverse Events
Relative Risk (95% Cl)
P Value
Event Type
9.23 (6.3513.40)1
< 0.0001
1.27 (0.99–1.63)
< 0.052
3.07 (2.383.97)
< 0.0001
1.48 (0.83–2.66)
< 0.19
Percentages are over total number of procedures in each column.
Assuming that the level of underreporting across different surgical specialties is similar.
Not statistically significant because of the small number of samples (12) in the cardiothoracic and head and neck surgery.
Copyright © 2015: Authors. !
Device and Instrument Malfunctions: We identified five major categories of device and instrument
malfunctions experienced during procedures that impacted the patients, either by causing injuries and
complications or by interrupting the progress of surgery and/or prolonging procedure times. Table 2
shows the numbers of events in each category, the event types as indicated by reporters, and the actions
taken by the surgical team to resolve the problems. The Other category includes the malfunctions that
could not be classified in any of the classes.
System errors and video/imaging problems contributed to 787 (7.4%) of the adverse events and
were the major contributors to the system resets (274 cases, 82% of all system resets), conversion
of the procedures to a non-robotic approach (462 cases, 59.2% of all conversions), and
aborting/rescheduling of the procedures (221 cases, 81.8% of all cases).
Falling of the broken/burnt pieces into the patient’s body constituted about 1,557 (14.7%) of
the adverse events. In almost all these cases, the procedure was interrupted, and the surgical team
spent some time searching for the missing pieces and retrieving them from the patient (in 119
cases, a patient injury, and in one case a death, was reported).
Electrical arcing, sparking, or charring of instruments and burns or holes developed in the tip
cover accessories constituted 1,111 reports (10.5% of the events), leading to nearly 193 injuries,
such as burning of tissues.
Unintended operation of instruments, such as uncontrolled movements and spontaneous
powering on/off, happened in 1,078 of the adverse events (10.1%), including 52 injuries and 2
In total, 5,054 reports (47.6%) were related to breakage of different parts of the system and instruments.
Cable, wire, or tube breakages are example causes of imaging problems at the surgeon’s console or
unintended instrument operations.
Copyright © 2015: Authors. !
Table 3. Major categories of malfunctions
No. of Reports
(% of all)
Surgical Team Actions
(% of malfunction category)
Event Type
- System error codes and faults
- System transferred into a recoverable
or non-recoverable safety state
- Loss of video
- Display of blurry images at surgeon’s
console or assistant’s touchscreen
Falling Into
- Burnt/broken parts and components
- Fell into surgical field or body cavity
- Required additional procedure time to
be found/removed from the patient
Tip Covers/
Elec. Arcing
- Tears, burns, splits, holes on tip cover
- Electrical arcing, sparking, charring
- Unintended or unstoppable movements
started without the surgeon’s command
- Instruments not working, open/closed
- Instruments not recognized by system
- Issues with electrosurgical units, power
supplies/cords, patient-side
manipulators, etc.
- Other events reported as Malfunction
(% of all)
- All malfunctions
All Adverse Events
Broken Pieces
Fell into Patients
Burns/Holes in
Tip Covers/Elec. Arcing
Instrument Operation
(n = 5,054)
camera, cables
instrument pieces,
tips, cautery hooks
tips, wires, insulation
cables, tubes, wires
The malfunction categories and actions taken by the surgical team are not mutually exclusive, and in many
cases two or three different malfunctions or two actions were reported in a single event. Figure 3 in Appendix
uses Venn diagrams to depict the intersections among different malfunction categories and actions taken by the
surgical team.
Event types as indicated by reporters: Malfunction (M), Injury (IN), Death (D), and Other (O).
Table 3 in Appendix lists the descriptions and frequencies of the most common system error codes extracted
from the reports.
In 1,019 of cases (10.9% of all the malfunctions), the device or instrument malfunction was detected prior to
start of the procedure, of which in 20 cases the procedure was rescheduled to a later time and in 2 cases it was
converted to a non-robotic approach.
Copyright © 2015: Authors. !
Figure 3 shows the cumulative rates of malfunctions per procedure over 20042013. Overall, the
malfunction rates decreased after 2006, but the rate of cases with arcing instruments and broken
instruments followed a relatively constant trend. The sudden increase in the rate of broken instruments
after the middle of 2012 could be related to changes made to the adverse event reporting practices by the
manufacturer in 2012 (mostly related to instrument cable breaks)
, as well as increased reporting of
adverse events after concerns about the safety of robotic surgery were raised by the FDA
and public
media in early 2013
In total, 9,382 reports were about technical problems, including 1,104 cases (10.4% of all the adverse
events) in which the procedure was interrupted and additional time was spent on troubleshooting the
errors, resetting the system, and/or converting the procedure to a traditional technique, or rescheduling the
procedure to a later time.
Figure 2. Cumulative rates of malfunctions per procedure
The rates of malfunctions per procedure were obtained for each week (see Figure 2 in Appendix for more details).
Copyright © 2015: Authors. !
Injury and Death Causes: A manual review of a sample set of injury and death reports (from 20002012)
was conducted. This allowed us to classify the causes indicated by reporters into three main categories:
inherent risks associated with surgery, technical issues with the robot, and mistakes made by the surgical
team. For the majority of death events, little or no information was provided in the reports. About 33.7%
of the death events were related to inherent risks or complications during surgery, and 7% were attributed
to operator mistakes. About 62% of the injury events involved device malfunctions (see Table 2), and the
rest were related to operator errors (7.1%), improper positioning of patient or port incisions (6.3%),
inherent risks of surgery (3.9%), or problems with grounding the equipment (1.5%) (see Tables 4 and 5
in Appendix).
Copyright © 2015: Authors. !
Our analysis shows an increasing number of adverse events related to the robotic surgical systems being
reported. As cautioned by the FDA
, the number of MAUDE reports may not be used to evaluate the
changes in rates of events over time, because the increased reporting of events may be due to different
factors, e.g., the increasing use of surgical systems
, changes in the manufacturersreporting practices
and/or better awareness and increased publicity resulting from product recalls, media coverage, and
. Therefore, we measured the prevalence of adverse events by estimating the number of events
reported per procedure. We found that despite a relatively high number of reports, the vast majority of
procedures were successful and did not involve any problems and the number of injury/death events per
procedure have stayed constant since 2007.
However, our analysis shows that estimated number of events per procedure in complex surgical areas,
such as cardiothoracic and head and neck surgery were significantly higher than gynecology, urology, and
general surgeries. Although not all the reported injuries and deaths were due to device problems, and the
procedure conversions, of themselves, cannot be considered adverse events
, the estimated numbers of
injury/death events and conversions per procedure are used as a metric to measure the difficulty
experienced in different surgical specialties. The best that we can tell from the available data is that the
higher number of injury, death, and conversion per adverse event, in cardiothoracic and head and neck
surgeries, could be indirectly explained by the higher complexity of the procedures, less frequent use of
robotic devices, and less robotic expertise in these fields. Although the use of robotic technology has
rapidly grown in urology and gynecology for prostatectomy and hysterectomy, it has been slow to
percolate into more complex areas, such as cardiothoracic and head and neck surgery. The limitations of
the robotic user interface
, long procedure times
, learning curve
, and higher costs
are some factors
that may have contributed to the lower utilization of the robotic approach in more complex surgical
procedures. For example, only a select type of robotic cardiac procedures are reported to have been
successfully performed using the robots, such as mitral valve repair and internal mammary artery
Copyright © 2015: Authors. !
. The recent experiences of highly competent robotic teams that performed multi-vessel
coronary artery bypass grafting (CABG) showed that the robotic approach may be associated with higher
mortality and morbidity rates compared to open surgery
In practice, the use of the robotic platform involves the interface of a sophisticated machine (see Table 6
in Appendix) with surgical teams, in an area of patient care that is safety-critical. From a technology
perspective, some of the reported events could be prevented by employing substantially improved safety
practices and controls in the design and operation of surgical systems. Some examples include:
New safety engines for monitoring of procedures (including surgeon, patient, and device status) and
providing comprehensive feedback to surgical team on upcoming events and troubleshooting
procedures to prevent long procedure interruptions.
Providing real-time feedback to the surgeon on the safe surgical paths that can be taken
, by
computing 3D models of the organs under surgery and surrounding critical tissues and vessels, as
well as surgeon-specific modeling and monitoring of robotic surgical motions
, to minimize the
risk of approaching dangerous limits and inadvertent patient injuries.
Improved human-machine interfaces and surgical simulators that train surgical teams for handling
technical problems and assess their actions in real-time during the surgery.
The results of our study come with the caveats that inherent risks exist in all surgical procedures (more so
in complex procedures) and that the MAUDE database suffers from underreporting and inconsistencies.
Thus, the estimated number of adverse events per procedure are likely to be lower than the actual
numbers in robotic surgery. Further, the lack of detailed information in the reports makes it difficult to
determine the exact causes and circumstances underlying the events. Therefore, the sensitivity of adverse
event trends to changes in reporting mechanisms, surgical team expertise, and inherent risks of surgery
could not be assessed here.
Copyright © 2015: Authors. !
While the robotic surgical systems have been successfully adopted in many different specialties, this
study demonstrates several important findings: (1) the overall numbers of injury and death events per
procedure have stayed relatively constant over the years, (2) the probability of events in complex surgical
specialties of cardiothoracic and head and neck surgery has been higher than other specialties, (3) device
and instrument malfunctions have affected thousands of patients and surgical teams by causing
complications and prolonged procedure times.
As the surgical systems continue to evolve with new technologies, uniform standards for surgical team
training, advanced human machine interfaces, improved accident investigation and reporting mechanisms,
and safety-based design techniques should be developed to reduce incident rates in the future.
Copyright © 2015: Authors. !
1. Annual Report 2013, Intuitive Surgical; http://phx.corporate-
2. “MAUDE: Manufacturer and User Facility Device Experience,” U.S. Food and Drug Administration,
3. Eichel L, Ahlering TE, Clayman RV. Robotics in Urologic Surgery: Risks and Benefits. AUA Update Series
2005; 24(lesson 13): 106111.
4. Kozlowski PM, Porter CR, Corman JM. Mechanical failure rate of DaVinci robotic system: Implications for
pre-op patient counseling [abstract 1159]. J Urol 2006; 175(suppl):s372s373.
5. Borden Jr, L. S., P. M. Kozlowski, C. R. Porter, and J. M. Corman. "Mechanical failure rate of da Vinci robotic
system." The Canadian journal of urology 14, no. 2 (2007): 3499.
6. Zorn, Kevin C., Ofer N. Gofrit, Marcelo A. Orvieto, Albert A. Mikhail, R. Matthew Galocy, Arieh L. Shalhav,
and Gregory P. Zagaja. "Da Vinci robot error and failure rates: single institution experience on a single three-
arm robot unit of more than 700 consecutive robot-assisted laparoscopic radical prostatectomies." Journal of
Endourology 21, no. 11 (2007): 1341-1344.
7. Fischer, Boris, Nadja Engel, Jean-Luc Fehr, and Hubert John. "Complications of robotic assisted radical
prostatectomy." World journal of urology 26, no. 6 (2008): 595-602.
8. Lavery, Hugh J., Rahul Thaly, David Albala, Thomas Ahlering, Arieh Shalhav, David Lee, Randy Fagin et al.
"Robotic equipment malfunction during robotic prostatectomy: a multi-institutional study." Journal of
Endourology 22, no. 9 (2008): 2165-2168.
9. Ham, Won Sik, Sung Yul Park, Ho Song Yu, Young Deuk Choi, Sung Joon Hong, and Koon Ho Rha.
"Malfunction of da Vinci robotic systemdisassembled surgeon's console hand piece: Case report and review
of the literature." Urology 73, no. 1 (2009): 209-e7.
10. Kim, Won Tae, Won Sik Ham, Wooju Jeong, Hyun Jung Song, Koon Ho Rha, and Young Deuk Choi. "Failure
and malfunction of da Vinci Surgical systems during various robotic surgeries: experience from six departments
at a single institute." Urology 74, no. 6 (2009): 1234-1237.
11. Kaushik, Dharam, Robin High, Curtis J. Clark, and Chad A. LaGrange. "Malfunction of the Da Vinci robotic
system during robot-assisted laparoscopic prostatectomy: an international survey." Journal of Endourology 24,
no. 4 (2010): 571-575.
12. Finan, Michael A., and Rodney P. Rocconi. "Overcoming technical challenges with robotic surgery in
gynecologic oncology." Surgical Endoscopy 24, no. 6 (2010): 1256-1260.
13. Mues, Adam C., Geoffrey N. Box, and Ronney Abaza. "Robotic instrument insulation failure: initial report of a
potential source of patient injury." Urology 77, no. 1 (2011): 104-107.
14. Agcaoglu, Orhan, Shamil Aliyev, Halit Eren Taskin, Sricharan Chalikonda, Matthew Walsh, Meagan M.
Costedio, Matthew Kroh et al. "Malfunction and failure of robotic systems during general surgical
procedures." Surgical Endoscopy (2012): 1-4.
15. Chen, Cheng-Che, Yen-Chuan Ou, Cheng-Kuang Yang, Kun-Yuan Chiu, Shian-Shiang Wang, Chung-Kuang
Su, Hao-Chung Ho et al. "Malfunction of the da Vinci robotic system in urology." International Journal of
Urology (2012).
16. Buchs, Nicolas C., et al. "Reliability of robotic system during general surgical procedures in a university
hospital." The American Journal of Surgery 207.1 (2014): 84-88.
17. Murphy, D., B. Challacombe, O. Elhage, and P. Dasgupta. “Complications in Robotic Urological
Surgery.” Minerva Urologica e Nefrologica 59, no. 2 (2007): 191.
Copyright © 2015: Authors. !
18. Andonian, S., Z. Okeke, D. A. Okeke, A. Rastinehad, B. A. Vanderbrink, L. Richstone, and B. R. Lee. “Device
Failures Associated with Patient Injuries during Robot-assisted Laparoscopic Surgeries: A Comprehensive
Review of FDA MAUDE Database. Canadian Journal of Urology 15, no. 1 (2008): 3912.
19. Lucas, Steven M., Erik A. Pattison, and Chandru P. Sundaram. “Global Robotic Experience and the Type of
Surgical System Impact the Types of Robotic Malfunctions and their Clinical Consequences: An FDA MAUDE
Review.” BJU International 109, no. 8 (2012): 12221227.
20. Fuller, Andrew, George A. Vilos, and Stephen E. Pautler. “Electrosurgical Injuries during Robot Assisted
Surgery: Insights from the FDA MAUDE Database.” In Proceedings of SPIE, vol. 8207, p. 820714. 2012.
21. Friedman, Diana CW, Thomas S. Lendvay, and Blake Hannaford. "Instrument Failures for the da Vinci
Surgical System: a Food and Drug Administration MAUDE Database Study." Surgical endoscopy 27.5 (2013):
SYSTEM AS REPORTED IN THE FDA MAUDE DATABASE." The Journal of Urology 189.4 Supplement
23. Manoucheri, E., et al. "MAUDEAnalysis of Robotic-Assisted Gynecologic Surgery." Journal of Minimally
Invasive Gynecology (2014).
24. “Adverse Event Reporting of Medical Devices,” U.S. Department of Health and Human Services, Office of
Inspector General (OEI-01-08-00110), October 2009;
25. Hauser RG, Katsiyiannis WT, Gornick CC, Almquist AK, Kallinen LM. Deaths and cardiovascular injuries due
to device-assisted implantable cardioverter-defibrillator and pacemaker lead extraction. Europace. 2010;
26. Cooper MA, Ibrahim A, Lyu H, Makary MA. Underreporting of Robotic Surgery Complications. J Healthc
Qual. 2013.
27. Investor Presentation, Intuitive Surgical, Inc., Q1 2013, http://phx.corporate-
28. “da Vinci
Procedures,” da Vinci Surgery,
29. Machin D, Campbell MJ, Tan S. Sample Size Tables for Clinical Studies. BMJ Books; 2008.
30. “Intuitive Surgical Comments on Medical Device Reporting Practices,” Intuitive Surgical, Inc., March 13,
31. “Computer-Assisted (Robotic) Surgical Systems: What are Computer-Assisted (Robotic) Surgical Systems?
U.S. Food and Drug Administration, Nov. 2013;
32. U.S. Food and Drug Administration, Warning Letters, July 16, 2013;
33. “Has the Halo Been Broken on Intuitive Surgical?” Citron Research, December 19, 2012;
34. “Intuitive Surgical: Angel with Broken Wings, or the Devil in Disguise?” Citron Research, January 17, 2013;
35. FDA Investigates Robotic Surgery System after Adverse Event Spike,” Medscape Today;
36. Simorov A, Otte RS, Kopietz CM, Oleynikov D. Review of surgical robotics user interface: what is the best
way to control robotic surgery?. Surg Endosc. 2012; 26(8): 2117-25.
37. Seco M, Cao C, Modi P, et al. Systematic review of robotic minimally invasive mitral valve surgery. Ann
Cardiothorac Surg. 2013; 2(6): 704-716.
Copyright © 2015: Authors. !
38. Holzhey DM, Seeburger J, Misfeld M, Borger MA, Mohr FW. Learning minimally invasive mitral valve
surgery: a cumulative sum sequential probability analysis of 3895 operations from a single high-volume center.
Circulation. 2013; 128(5):483-91.
39. Lee J, Yun JH, Nam KH, Soh EY, Chung WY. The learning curve for robotic thyroidectomy: a multicenter
study. Ann Surg Oncol. 2011;18(1):226-32.
40. Barbash GI, Glied SA. New technology and health care costs--the case of robot-assisted surgery. N Engl J Med.
41. Marusch, Frank, et al. "Importance of conversion for results obtained with laparoscopic colorectal
surgery." Diseases of the colon & rectum 44.2 (2001): 207-214.
42. Gonzalez, Rodrigo, et al. "Consequences of conversion in laparoscopic colorectal surgery." Diseases of the
colon & rectum 49.2 (2006): 197-204.
43. Damiano RJ. Robotics in cardiac surgery: the Emperor’s new clothes. J Thorac Cardiovasc Surg. 2007;
44. Robicsek F. Robotic cardiac surgery: time told! J Thorac Cardiovasc Surg. 2008; 135(2):243-6.
45. Modi P, Hassan A, Chitwood WR. Minimally invasive mitral valve surgery: a systematic review and meta-
analysis. Eur J Cardiothorac Surg. 2008; 34(5):943-52.
46. Dhawan R, Roberts JD, Wroblewski K, Katz JA, Raman J, Chaney MA. Multivessel beating heart robotic
myocardial revascularization increases morbidity and mortality. J Thorac Cardiovasc Surg. 2012;143(5):1056-
47. R. R. Shamir, M. Horn, T. Blum, J. Mehrkens, Y. Shoshan, L. Joskowicz, and N. Navab, “Trajectory planning
with augmented reality for improved risk assessment in image-guided keyhole neurosurgery,” in Proc. IEEE Int.
Symp. Biomed. Imaging: From Nano to Macro, Chicago, IL, USA, 2011, pp. 18731876.
48. Lin, Henry C., Izhak Shafran, David Yuh, and Gregory D. Hager. "Towards automatic skill evaluation:
Detection and segmentation of robot-assisted surgical motions." Computer Aided Surgery 11, no. 5 (2006): 220-
Copyright © 2015: Authors. !
The underreporting in data collection is a fairly common problem in social sciences, public health, criminology, and
microeconomics. It occurs when the counting of some event of interest is for some reason incomplete or there are
errors in recording the outcomes. Examples are unemployment data, infectious or chronic disease data (e.g. HIV or
diabetes), crimes with an aspect of shame (e.g. sexuality and domestic violence), error counts in a production
processes or software engineering, and traffic accidents with minor damage [1]. An estimated prevalence of events
based on the incomplete counts is likely to be smaller than the true proportion of events in the population. Several
inference techniques based on binomial, beta-binomial, and regression models have been proposed for estimating
the actual count values [2]. However, in all those techniques the reporting probability (underreporting rate) is
assumed to be a constant parameter over time that is estimated based on the sample counts.
A very similar problem exists in preliminary or pilot clinical investigations, epidemiological surveys, and longitude
studies where the objective is to estimate any possible clinical effect of a treatment or prevalence of a particular
disease in a population of patients, but the prevalence of events can only be estimated by selecting a sample of
patients from the population [3].
In all these situations, the prevalence of the events are estimated based on a random sample of events from the
population, under the assumption that the sample set contains the same characteristics and distributions of the actual
population, including those of the underreported and missing cases.
Furthermore, it is often required to perform a sample-size calculation based on confidence intervals in order to
provide a precise estimate with a large margin of certainty and to make sure that the estimated proportion is close to
the actual proportion with a high probability [3]. Confidence intervals for the proportions estimated based on
samples from large populations and finite populations can be calculated by using the normal approximation to the
binomial distribution as follows:
For large populations:
𝑝 ± 𝑧
𝑝(1 𝑝)
For finite populations:
𝑝 ± 𝑧
𝑝(1 𝑝)
where N is the size of sample, 𝑝 =
is the estimate of the proportion of events of interests in the sample and
is the size of population in case of finite populations [3].
In this study, we estimated the prevalence of adverse events by making sure that we have a significantly large
enough number of samples to provide confident estimates. Our estimations are obtained under the assumption that
the characteristics and distributions of the observed events are not significantly different from those in the actual
population and would not significantly change after including the underreported cases. We are currently
investigating the extension of the proposed inference techniques in [1][2] to estimate the actual number of adverse
events with considering a variable reporting probability over time.
[1] Neubauer, G. and Friedl, H., “Modelling sample sizes of frequencies,” Proceedings of the 21
Workshop on Statistical Modelling, 3-7 July 2006, Galway, Ireland.
[2] Neubauer, G., Djuras G., Friedl H., “Models for underreporting: A Bernoulli sampling approach for reported
counts,” Austrian Journal of Statistics, Vol. 40 (2011), No. 1 & 2, 8592
[3] Machin D, Campbell MJ, Tan S. Sample Size Tables for Clinical Studies. BMJ Books; 2008.
Copyright © 2015: Authors. !
Figure 1. Data extraction and analysis flow from the FDA MAUDE database
MAUDE Database
2,959,683 Records
Search Query
Report Date:
From: 01/01/2000
To: 01/01/2013
MDR Report Key
Brand Name
Generic Name
Manufacturer Name
Baseline brand name
Baseline generic name
Baseline device family
Device Manufacture Date
Event Date
Report Date
Date FDA Received
Classified Adverse Events
1. Report Year
2. Time to FDA Received
3. Event Type (M, I, D, O)
4. Patient Outcome
5. Event Description
6. Manufacturer Narrative
7. Number of Devices
8. Class/Type of Surgery
9. Converted To?
10. Rescheduled?
11. Malfunctioned Instuments
160 Miss-Reported Injuries
203 System Error Codes
Search for
Cross-match to
Online Records
Robotic Surgical
Keywords related to
Surgery Classes
Robotic Instrument
Pattern matching and
Keyword searching
Instrument Names
Injury Keywords
‘converted’ and
‘rescheduled’ terms
Natural Language Parsing:
- Segmentation
- Normalization
- Part-of-speech Tagging
- Negation Detection
Domain-specific Dictionaries
Domain Experts
Copyright © 2015: Authors. !
Figure 2. Estimated numbers of procedures performed during 20042013
The annual numbers of procedures performed in the U.S. for 20102013 were extracted from the annual reports of
the manufacturer
. For 20042009, we estimated the numbers of procedures by measuring the graphs in the
company’s investor presentations
. Whenever the estimated numbers from two different sources did not match or
the data were available only for the total worldwide procedures, we chose the maximum number of procedures for
that year in order to achieve a lower bound on the likelihood of events.
We estimated the number of procedures per week from annual number of procedures by fitting a 4-degree
polynomial curve (R
= 0.999) to the bar graph of annual procedures and calculating the area under the fitted curve
for every week.!
Copyright © 2015: Authors. !
Figure 3. (a) Intersections among different malfunction categories,
(b) Intersections among system resets, converted, and rescheduled cases
(A total of 3,067 adverse event reports were not classified by MedSafe in any of the malfunction categories.)
(For 9,520 adverse events, no system resets, conversions, or reschedulings were reported.)!
Copyright © 2015: Authors. !
Table 1. Summary of related work on failures of robotic surgical systems
Ref. No.
Surgery Types
Medical Institute
Total Number of Failures (Failure Rate)
Types of Malfunctions
UC Irvine
Total = 5 (2.5%)
Software (4), Mechanical (1)
Virginia Mason
Medical Center
Total = 6 (4.6%)
Setup joint (2), Software incompatible (1),
Robotic arm (1), Power-off (1),
Monitor loss (1)
Laparoscopic (1)
Open (1)
Virginia Mason
Medical Center
Total = 9 (2.6%)
Setup joint (2), Robotic arm (1), Camera (1),
Power error (1), Console metal break (1),
Software incompatible (1), Monitor loss (1)
Open (3)
University of
Pritzker School of
Total = 7
(Recover. = 0.21%, Non-Recover. =.05%)
Power-up failure (1), Optical malfunction
(3), Surgeon handicap (3), Robotic arm (1),
Camera (2)
Completed (3)
Klinik Hirslanden,
Zurich, Switzerland
Total = 2 (1%)
Robotic arm (2)
11 Institutions
700 Surgeons
34 critical failures (0.4%)
Robotic arm (14), Optical system (14),
Masters (4), Power supply/circuit (6),
Unknown error (3)
Laparoscopic (2)
Open (8)
Yonsei University
College of
Medicine, Korea
Case report of Surgeon’s console failure
Delayed 15 min
General Surgery,
Obstetrics and
Thoracic Surgery,
Cardiac Surgery,
Yonsei University
College of
Medicine, Korea
Total = 43 (2.4%)
Robot failures (24): On/off failure (1),
Console malfunction (5), Robotic arm (6),
Optic system (2), System error (10)
Instrument failures (19): Shaft injuries (9),
Wire cutting (2), Unnatural motion (2),
Instrument tip (2), Limitation in motion (1)
Open (3)
Survey of
176 Surgeons from
4 Countries
Total failures = 260
Robotic arm (38%), Camera (17.6%),
Setup joint (13.8%), Power error (8.8%),
Ocular monitor loss (8%), Instruments
(7.6%), Console handpiece break (3%),
Software (1.9%), Backup battery (0.3%),
Instrument identification (0.3%)
Open (18.8%),
Another robot,
with one fewer
robotic arm
Mitchell Cancer
Institute, University
of South Alabama
Total = 11 (8.02%)
Robotic arm (2), Light or camera cord (2),
Maylard bipolar (1), Power failure (1), Port
problem (1), Others (3)
Delayed 25 min.
General surgery,
Ohio State
University Medical
James Cancer
Tip cover failures = 12 (2.6%)
Significant patient complications (25%)
Repaired at the
time of surgery
Cleveland Clinic
Total = 10 (4.5%)
Robotic instrument (4), Optical system (3),
Robotic arms (2), Robotic console (1)
Open surgery
Veterans General
Hospital, Taiwan
Total = 14 (3.5%)
Robotic arm/joint (11), Optical system (1),
Power system (1), Endoscopic instrument
(1), Software incomp. (1)
Completed (10),
Laparoscopy (3)
General Surgery
A Teaching
Total = 18 (3.4%)
Robotic instruments (9), Robotic arms (4),
Surgical console (3), Optical system (2)
Laparoscopic (1)
Copyright © 2015: Authors. !
Table 2. Summary of related work on analysis of MAUDE data of robotic surgical systems
Murphy et al.
identified 38 system failures and 78 adverse events related to the da Vinci robotic system,
reported from 2006 to 2007; most of them were related to broken instrument tips or failure of
electrocautery elements.
Andonian et al.
found an estimated failure rate of 0.38% for robotic-assisted laparoscopic surgeries by
reviewing 189 adverse events related to the ZEUS and da Vinci surgical robotic systems, reported to the
MAUDE database between the years 2000 and 2007.
Lucas et al.
compared the rates of adverse events for two different models of da Vinci surgical systems
(dVs and dV) during the period of 20032009 and showed that both device malfunctions and open
conversions were reduced by increased robotic experience and newer surgical systems.
Fuller et al.
reviewed 605 adverse events involving the da Vinci system during 20012011, and
identified 24 (3.9%) reports related to electrosurgical injuries (ESI) that occurred during gynecological
and prostatectomy procedures.
Friedman et al.
analyzed the da Vinci robotic system instrument failures reported to the MAUDE
database in 2009 and 2010. They found a total of 565 instrument failures, of which the majority were
related to the instrument wrist or tip (285), 174 were related to cautery problems, 76 were shaft failures,
and the rest were cable and control housing failures (36).
Gupta et al.
reviewed a total of 741 adverse events reported to the MAUDE database in 2009 and 2010.
They found that 43.5% of the events were related to the use of energy instruments, that 30.97% were
associated with the surgical systems and instruments, and that the severity of events correlated with the
type of surgery and the type of device used.
Finally, Manoucheri et al.
evaluated the adverse events reported during robotic gynecologic procedures
and found that the majority of reported injuries (65%) were not directly related to the surgical system;
21% were related to operator error; and 14% were due to technical system failures. !
Copyright © 2015: Authors. !
Table 3. Most frequent system error codes
Type of
Safe State
that System
Transits To
No. of
The angular position of one or more robotic joint’s on the specified
manipulator, as measured by the joint’s primary control sensor (encoder) and
the secondary sensor (potentiometer), were out of specified tolerance for
A voltage-tracking fault reported by the digital signal processor (dsp) when the
actual voltage to drive current through the motors deviates from the expected
voltage by a specified amount.
Hardware wheel "wdog" has tripped on one of the digital communication links
in the system (due to an excessive number of retries on hardware message
packets). This means that the system cannot reliably communicate over that
digital link and therefore cannot continue normal operation.
Communication faults in the low-voltage differential signal carrying
information about the patient side manipulator.
Communication faults between two system components.
A power supply voltage was out of range.
A redundant switch was missing its ground sense, or the contacts did not report
as expected at startup.
A motor did not respond as expected, and the measured motion did not match
the internal stimulation of the motor.
A reference voltage was out of range.
One of the camera controller units in the doco has failed to power on after
multiple attempts or has shut down after initially powering up.
One or more fans are not moving as desired
An electronic component was reporting an incorrect configuration.
Master supervisory controller did not receive an expected message within a
specified time.
One of the switches in a specific manipulator is showing inconsistent signals
on its two switch leads.
During the power up self-test, the remote arm controller board (rac) brakes
failed the brake voltage test.
A sympathetic error and occurs during the self-test upon system power-up
when a loop response test fails.
On startup, one or more robotic joints on the manipulator did not make the
prescribed test motion to within the specified tolerance.
The arm did not perform the commanded motions during startup within a
specified tolerance.
A processor did not complete a step during system startup within the allotted
After a specified amount of time, a valid event was not seen for one of the
remote compute engine switches.
A communication timeout with the software running the da Vinci onsite
Copyright © 2015: Authors. !
Table 4. Summary of death and injury reports (2000–2012)
Death Reports (Total = 86)
Example Causes
Number of Reports (%)
Surgeon/staff mistake
6 (7.0%)
Patient’s history
10 (11.6%)
Inherent risks
19 (22.1%)
27 (31.4%)
During Procedure
Punctures, bleeding, pulmonary
embolism, cardiac arrest
64 (75.3%)
After Procedure
Infection/sepsis, heavy bleeding
15 (17.4%)
Injury Reports (Total = 410)
Example Causes
Number of Reports (%)
Device malfunctions
254 (62.0%)
Surgeon/staff mistake
29 (7.1%)
Improper positioning of the patient led to post-operation
complications such as nerve damage
17 (4.1%)
Inherent risks of surgery and patient history
16 (3.9%)
Burning of tissues near port incisions
9 (2.2%)
Possible passing of the electrosurgical unit currents through
instruments to the patient body
6 (1.5%)
Surgeon felt shocking at the surgeon-side console
2 (0.5%)
77 (18.8%)
Table 5 lists some example reports on device malfunctions that impacted patients during cardiothoracic procedures.!
Copyright © 2015: Authors. !
Table 5. Example malfunctions and their patient impact during cardiothoracic procedures
Patient Impact
Recovery Actions
Possibly port placement;
The robotic arms were
never seen to collide, but
this could have occurred,
resulting in pressure on the
Left atrial disrupted,
A 3 cm tear occurred
in the hood of atrium
medial to left atrial
extending down
towards the mitral
A patch was brought
into place, trimmed,
sewn using a suture,
and tied.
Sternotomy incision
made for further
Unexplained movement of
the system arm with the
endowrist stabilizer
instrument attached to it.
Feet at the distal end of the
endowrist stabilizer tipped
Damage to the
myocardium of the
patient’s left
Converted to open
sternotomy and
Micro bipolar forceps
(mbf) instrument jumped
When master tool
manipulator was moved,
the instrument felt stuck
and then moved.
Patient side
Patient’s artery
Damaged section of
artery was transected
and the healthy
portion was used to
complete the bypass.
Company replaced the
psm component.
Arcing from bipolar
forceps instrument.
Small burn to
Connected ground
pads and checked
electrical surgical unit.
Patient-side manipulator
(psm) arm 2 jumped.
The forceps
instrument on the
psm lacerated the
patient's mammary
Converted to open
Copyright © 2015: Authors. !
Table 6. Example complex robotic interactions with possible failure modes
1) A surgeon or surgical assistant needs to be by the patient’s side, inserting the ports/scope/instruments.
2) The main surgeon sits at a console some distance away from the patient, with no peripheral vision,
and so does not get to see the manipulation of the arms in and around the patient.
3) Any change of instrumentation requires a pause in proceedings, as the patient-side surgeon stops and
changes instruments. Once the instrument is docked in the port, registered, and secured, the
procedure can be resumed. from where it was stopped. Each of those instrument changes takes about
30 seconds to 2 minutes, so if there are 10 instrument changes in a case, that add 20 minutes to the
total time of the procedure.
4) There is no tactile feedback or haptics. Several of the adverse events included inadvertent injury to
the aorta, right ventricle, lungs, etc. Sometimes, vessels have been ripped because of lack of feel, and
the force delivered by the grasping forceps might significantly exceed safe limits.
5) The endoscope’s field of vision is very limited and it can be easy to get disoriented, in terms of both
the horizon and the location within the body.
6) Visualization requires insufflation of carbon dioxide at a high flow of 610 liters/minute. While CO
insufflation is also done in non-robotic laparoscopy, it is usually not at such a high flow. That high
flow of CO
can result in absorption of carbon dioxide, which can cause significant metabolic
derangements that affect the heart.
7) Each instrument may be used only 10 times, after which software shutdown occurs, driving up the
costs and making instruments part of the disposable costs. In open and non-robotic laparoscopic
surgeries, some disposable instruments are used, but they are not as expensive as robotic instruments.
8) There is an obligatory setup time, in addition to longer operative times with the robot. Robotic
procedures in all fields of surgery take longer than non-robotic (open or laparoscopic) procedures.
... Despite the many clear benefits of promoting constant innovation in the field of healthcare robotics, its application in the real world presents multiple gaps that can cause harm in a way that humans cannot necessarily correct or oversee [3]. For instance, safety issues such as injury or death may arise if robot surgeons power down mid-operation or operate unintendedly [1,27]. Moreover, as robots' perception, decision-making power, and capacity to perform a task autonomously increase, the human role and its associated responsibilities will necessarily change, and other issues relating to cybersecurity and privacy will become more significant [95]. ...
... They usually also have surgeon consoles and probes, and mobile compartments and tools. In practice, robotic platforms for surgical procedures involve an interplay between the sophisticated automated platform on one side and the surgeon, along with his/her team on the other [1]. The outcome and implications of such shared task performance essentially depends on how they are attuned to one another. ...
... RAS extends the abilities of the doctor, but it also presents new challenges for team fluency, which may be measured by quantitative metrics such as task execution time and the amount of concurrent motion [59]. A revision of 14 years of data from the Food and Drug Administration (FDA) shows that surgical robots can cause injury or death if they spontaneously power down mid-operation due to system errors or imaging problems [1]. Broken or burnt robot pieces can fall into the patient, electric sparks may burn human tissue, and instruments may operate unintendedly, all of which may cause harm, including death [1]. ...
Full-text available
Innovation in healthcare promises unparalleled potential in optimizing the production, distribution, and use of the health workforce and infrastructure, allocating system resources more efficiently, and streamline care pathways and supply chains. A recent innovation contributing to this is robot-assisted surgeries (RAS). RAS causes less damage to the patient's body, less pain and discomfort, shorter hospital stays, quicker recovery times, smaller scars, and less risk of complications. However, introducing a robot in traditional surgeries is not straightforward and brings about new risks that conventional medical instruments did not pose before. For instance, since robots are sophisticated machines capable of acting autonomously, the surgical procedure's outcome is no longer limited to the surgeon but may also extend to the robot manufacturer and the hospital. This article explores the influence of automation on stakeholder responsibility in surgery robotization. To this end, we map how the role of different stakeholders in highly autonomous robotic surgeries is transforming, explore some of the challenges that robot manufacturers and hospital management will increasingly face as surgical procedures become more and more automated, and bring forward potential solutions to ascertain clarity in the role of stakeholders before, during, and after robot-enabled surgeries (i.e. a Robot Impact Assessment (ROBIA), a Robo-Terms framework inspired by the international trade system 'Incoterms', and a standardized adverse event reporting mechanism). In particular, we argue that with progressive robot autonomy, performance, oversight, and support will increasingly be shared between the human surgeon, the support staff, and the robot (and, by extent, the robot manufacturer), blurring the lines of who is responsible if something goes wrong. Understanding the exact role of humans in highly autonomous robotic surgeries is essential to map liability and bring certainty concerning the ascription of responsibility. We conclude that the full benefits the use of robotic innovations and solutions in surgery could bring to healthcare providers and receivers cannot be realized until there is more clarity on the division of responsibilities channeling robot autonomy and human performance, support, and oversight; a transformation on the education and training of medical staff, and betterment on the complex interplay between manufacturers, healthcare providers, and patients.
... In 1983, the first surgical robot was introduced, a device that ultimately incorporated the development of robotic arms to complement ophthalmologic procedures. 1 The Zeus ® system was initially utilized in gynecologic surgery to reconnect fallopian tubes in 1997 and in 2000, the da Vinci Surgery System became the first robotic surgery system approved by the FDA; 2 throughout 2016, there were approximately 1.75 million robotic surgeries (e.g., urology, gynecology, cardiology) conducted in the United States. 3 Robotic surgery enables the surgeon to achieve increased precision via intuitive instrument handling, tremor elimination, and motion scaling. 4 The advent of this technology was envisioned as a clinical upgrade over conventional laparoscopic surgery, which previously obviated the large incisions inherent in open surgeries. ...
Background: Robotic-assisted surgery facilitates the performance of numerous, complex procedures, namely conferring precision, flexibility, and control that is otherwise unavailable with conventional laparoscopy; and compared to open surgery, robotic-assisted surgery is ostensibly associated with fewer complications, reduced intraoperative complications, and shorter hospital stay duration. Nevertheless, the American College of Obstetricians and Gynecologists and the Food and Drug Administration have criticized the pervasive acceptance of robotic-assisted surgery, given the absence of randomized clinical trial data compared to traditional laparoscopy and open procedures, not to mention the increased surgical cost. Conclusions: While the research data continue to be borne out, surgeons should exercise considerable discretion in selecting the surgical approach from which their patients would derive the greatest clinical benefit.
... Robotics and artificial intelligence (AI) are growingly featured in healthcare contexts due to their increased roles and capacities in performing surgery helping in rehabilitation or therapy, currently upshot of the need to reduce human contact (Alemzadeh et al., 2016;Aymerich-Franch and Ferrer, 2020;2. The Difficulties of Regulating Emerging Robots: Lack of Information, General Principles Codes, and the Quest for Better Norms A literature review reveals a paucity in harnessing R&D outcomes to improve existing regulatory instruments (AbouZahr et al., 2007;Höchtl et al., 2016;Athey, 2017;Fosch-Villaronga and Heldeweg, 2018). ...
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From exoskeletons to lightweight robotic suits, wearable robots are changing dynamically and rapidly, challenging the timeliness of laws and regulatory standards that were not prepared for robots that would help wheelchair users walk again. In this context, equipping regulators with technical knowledge on technologies could solve information asymmetries among developers and policymakers and avoid the problem of regulatory disconnection. This article introduces pushing robot development for lawmaking (PROPELLING), an financial support to third parties from the Horizon 2020 EUROBENCH project that explores how robot testing facilities could generate policy-relevant knowledge and support optimized regulations for robot technologies. With ISO 13482:2014 as a case study, PROPELLING investigates how robot testbeds could be used as data generators to improve the regulation for lower-limb exoskeletons. Specifically, the article discusses how robot testbeds could help regulators tackle hazards like fear of falling, instability in collisions, or define the safe scenarios for avoiding any adverse consequences generated by abrupt protective stops. The article’s central point is that testbeds offer a promising setting to bring policymakers closer to research and development to make policies more attuned to societal needs. In this way, these approximations can be harnessed to unravel an optimal regulatory framework for emerging technologies, such as robots and artificial intelligence, based on science and evidence.
... It remains to be seen if those are particularly difficult to anticipate and to prevent compared to clearly categorized complications. Accordingly, based on thorough literature review and author consensus, we adjusted and complemented the predefined catalog of complications proposed by Vetterlein et al. [14] by adding RARC-specific complications, such as ascites [23], port hernia [24], compartment syndrome (extremity) [25], skin damage due to surgery or robotic system malfunction [26] and general diversion-associated complications such as urostomy prolapse [27]. ...
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Objective To assess suitability of Comprehensive Complication Index (CCI®) vs. Clavien–Dindo classification (CDC) to capture 30-day morbidity after robot-assisted radical cystectomy (RARC). Materials and methods A total of 128 patients with bladder cancer (BCa) undergoing intracorporeal RARC with pelvic lymph node dissection between 2015 and 2021 were included in a retrospective bi-institutional study, which adhered to standardized reporting criteria. Thirty-day complications were captured according to a procedure-specific catalog. Each complication was graded by the CDC and the CCI®. Multivariable linear regression (MVA) was used to identify predictors of higher morbidity. Results 381 complications were identified in 118 patients (92%). 55 (43%), 43 (34%), and 20 (16%) suffered from CDC grade I–II, IIIa, and ≥ IIIb complications, respectively. 16 (13%), 27 (21%), and 2 patients (1.6%) were reoperated, readmitted, and died within 30 days, respectively. 31 patients (24%) were upgraded to most severe complication (CCI® ≥ 33.7) when calculating morbidity burden compared to corresponding CDC grade accounting only for the highest complication. In MVA, only age was a positive estimate (0.44; 95% CI = 0.03–0.86; p = 0.04) for increased cumulative morbidity. Conclusion The CCI® estimates of 30-day morbidity after RARC were substantially higher compared to CDC alone. These measurements are a prerequisite to tailor patient counseling regarding surgical approach, urinary diversion, and comparability of results between institutions.
... OBOTICS and Artificial Intelligence (AI) have increased productivity and resource efficiency in the industrial and retail sectors, and now there is an emerging interest in realizing a comparable transformation in other sectors, such as healthcare. Such a shift is encouraged by the urge to increase care quality and safety while simultaneously restraining expenditure [1] and lately reducing human contact [2]. However, inserting robots in such a remarkably sensitive domain of application raises puzzling legal and ethical considerations [3,4]. ...
Despite the growing body of literature highlighting the legal and ethical questions robots raise, robot developers struggle to incorporate other aspects than mere physical safety into robot design to make them comprehensively safe. The chemical, food, and pharmaceutical industries established years ago use evidence-based frameworks that ensure the safety of these products EU-wide. However, these evidence-based frameworks have yet to be seen for robot technology. As a result, current robot technology raises many legal and ethical issues. The PROPELLING project aims to investigate how robot testbeds can be harnessed as data generators for standard-makers. To this end, the project focuses on testing safety requirements for lower-limb exoskeletons to understand whether standards, particularly ISO 13482:2014, address safety sufficiently and comprehensively and use the H2020 Eurobench testing beds and data as a means for appraising the standard. We suggest that linking experimentation settings with standard-making processes could speed up the creation, revision, or discontinuation of norms governing robot technology.
... 4,7,8 Assuring adequate preparation of the surgeons, before they start their surgical activity on human cases, may have a positive impact on patient outcomes by reducing the number of adverse events occurring. 9 A surgical training program based on objective, fair, transparent, and validated performance metrics is needed. ...
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Introduction The introduction of robot-assisted surgical devices requires the application of objective performance metrics to verify performance levels. Objective To develop and validate (face, content, response process, and construct) the performance metrics for a robotic dissection task using a chicken model. Methods In a procedure characterization, we developed the performance metrics (i.e., procedure steps, errors, and critical errors) for a robotic dissection task, using a chicken model. In a modified Delphi panel, 14 experts from four European Union countries agreed on the steps, errors, and critical errors (CEs) of the task. Six experienced surgeons and eight novice urology surgeons performed the robotic dissection task twice on the chicken model. In the Delphi meeting, 100% consensus was reached on five procedure steps, 15 errors and two CEs. Novice surgeons took 20 min to complete the task on trial 1 and 14 min during trial two, whereas experts took 8.2 min and 6.5 min. On average, the Expert Group completed the task 56% faster than the Novice Group and made 46% fewer performance errors. Results Sensitivity and specificity for procedure errors and time were excellent to good (i.e., 1.0-0.91) but poor (i.e., 0.5) for step metrics. The mean interrater reliability for the assessments by two robotic surgeons was 0.91 (Expert Group inter-rater reliability = 0.92 and Novice Group = 0.9). Conclusions We report evidence which supports the demonstration of face, content, and construct validity for a standard and replicable basic robotic dissection task on the chicken model.
Objective: Comparative assessment of immediate and long-term results of robot-assisted and conventional endoscopic technologies in the Russian Federation. Material and methods: Searching for primary trials devoted to robot-assisted (RAE) and traditional video endoscopic (TVE) surgeries in the Russian Federation was carried out in the e-library and CENTRAL Cochrane databases. We used the recommendations of the Center for Expertise and Quality Control of Medical Care (2017, 2019) and the current version of the Cochrane Community Guidelines (2021). These guidelines define the features of meta-analysis of non-randomized comparative studies. Review Manager 5.4 software was used for statistical analysis. Results: We enrolled 26 Russian-language primary sources (3111 patients) including 1174 (38%) ones in the RAE group and 1937 (62%) patients in the TVE group. There were no randomized controlled trials in the Russian Federation, and all primary studies were non-randomized. We found no significant between-group differences in surgery time, incidence of intraoperative complications, intraoperative blood loss in thoracic surgery, urology and gynecology, conversion rate, postoperative hospital-stay, postoperative morbidity (in abdominal surgery, urology and gynecology), postoperative mortality. We observed slightly lower intraoperative blood loss for RAE in abdominal surgery and lower incidence of postoperative complications in robot-assisted thoracic surgery. These results can be compromised by methodological quality of comparative studies, significant heterogeneity and systematic errors. Conclusion: Currently, we cannot confirm the benefits of robot-assisted technologies, since this approach does not worsen or improve treatment outcomes. Further high-quality studies are needed.
Conference Paper
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We present a telerobotics research platform that provides complete access to all levels of control via open-source electronics and software. The electronics employs an FPGA to enable a centralized computation and distributed I/O architecture in which all control computations are implemented in a familiar development environment (Linux PC) and low-latency I/O is performed over an IEEE-1394a (FireWire) bus at speeds up to 400 Mbits/sec. The mechanical components are obtained from retired first-generation da Vinci ® Surgical Systems. This system is currently installed at 11 research institutions, with additional installations underway, thereby creating a research community around a common open-source hardware and software platform.
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This paper presents a simulation framework for recreating the realistic safety hazard scenarios commonly observed in robotic surgical systems, which can be used to prepare surgical trainees for handling safety-critical events during procedures. The proposed simulation platform is composed of a surgical simulator based on an open-source surgical robot platform, Raven II, integrated with a software-based fault-injection engine, which automatically inserts faults into different modules of the robotic software. We demonstrate the value of software-based fault injection for simulating representative safety hazards seen in the adverse events reported to the FDA MAUDE database, by performing experiments both in simulation and on the actual Raven II robot.
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Robotic telemanipulators have evolved to assist the challenges of minimally invasive mitral valve surgery (MVS). A systematic review was performed to provide a synopsis of the literature, focusing on clinical outcomes and cost-effectiveness. Structured searches of MEDLINE, Embase, and Cochrane databases were performed in August 2013. All original studies except case-reports were included in qualitative review. Studies with ≥50 patients were presented quantitatively. After applying inclusion and exclusion criteria to the search results, 27 studies were included in qualitative review, 16 of which had ≥50 patients. All studies were observational in nature, and thus the quality of evidence was rated low to medium. Patients generally had good left ventricular performance, were relatively asymptomatic, and mean patient age ranged from 52.6-58.4 years. Rates of intraoperative outcomes ranged from: 0.0-9.1% for conversion to non-robotic surgery, 106±22 to 188.5±53.8 min for cardiopulmonary bypass (CPB) time and 79±16 to 140±40 min for cross-clamp (XC) time. Rates of short-term postoperative outcomes ranged from: 0.0-3.0% for mortality, 0.0-3.2% for myocardial infarction (MI), 0.0-3.0% for permanent stroke, 1.6-15% for pleural effusion, 0.0-5.0% for reoperations for bleeding, 0.0-0.3% for infection, and 1.1-6% for prolonged ventilation (>48 hours), 1.5-5.4% for early repair failure, 12.3±6.7 to 36.6±24.7 hours for intensive care length of stay, 3.1±0.3 to 6.3±3.9 days for hospital length of stay (HLOS) and 81.7-97.6% had no or trivial mitral regurgitation (MR) before discharge. All subtypes of mitral valve prolapse are repairable with robotic techniques. CPB and XC times are long, and novel techniques such as the Cor-Knot, Nitinol clips or running sutures may reduce the time required. The overall rates of early postoperative mortality and morbidity are low. Improvements in postoperative quality of life (QoL) and expeditious return to work offset the increase in equipment and intraoperative cost. Evidence for long-term outcomes is as yet limited.
The da Vinci(®) Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) is a computer-assisted (robotic) surgical system designed to enable and enhance minimally invasive surgery. The Food and Drug Administration (FDA) has cleared computer-assisted surgical systems for use by trained physicians in an operating room environment for laparoscopic surgical procedures in general, cardiac, colorectal, gynecologic, head and neck, thoracic and urologic surgical procedures. There are substantial numbers of peer-reviewed papers regarding the da Vinci(®) Surgical System, and a thoughtful assessment of evidence framed by clinical opinion is warranted. The SAGES da Vinci(®) TAVAC sub-committee performed a literature review of the da Vinci(®) Surgical System regarding gastrointestinal surgery. Conclusions by the sub-committee were vetted by the SAGES TAVAC Committee and SAGES Executive Board. Following revisions, the document was evaluated by the TAVAC Committee and Executive Board again for final approval. Several conclusions were drawn based on expert opinion organized by safety, efficacy, and cost for robotic foregut, bariatric, hepatobiliary/pancreatic, colorectal surgery, and single-incision cholecystectomy. Gastrointestinal surgery with the da Vinci(®) Surgical System is safe and comparable, but not superior to standard laparoscopic approaches. Although clinically acceptable, its use may be costly for select gastrointestinal procedures. Current data are limited to the da Vinci(®) Surgical System; further analyses are needed.
To evaluate the adverse events encountered during robotic gynecologic surgery, as reported to the FDA MAUDE database from January 2006 to December 2012. A search of the FDA MAUDE database was performed by brand name 'da Vinci' and manufacturer 'Intuitve Surgical'. Reports reflecting gynecologic procedures either by description or procedure name were included. A record of reports was kept to ensure no duplicates were added. The date and type of event (operator-related error, technical system failures, or surgical injuries attributed to the use of the robot) as well as the clinical outcome were recorded. Twenty six percent of the reported events (n=73) resulted in injury, and 8.5% (n=24) resulted in death. Notably, while adnexal procedures accounted for less than 3% of the cohort, they compromised 20% of the fatality cases. Twenty-one percent of injuries were attributed to operator-related error, 14% to a technical system failure, and 65% were not directly related to the use of the robot.Fifteen fatal cases were reported during planned hysterectomy. Four of those cases resulted in an injury to a major blood vessel (three iliac and one aortic injuries), although detailed description of how the injury occurred was absent from the event description. It is important to continue to evaluate the occurrence of injuries during robot-assisted surgery in an effort to identify unique challenges associated with this advanced technology.
Introduction: The da Vinci surgical system requires the use of electrosurgical instruments. The re-use of such instruments creates the potential for stray electrical currents from capacitive coupling and/or insulation failure with subsequent injury. The morbidity of such injuries may negate many of the benefits of minimally invasive surgery. We sought to evaluate the rate and nature of electrosurgical injury (ESI) associated with this device. Methods: The Manufacturer and User Facility Device Experience (MAUDE) database is administered by the US Food and Drug Administration (FDA) and reports adverse events related to medical devices in the United States. We analyzed all incidents in the context of robotic surgery between January 2001 and June 2011 to identify those related to the use of electrosurgery. Results: In the past decade, a total of 605 reports have been submitted to the FDA with regard to adverse events related to the da Vinci robotic surgical platform. Of these, 24 (3.9%) were related to potential or actual ESI. Nine out of the 24 cases (37.5%) resulted in additional surgical intervention for repair. There were 6 bowel injuries of which only one was recognized and managed intra-operatively. The remainder required laparotomy between 5 and 8 days after the initial robotic procedure. Additionally, there were 3 skin burns. The remaining cases required conservative management or resulted in no harm. Conclusion: ESI in the context of robotic surgery is uncommon but remains under-recognized and under-reported. Surgeons performing robot assisted surgery should be aware that ESI can occur with robotic instruments and vigilance for intra- and post-operative complications is paramount.
Since its Food and Drug Administration (FDA) approval, robot-assisted laparoscopic surgery has grown with expanding indications. One factor used to expand indications is device-related complications. We designed a study to evaluate device-related robotic surgery complications reported to FDA. We searched the FDA device-related complication database, LexisNexis, and PACER (Public Access to Court Electronic Records) to identify robotic surgery-related complications over a 12-year period (January 1, 2000 to August 1, 2012). Cases from LexisNexis and PACER were cross-referenced with the FDA database to determine cases where an FDA report was inaccurate, filed late or not filed. A total of 245 events were reported to the FDA during the study period, including 71 deaths and 174 nonfatal injuries. Median time to report an event to the FDA was 30 days (range = 0-930 days). Eight cases were identified from the LexisNexis and PACER searches where FDA reports were improperly filed. In five of these, no report was filed with a mean follow-up of 4.1 years (range = 2.3-5.8 years). In the three cases where a report was filed, the mean time between the event and the FDA report was 20.4 months (611 days, range = 292-930 days). It is important that the true incidence of complications with robotic-assisted laparoscopic surgery be known to ensure continued safe innovation.