Industry Insights

Robotics Programs in Europe and Asia

Watch this week’s video to open the discussion about Robotic Programs around the world!


Reducing Medical Malpractice in Your Robotics Program

Watch this week’s video to learn about reducing medical malpractice risk to your program.


Data Deficiencies are Costing You Millions: Part 2

Unrecognized Costs in Computer-Assisted Surgery: 

Equipment Malfunction and Repair

Several years ago, CAVA encountered a request from a hospital to evaluate costs that were not being fully represented in the financial analysis of each robotic surgical case.  As a beginning robotic surgical program, scope repairs were frequent and reposable instrument dysfunction was high.  The full breadth of the costs associated with the issue and the underlying factors influencing these events were unrecognized.

Together, we discovered that scope repair costs were underappreciated.  Due to a multitude of factors including staff training, sterile processing and scope handling, the incidence of scope repair was much higher than anticipated. Scope repair provided by the vendor despite a maintenance contract approached $9k/incident and over $60,000/year!

Reposable malfunction was much more difficult to quantify, however. When an instrument malfunctioned, it was sent back to the vendor for evaluation.  The vendor determined the reason for the malfunction and whether the unused “lives” of the reposable instrument remaining could be credited back to the hospital.  In the cost accounting software and methodology of the hospital, keeping track of these incidents was challenging.  However, CAVA’s analysis of the reasons for instrument failure revealed several things: surgeon error and potential internal collisions were frequently a cause of instrument malfunction as indicated by things such as fracture of the “shaft” of the instrument.  Moreover, many of these events went unrecognized during the operation.  Another cause was attributed to excessive or improper handling and processing of the reposable instruments.  This brought to light important changes in turnover and sterile processing protocol that are important to maximizing the life of these reposables.  Lastly, there were “unknown” factors in which a specific cause for instrument malfunction could not be found, including things like a cable being displaced, the instrument not responding well, and the scissors not cutting well.

CAVA’s advice:  Avoid wracking-up unmonitored costs of computer-assisted surgery.  Be sure to track your “under the radar” costs and repairs.  More importantly, have processes in place such as handling of reposables and scopes in all departments (i.e., the operating room and sterile processing) because the financial consequences of unmonitored repair costs are significant.

Data Deficiencies are Costing You Millions

Part 1  

Unrecognized Costs in Computer-Assisted Surgery:

The Learning Curve

This is Part 1 in a series highlighting how gaps in data collection, data analysis, or cost accounting leads to unrecognized financial losses in laparoscopy and computer-assisted surgery.  This series draws upon CAVA Robotics’ extensive experience helping our client hospitals with their OR’s clinical and financial data analytics and the associated lessons learned in the process.

As a starting point, let’s focuses on the unrecognized costs in computer-assisted surgery that are either difficult to assess, impossible to ascertain, or frequently neglected.  For these reasons, these areas are often not considered in the “cost” of running a robotic program.

The first–and most costly–is the cost of the robotic learning curve.  Almost all surgeons are already extremely efficient in their laparoscopic or open surgery.  However, in making the transition to robotics, a unique, well defined “learning curve” is associated with every specialty and with every operation. During this learning curve, excessive time in the operating room, excess consumption of supplies, medical malpractice exposure, and risk to the patient usually exist.  It is therefore imperative that a robotic program establish expectations regarding the progression of a “novice” robotic surgeon through this learning curve as well as empower the surgeon with the resources necessary to conquer this learning curve in the expected time/case frame.

To give a concrete example that CAVA frequently sees in the healthcare environment, let’s take an example of inguinal hernia repair.  Hospital X does laparoscopic bilateral inguinal hernia repair with a net margin of +$700/case and completes the operation in about 45 minutes.   When it starts doing the case robotically, that same margin was expectantly negative.  After 100 cases however, that margin is still -$1200/case!  Factors that  contribute to the negative margin include excessive robotic reposable use ($200/case), excessive operative time (60 min/case longer) and excessive supply usage ($600/case).  This example shows how the “urban legend” of robotics that it “costs too much” is perpetuated, but is actually an all-too-common phenomenon in many robotic programs.

CAVA’s advice:  Avoid this happening in your computer-assisted surgical operating room by making sure that you:

  • Define your surgeons’ learning curve
  • Empower your surgeons with the knowledge and resources needed to progress through the learning curve and get out the other side!
  • Monitor your computer-assisted surgical performance to make sure that excellent clinical and financial outcomes are maintained

Next: Part 2…Unrecognized costs in computer-assisted surgery – equipment malfunction and repair.

Save Your Program Millions, Just Follow the Data!

Be sure to watch this week’s video from CAVA Robotics to learn how to save your program by tracking your data.


How Efficient is Your Robotic Program?

Watch our short video about robotic efficiency and cost, where we dispel common myths about robotic programs.  Is your robotic program working at peak performance?

Robotics 101 (Part 2):



Too often, I hear surgeons and non-surgeons say that suturing takes too long and is too tedious and therefore these laparoscopic shortcuts are desired in robotics.

To these individuals, I share two opinions.

First, an investment in robotic suturing should be made to make the task efficient.  Again, this is Robotics 101. All too often, attempts to suture in robotics prove long and frustrating with the surgeon abandoning the task in favor of “shortcuts”.  A dedication to getting through the suturing learning curve is essential not only because of the financial and clinical benefits, but also because suturing is the largest advantage for which the robot was created. The wrist facilitates suturing foremost as well as other tasks, such as dissection and retraction. To abandon suturing is like abandoning the main reason for which the robot was created: to overcome the shortcomings of laparoscopy.

Second, suturing should NOT be learned by practicing on a patient.  Another advantage of robotics over laparoscopy is that simulation provides a more realistic experience on which to train.  The most “real life” simulation tasks available are suturing regardless of the simulation platform chosen.  It is more realistic than even tying.  Suturing must be mastered on a simulator at the convenience and pace of the “apprentice” surgeon.  Trying to learn this task in the operating room places a burden and risk upon the patient, the hospital (through increased indirect, operating room time costs), and society (through increased healthcare costs).  Learning this task in the operating room is irresponsible on the part of the surgeon and hospital. Suturing is a task that must be mastered, and is another fundamental to Robotics 101.

Robotics 101:



One of the biggest problems confronting robotic surgery remains the persistent lack of understanding regarding the basic, fundamental tenant of robotics.

First and foremost: robotic surgery is a reproduction of open surgery, not laparoscopic surgery.  

Laparoscopic surgery is a series of “shortcuts” like tacking instead of suturing, stapling or energy ligation instead of “clamp, cut, and tie,” and balloon dissection instead of manual dissection.  Shortcuts are necessary because a surgeon is limited in manual dexterity with laparoscopy and, therefore, instruments such as tackers were created to replace normal “open” tasks.

The problem with these “shortcuts” is that they are much more expensive and less reliable than their open counterparts.

One of the best examples is examining how a surgeon deals with ligating a large cystic duct.  The common laparoscopic approach is to use a laparoscopic stapler across the cystic duct, and often taking another step to “endoloop” the stump because of the concern of leakage through clips. This laparoscopic solution costs $400, while the integrity of the closure may provide postoperative concerns for the surgeon.  The traditional open solution, however, involves just a single silk tie ligation. It costs pennies and the surgeon does not worry about the integrity of the closure.  By reproducing open surgery in robotics, the benefits of minimally invasive surgery can be delivered in a financially and clinically advantageous compared to laparoscopy. This is Robotics 101 in action.

Please note, however, that I’m not suggesting that laparoscopic instruments do not have a place in robotic surgery.  They do, especially in the learning curve of a surgeon transitioning from laparoscopy to robotics.  During a learning curve, for example, a surgeon should worry only about getting through an operation safely and at a comfortable pace.  Concerns of the financial implications and the need to speed up to save on indirect time costs or select disposables based on reduced cost should not burden the “apprentice” surgeon.  During this period, disposables such as the V-loc suture or tackers can provide the “apprentice” surgeon with comfort or reassurance and provide a positive experience to a learning curve case in the form of time efficiency and convenience.

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