Robotic manufacturers will fight a war to get robots into the operating room, but even if we figure out how to create a one-stop-shop robot for the OR, there’s another conundrum: how do we collect and use the data coming from robots to drive improvements in outcomes and patient care?

The ultimate value that robotics data will provide is in improving outcomes through better recording of process, approach, measurements and outcomes data. Collected through an individual robot in one OR, that data is likely interesting, but not significantly valuable. It’s also not linked to the patient’s ultimate outcomes data. Across hundreds of hospitals, ORs and procedures, however, that data becomes amazingly valuable. Imagine the level of information that shows the differences and similarities in how surgeons operate and links it to the hospital’s data on patient outcomes. Right now, there are likely hundreds of ways that surgeons do a full knee replacement with differing results. Which work best? Nobody really knows because the data is very difficult to obtain, coming from many potential sources. But with the collection of surgical robotic data linked to outcomes information from 90-day patient management programs, we’ll be able to isolate what activities and processes drive the best outcomes. We can help surgeons improve execution and hospitals improve care, which ultimately improves patient outcomes and reduces the total cost to the system. That would be amazing for all parties involved.

In their first iterations, surgical data from robots is very difficult to obtain. Most surgical robots are not yet hooked up to hospital systems to directly interface with them. More importantly, they cannot easily connect to the manufacturer and require a USB to download data. That means a person from either the hospital or the manufacturer needs to pull the data manually to use it—not an ideal way to capture data. And there is little to no connection to the ultimate patient outcomes, meaning the data is not easily usable to drive improvements in care.

To get to this dream future state, medtech companies have to overcome several obstacles:

• Connectivity: Most medtech organizations plan to connect their robots to hospital sysytems, but since many of the first-gen robots don’t have that level of connectivity, that’s still a few years away. As mentioned, data can be pulled through thumb drives, but it’s a very manual effort.
• Data management: Most medtech companies currently manage existing, one-dimensional customer, sales and market data. The data from robotics, on the other hand, is going to be very complex, which is why it’s so valuable. Because the data will have multiple dimensions, it not only needs to pull from the robot, but also be linked to patient progress and outcomes.
• Analytics: While medtech companies have been making significant strides in analytics, there’s still much to be desired. Many organizations have improved their reporting, but haven’t really dove into true analytics around very large data sets.
• Sharing: Who really owns the robotics data? Is it the hospital using the machine? Is it the medtech organizations, because they created the robot? Is it really the patient’s data? These are key questions that need to be ironed out to ensure everyone benefits from robotic data and it is shared across the right entities. The procedure data needs to be married up to the patient data, both pre- and post-operative, to understand how all the factors impact outcomes.

Given these obstacles, there are several strategies medtech firms need to drive long-term success with robotic data:

• Start discussions with hospitals now. Rather than discussing the product, sales and contracting or anything related to robotics, start to talk to hospitals about the future of data, the partnerships you want to create and the impact you want to have. Partner with hospitals in a different way than medtech firms historically have by determining the mutual value of information and how to drive improved outcomes.
• Get your IT systems and tech team ready now. Figure out how hospital systems work and how they leverage EMRs like Epic to capture outcomes data. Be ready to connect the robot to the hospital systems and the hospital systems to the medtech company.
• Consider creating partnerships with EMRs. This will make it easier to push robotic data to the hospital and gather data coming from the hospitals.
• Define the structure and enhance your own data management capabilities. To inform how data will be set up in the robotic systems, determine what the data might look like coming from the robots and hospitals and figure out how to gather and store that data so it can be easily analyzed, and how you will aggregate and use the data.

There’s an amazing amount of opportunity and a wealth of data that will come from robotics, but the life sciences industry is not ready to handle it yet. While getting the product right and getting it to market is critical, if we’re not ready to manage the output of these robotic systems or know how they work with other stakeholders’ systems, half of their value disappears instantly. Medtech organizations have an opportunity to change how care is provided and improve the lives of millions of future patients. Make sure you’re ready.

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