Healthcare Robotics (Annotated)

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Fifteen years ago, healthcare robotics was largely science fiction. One company called Robodoc,^[65] a spin-out from IBM, developed robotic systems for orthopedic surgeries, such as hip and knee replacements. The technology worked, but the company struggled commercially, and was ultimately shut down and acquired for its technology.^[66] More recently, though, the research and practical use of surgical robotics has exploded.

In 2000 Intuitive Surgical^[67] introduced the da Vinci system, a novel technology initially marketed to support minimally invasive heart bypass surgery, and then gained substantial market traction for treatment of prostate cancer and merged with its only major competition, Computer Motion, in 2003. The da Vinci, now in its fourth generation, provides 3D visualization (as opposed to 2D monocular laparoscopy) and wristed instruments in an ergonomic platform. It is considered the standard of care in multiple laparoscopic procedures, and used in nearly three quarters of a million procedures a year,^[68] providing not only a physical platform, but also a new data platform for studying the process of surgery.

The da Vinci anticipates a day when much greater insight into how medical professionals carry out the process of providing interventional medical care will be possible. The presence of the da Vinci in day-to-day operation has also opened the doors to new types of innovation—from new instrumentation to image fusion to novel biomarkers—creating its own innovation ecosystem. The success of the platform has inspired potential competitors in robotic surgery, most notably the Alphabet spin-off Verb, in collaboration with J&J/Ethicon.^[69] There are likely to be many more, each exploring a unique niche or space and building out an ecosystem of sensing, data analytics, augmentation, and automation.

Intelligent automation in hospital operations has been less successful. The story is not unlike surgical robotics. Twenty years ago, one company, HelpMate, created a robot for hospital deliveries,^[70] such as meals and medical records, but ultimately went bankrupt. More recently, Aethon^[71] introduced TUG Robots for basic deliveries, but few hospitals have invested in this technology to date. However, robotics in other service industries such as hotels and warehouses, including Amazon Robotics (formerly Kiva), are demonstrating that these technologies are practical and cost effective in at least some large-scale settings, and may ultimately spur additional innovation in health care.

Looking ahead, many tasks that appear in healthcare will be amenable to augmentation, but will not be fully automated. For example, robots may be able to deliver goods to the right room in a hospital, but then require a person to pick them up and place them in their final location. Walking a patient down the corridor may be relatively simple once a patient is standing in a walker (though will certainly not be trivial for patients recovering from surgery and/or elderly patients, especially in corridors crowded with equipment and other people). Driving a needle to place a suture is relatively straightforward once the needle is correctly placed.^[72] This implies that many future systems will involve intimate interaction between people and machines and require technologies that facilitate collaboration between them.

The growth of automation will enable new insights into healthcare processes. Historically, robotics has not been a strongly data-driven or data-oriented science. This is changing as (semi)automation infiltrates healthcare. As the new surgical, delivery, and patient care platforms come online, the beginnings of quantification and predictive analytics are being built on top of data coming from these platforms.^[73] This data will be used to assess quality of performance, identify deficiencies, errors, or potential optimizations, and will be used as feedback to improve performance. In short, these platforms will facilitate making the connection between what is done, and the outcome achieved, making true “closed-loop” medicine a real possibility.

[65] ROBODOC, accessed August 1, 2016, http://www.robodoc.com/professionals.html.

[66] THINK Surgical, accessed August 1, 2016, http://thinksurgical.com/history.

[67] Intuitive Surgical, accessed August 1, 2016, http://www.intuitivesurgical.com.

[68] Trefis Team, "Intuitive Surgical Maintains Its Growth Momentum With Strong Growth In Procedure Volumes," Forbes, January 22, 2016, accessed August 1, 2016, http://www.forbes.com/sites/greatspeculations/2016/01/22/intuitive-surgical-maintains-its-growth-momentum-with-strong-growth-in-procedure-volumes/#22ae6b0939a1.

[69] Evan Ackerman, "Google and Johnson & Johnson Conjugate to Create Verb Surgical, Promise Fancy Medical Robots," IEEE Spectrum, December 17, 2015, accessed August 1, 2016, http://spectrum.ieee.org/automaton/robotics/medical-robots/google-verily-johnson-johnson-verb-surgical-medical-robots.

[70] John M. Evans and Bala Krishnamurthy, "HelpMate®, the trackless robotic courier: A perspective on the development of a commercial autonomous mobile robot," Lecture Notes in Control and Information Sciences 236, June 18, 2005 (Springer-Verlag London Limited, 1998), 182-210, accessed August 1, 2016, http://link.springer.com/chapter/10.1007%2FBFb0030806.

[71] Aethon, accessed August 1, 2016, http://www.aethon.com.

[72] Azad Shademan, Ryan S. Decker, Justin D. Opfermann, Simon Leonard, Axel Krieger, and Peter CW Kim, “Supervised Autonomous Robotic Soft Tissue Surgery,” Science Translational Medicine 8, no. 337 (2016): 337ra64-337ra64.

[73] Carolyn Chen, Lee White, Timothy Kowalewski, Rajesh Aggarwal, Chris Lintott, Bryan Comstock, Katie Kuksenok, Cecilia Aragon, Daniel Holst, and Thomas Lendvay, "Crowd-Sourced Assessment of Technical Skills: a novel method to evaluate surgical performance." Journal of Surgical Research 187, no. 1 (2014): 65-71.