Today’s surgical augmentation systems require substantial capital expenditures that can stress the resources of a hospital seeking to provide cutting-edge service to their patients. These acquisition structures result in business dynamics that have the potential to influence medical decisions (for example, the decision to refer some procedures to a larger hospital). The impact of such large capital purchases also puts more pressure on equipment utilization to justify the costs involved in individual procedures.
Today’s cloud-based computing and infrastructure have shifted to prioritize cost-effective pay-as-you-go models offering “everything-as-a-service”. Businesses today no longer must purchase expensive computer equipment to host enterprise applications, such as those for finance and accounting, customer relationship management, or data analytics. Using the same strategies to rethink the way surgical platforms are procured can substantially influence the ability of hospitals to acquire novel technology, assist practitioners with minimally invasive surgery, and improve patient satisfaction.
This same dynamic could apply to surgical robots. What if we could overcome the limitations of physical hardware by developing per patient usage pricing models with no capital expense outlay? What if a ground-up design made surgical robots inherently less expensive to produce? What if maintenance was also factored into usage, thereby lowering fixed costs, and reducing the need for hospitals to have trained personnel? What if the robots were designed for lowing surgical procedural costs to begin with?
Under such a structure, device manufacturers recoup their costs through increased use of their systems. In addition, the choices for the healthcare sector will no longer be limited to “one price fits all” offerings. Each case can be priced out according to what the surgeon chooses to utilize.
Surgery-as-a-Service models democratize access to advanced technologies among healthcare providers. Local, community hospitals and ambulatory surgical centers (ASCs) can have access to the latest equipment and newest technologies.
Rethinking surgical platform design could also add new dimensions to data processing. What if one considered a robot to be a perfect receptacle for surgical data? What if a system that plays an assistive or augmentative role could capture the surgeon’s experienced hand movements? These could be gathered from many surgeons and procedures to provide “best practice” guidance to other surgeons while providing potential suggestions in real-time. Such a repository could help raise the level of proficiency for all surgeons and help to continually increase their effectiveness.
The collective knowledge associated with updated data management could be effective for training inexperienced surgical residents. Rather than traditional teaching methods of observing surgeons at work as well as critiquing residents in the operating room or surgical suite, residents could learn in the most hands-on way possible – with real-time comparisons to successful surgeons.
For years, surgical platforms have been excellent data collectors, able to capture the exact interactions between surgeon and patient in a way no other system can. Mining this data can produce two extraordinary benefits; the most successful hand movements combined with procedural steps and judgment can be captured and offered to other surgeons as recommendations to sharpen surgical skills.