While artificial intelligence and robotics have the potential to solve many of today’s healthcare challenges, several obstacles must be overcome for these technologies to dramatically improve our care delivery system.
Artificial intelligence (AI) refers to the use of algorithms that approximate human cognition in the analysis of complex medical data, with the aim of determining relationships between prevention or treatment techniques and patient outcomes. Robotics primarily deals with the design, development and operation of robots for activities such as patient monitoring and evaluation as well as surgical assistance. Together, AI-powered robotics have the potential to completely change the way healthcare is delivered, as machines may someday become powerful and smart enough to replace physicians and medical staff.
While fully autonomous robotic systems are still far away, there are a number of advantages that AI and robotics already offer in clinical settings and physician training to advance care and improve outcomes. Moreover, the clinical application of robotic/AI technologies is growing, with more big-name providers coming into the scene alongside new and emerging technologies. These include the da Vinci system that enables robot-assisted surgery; the Cyberknife, which uses images and computer-controlled robotics to radiate tumors; Babylon Health’s AI-powered chatbot to assist in remote patient diagnosis; and Memorial Sloan Kettering’s use of Watson Oncology to interpret cancer patients’ clinical data.
Despite great promise from AI and robots, several challenges are still preventing rapid adoption.
Technology and data constraints
Given the current challenges of EHR integration, one of the largest hurdles would be the availability of complete and comprehensive patient data. Many AI technologies rely on the ability to digest large sets to perform advanced analytics, but without a complete understanding of the patient’s medical history, the system fundamentally doesn’t work. Worse, it may put the patient’s health at risk.
Unfortunately, the means by which healthcare providers collect and maintain patient data today is insufficient. Exacerbating this issue are the inherent complications around interoperability. With hundreds of EHRs, each with different data architectures, it’s extremely difficult for providers to keep a single, comprehensive health record for a patient. The inability to integrate with other EHRs to understand the full patient history remains a challenge for any machine learning technology.
Providers that want to successfully adopt AI-related technologies must first overcome technological deficiencies surrounding comprehensive patient data. While full interoperability may be years away, providers can still leverage AI technologies by manually collecting all relevant patient data, performing the appropriate diligence before a procedure or diagnosis.
Financial and economic concerns
In addition to technological limitations, AI and robotics present significant financial concerns for smaller or mid-sized providers. The high acquisition and implementation costs, along with annual maintenance and contract costs, places a significant financial burden on organizations. In addition, providers also need to consider the costs of robotic instruments and accessories, which are procured and replaced on a per-procedure basis. Finally, many of these technologies require extensive physician and staff training, costly workflow changes, and in some cases, changes to the EHR.
Further, there have been studies showing that robotic surgeries can cost roughly 10 times more than traditional surgeries, with no evidence that outcomes are significantly better than traditional methods. Worse, there have been instances where robotic malfunction has resulted in injuries and even death, resulting in the potential for legal fees and other unforeseen costs.
Finally, in today’s health insurance system, how do providers justify the cost of delivering the very best care to patients when insurers today are only reimbursing for good enough care? For larger healthcare institutions such as Mayo Clinic, MD Anderson, and Partners Healthcare, there is plenty of funding to take on innovative and exploratory AI programs from top vendors; however, smaller institutions may struggle to justify the high cost of introducing AI and robotic technologies until they are more proven.
Legal, regulatory and ethical risk
Among the biggest concerns facing providers adopting AI and robotics are the legal, regulatory and ethical concerns. Because of the sensitivity and relative uncertainty around effectiveness of robotic and AI technologies, the risk is greater, particularly when the procedure could have been successfully performed by a physician. Navigating these challenges may require providers to adopt a new approach to governance and legal arbitrage.
Using fully autonomous, AI-powered robots in operating rooms requires machines to be smart enough to calculate risks and make “human-like” decisions. However, what criteria should we ethically program these machines to use in order to “calculate” the risks and make clinical decisions? Consider a future world where we’ve introduced an AI/robotic delivery system; how does the robot choose one life over another when both baby and mother’s heart rate begins to drop and both are entering a critical state? Equally important, how would the patient, families and legal system respond?
Moreover, having access to a patient’s complete health history, a requisite for many AI technologies, also presents some major challenges to patient privacy and security. This raises regulatory concerns for hospital IT—how patient data be securely protected, and how will access to data be controlled?
For organizations that are ready and willing to adopt robotic and AI technologies, gaining a thorough understanding the various risks as well as the limitations of each technology, developing a strong governance model, and implementing very rigid protocols and workflows will enable them to protect themselves against such risks and litigation, demonstrate successes, and help shape the delivery of these technologies to improve adoption.
Arguably the biggest barrier to full-scale adoption is social resistance. Even if organizations are willing to take on the inherent risk associated with clinical robotics and AI, how do organizations ensure that the doctors and nurses are comfortable adopting robotic care; that insurers are willing to pay for it; and that patients are willing to receive it?
Will doctors ever be willing to concede that their training-based skills could be performed, perhaps better, by a robot? Historically robotic-enabled job replacement has typically impacted low-wage, routine jobs; however, AI is changing the game; creative and intelligent white-collar jobs will also be impacted, and doctors are not excluded. For example, a machine that reads patient’s medical images could quickly replace radiologists. Or one that can calculate and administer IV medications could replace an anesthesiologist.
Similarly, we cannot ignore how patients will respond. With the rise of the patient as a consumer, what control will patients have over the method of care they receive, and by whom? Even if the facts demonstrate higher quality care and better overall financial value for a robotic-operated surgery, does the patient have a choice? Also, will payers be willing to reimburse for these treatments?
Certainly, a cultural shift among patients, providers, institutions and governments must take place in order for robotics to truly become mainstream in the healthcare market. Organizations must think of robotics and AI as a means of supplementing care for the greater good of the patient.
In the near future, it’s likely that robots can be even more capable than humans at doing very human-like tasks. However, to accelerate the path to change, providers considering the adoption of these technologies must think ahead to understand how they will fit in the future healthcare world and how to mitigate some of the potential challenges they will face.
By understanding the technology and data gaps, legal and ethical limitations, costs, and social concerns, providers can properly prepare their organization for the adoption and growth of AI and robotic technologies.
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