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Artificial Intelligence and Machine Learning in Anesthesia: Are We There Yet?
Within five years, more than half of hospitals will have begun using artificial intelligence, including machine learning, to support clinical care and business functions, a survey has found. We offer current information on the emerging technology’s expansion and acceptance in medicine and healthcare, along with insights on potential applications in anesthesia.
August 13, 2018
Artificial intelligence (AI) and its close relative, machine learning (ML), are, as predicted, burgeoning in healthcare (see our June 12, 2017 eAlert). Though some barriers remain, the emerging technology’s future as a support tool in anesthesiology also looks bright.
AI’s rise across all sectors is even outpacing the speed of Moore’s Law, the prediction by Intel co-founder Gordon Moore in 1965 that processing power would double every two years, according to Intel’s Bryce Olson. The technology’s exponential expansion will lead to the development of faster, better and cheaper solutions, he said in Healthcare IT News.
The investment numbers bode well for AI. Consulting firm IDC projects that worldwide spending on AI will grow to $46 billion by 2020 from $12.5 billion in 2017. “Cognitive/AI systems are quickly becoming a key part of IT infrastructure and all enterprises need to understand and plan for the adoption and use of these technologies,” research director David Schubmehl said.
Calling AI “healthcare’s new nervous system,” consultant Accenture predicts spending on AI in healthcare will surpass $6.6 billion by 2021—an annual growth rate of 40 percent. The company estimates that “key clinical health AI applications can potentially create $150 billion in annual savings for the healthcare economy by 2026.”
Healthcare organizations seeking competitive advantage already understand that AI is on the brink of becoming a critical asset that will impact all corners of the enterprise, far beyond the sphere of health information technology alone. But, as with any emerging development, healthcare organizations must surmount a few hurdles before AI can really take hold within the sector. Among the top obstacles to implementation, the HIMSS survey found, are the perception that the technology is still developing (23 percent), the need for stronger evidence for a business case (15 percent), infrastructure constraints (12 percent) and current data integration within the organization (12 percent).
Healthcare organizations appear motivated to overcome these limitations. Although just under five percent of hospitals currently have AI capabilities, within five years, more than half will have begun leveraging the technology, reports the Health IT News and HIMSS Analytics (Health Information Management Systems Society) Market Indicator: Artificial Intelligence. According to the survey of 85 executives, 24 percent indicate they will have AI solutions within two years and another 25 percent report they will use AI within three to five years.
AI took center stage at HIMSS 2018, HIT’s largest meeting, where announcements and presentations of partnerships between healthcare providers and technology developers drew notice for innovations using AI, ML and related technologies in a seemingly limitless array of applications, including everything from improving congestive heart failure care to predicting and sending early alerts when patients might be deteriorating.
Not that AI is automatically poised to become all things to all providers. “It takes a while to understand how to align the pros and cons of a new technology with actual business needs,” according to ML expert Leonard D’Avolio, PhD, of Harvard Medical School and Brigham and Women’s Hospital. In his keynote speech at the HIMSS conference's AI event, Dr. D'Avolio encouraged healthcare organizations and HIT professionals not to allow the current hyperbole surrounding AI to block meaningful discussions about what the technologies can and cannot do and how they should be applied. He advised organizations to have a clear problem and execution plan in place before pursuing AI as a solution. “Good design and application of technology involves matching what is technically possible with what organizations really need to do,” he advises.
Implications for Anesthesia
What about AI in anesthesia? In an article titled “Anesthesiology, Automation and Artificial Intelligence” in the January 2018 issue of Baylor University Medical Center Proceedings, anesthesiologists John C. Alexander, MD, MBA, and Girish P. Josh, MBBS, MD, of the University of Texas Southwestern Medical Center, Dallas, point to the promise and sophistication of ML in overcoming some of the obstacles that have stymied many attempts thus far to automate anesthesia.
They observe that the specialty’s unique qualities perhaps mean some of those roadblocks will never completely be surmounted, and that’s okay. “Anesthesiologists enjoy a good mix of cognitive and dexterity-based labor, and given that AI will primarily result in the automation of cognitive work, it may be that our hands prevent full automation of the specialty,” they write.
Current robotic devices do not match the dexterity required for such routine activities as tracheal intubation, venous cannulation or neural blockade, so perhaps AI will best serve the specialty in the cognitive domain; that is, in the development of more advanced clinical decision support tools based on ML. By using ML to generate new knowledge based on analyses of pooled patient data from electronic medical records, these tools “could give individual providers the cognitive ‘boost’ needed to deal with the massive quantity of available medical data, yet also provide better clinical pathways to more effectively improve patient outcomes,” they write.
One solution moving in that direction is Touch iQ, a fully integrated perioperative platform born of a new strategic partnership between Plexus Technology Group (PTG) and London-based Synopsis Healthcare, a supplier of digital preoperative assessment for the National Health Service and private hospitals. The solution integrates PTG’s Anesthesia Touch™ anesthesia information management system (AIMS) and Pharmacy Touch™ automated medication management system with Synopsis iQ, a digital preoperative assessment tool that utilizes AI to recommend therapies and risk profiles for patients based on regional, therapeutic and established clinical pathways.
Drs. Alexander and Joshi see advances in AI and ML in anesthesia as offering an opportunity for the profession “to reinvent itself from an intraoperative specialty to one of true perioperative medicine” through the ASA’s perioperative surgical home and other efforts. By taking on some of the cognitive workload for anesthesiologists, they say, AI systems might support “a renewed emphasis on the doctor-patient relationship as we can once again address the wants, needs, hopes and fears of our patients without compromising productivity and quality.”
With best wishes,
President and CEO