Health-care institutions have been anticipating the impact that artificial intelligence (AI) will have on the performance and efficiency of their operations and their workforces—and the quality of patient care. But many have already been reaping the benefits of AI tools. And contrary to common, yet unproven, fears that machines will replace human workers, AI technologies in health care may actually be “re-humanizing” health care, just as the system itself shifts to value-based care models that may favor the outcome patients receive instead of the number of patients seen.
A survey of more than 900 health-care professionals by MIT Technology Review Insights, in association with GE Healthcare, finds that health-care professionals are already using AI to improve data analysis, enable better diagnoses and treatment predictions, and free medical staff from administrative burdens. These findings are even more critical as health-care delivery and administration are becoming more complex and costly, and professional and technological capacity is ever more burdened, with doctors buried amid vastly expanding workloads and administrative, lower-yield work, and patients robbed of personal interactions with their physicians.
For one, machines must work for doctors and clinicians, not the other way around; much patient consultation time is spent entering data, not drawing inference from it. This, however, is largely an evolutionary transition in the adoption of AI. More important, health-care organizations must allow for fundamental shifts in how patients are cared for—doctors and other health-care workers must leverage increasingly comprehensive pools of AI-mediated medical data to make decisions in collaboration with machines.
“By focusing on areas that patients, providers, or systems are invested in addressing, we have set the stage for more rapid adoption and dissemination of AI.”
Not long ago, no one would have dreamed that a machine could be a partner in guiding a medical procedure. But advancements in AI have positioned this class of technologies as a powerful tool for clinical and operational efficiency. Numerous technologies are in play today to allow health-care professionals to deliver the best care, increasingly customized to patients, and at lower costs. Our survey has found medical professionals are already using AI tools, to improve both patient care and back-end business processes, from increasing the accuracy of oncological diagnosis to increasing the efficiency of managing schedules and workflow. Indeed, 72% of respondents show interest in implementing AI.
The vast majority of survey respondents believe AI represents the extension—not extinction—of professional capability in health care: of those who have implemented AI, or are planning to, more than 80% believe that AI is, or will, help them improve their ability to generate revenue, recruit talent, and be competitive. And during the next 10 years, AI will radically streamline health-care delivery processes.
AI investment in health care has only just begun: most survey respondents with ongoing AI projects say that they will be spending even more to develop applications in 2020.
AI for patient flow optimization
Medical imaging and diagnostics
Automation of electronic health records via natural language processing tools
AI for predictive analytics
Patient data and risk analytics
AI-enabled screening for diseases/conditions
Automated medical condition diagnosis
AI-enabled dosage error reduction system
Predictive health trackers
Virtual nursing assistants
AI for personalized health-care plans
Analytics for mental health
AI adoption in clinical applications is widespread, but survey respondents show the highest level of interest in AI-powered patient flow optimization—making sure patients move through a facility, with the right level of care, as efficiently as possible.
AI’s growing centrality to health-care executive strategy is evident in how much effort institutions are placing in tailoring their solutions to fit their precise requirements—and recruit expertise to maintain and improve their AI resources. For Bijoy Khandheria, a cardiologist at Aurora Hospital’s Aurora International & Executive Health Program, in Milwaukee, Wisconsin, “the basic interpretation [of an MRI], with the help of machine learning and AI, can be standardized and left to the machine. But there is still a need for human intervention.”
Using AI for “smarter” scheduling opens up more slots, which in turn empowers referring physicians to
schedule morepatients or more effectively match patients withspecialists.
AI’s core value proposition is in both improving diagnosing abilities and reducing regulatory and data complexities by automating and streamlining workflow. This allows health-care professionals to harness the wealth of insight the industry is generating, without drowning in it. In our survey, respondents indicate that AI has increased the operational efficiency of health-care institutions: medical professionals report that their workweek is now being rebalanced in favor of care-giving activities.
AI-optimized schedule management not only makes a doctor’s day more efficient—it creates opportunity to use AI for more patient-facing applications, giving clinicians time back to work with their patients more closely, and with more insight. For Dushyant Sahani, professor and chair of radiology at the University of Washington Medical Center, “with improvement in the physician’s workflow and health-care operations, diagnostic tools will be better applied. If the focus is only on the diagnostic tool, adoption will be much slower.”
AI decision-support tools further improve the ability of front-line doctors and other caregivers to provide better treatment. With the help of AI, “what we see is that the technology supports decisions,” says Michael Brady, professor of oncological imaging at the University of Oxford in the UK and founder of several medical imaging companies, “and amplifies the performance of radiologists.”
Medical professionals using AI applications are seeing immediate gains in reducing clinical error—something that’s still a major challenge for those who have not yet adopted such tools.
AI-enabled decision-support algorithms allow medical teams to make more accurate diagnoses. For Matthias Merkel, professor of anesthesiology and perioperative medicine at the School of Medicine at Oregon Health & Science University, it’s doing something big by doing something really small: noticing minute irregularities in patient information. That could be the difference between acting on a life-threatening issue—or missing it.
“When we combine AI-based imaging technologies together with radiologists, what we have found is that the combination of the AI technology and the radiologist outperforms either the AI or the radiologist by themselves.”
Scheduling tools give more autonomy to patients while streamlining smarter scheduling, and increasing resource capacity. And AI-enabled clinical tools help medical professionals manage growing information overload. But, as Merkel points out, there is room for improvement: much of consultation time is spent with computers in between patients and caregivers, with the latter recording information, rather than being health coaches or facilitators.
AI is helping enable doctors to reconnect with their aspirations to go into medicine in the first place. “When I was in medical school,” says Merkel, “one strong benefit was if you had a photographic memory and could memorize large data sets. This is almost obsolete today, and the ability to combine the patient information rapidly with relevant information in the digital world is pivotal.”
AI is being used to assume many of a physician’s more mundane administrative responsibilities, such as taking notes or updating electronic health records, which survey respondents report takes up to 10% of a typical medical professional’s workweek.
The more AI is deployed, the less time doctors spend at their computers. Indeed, 45% of survey respondents believe that AI has helped increase consultation time and time to perform surgery and other procedures.
AI frees up medical staffers’ time so they can do other things—spending time with patients or assessing and planning treatment requirements. Cancer doctors, for example, might examine 200 cases at a time, and the majority of the information they sift through will not be clinically significant. According to Brady, on the time-saving benefit of AI, “clinicians are looking more and more towards AI technologies to help them focus within the time available onto the most salient parts of images.”
During the next 10 years, AI will transform health-care delivery processes into collaborative platforms that will allow patients and professionals to harness, share, and act on immensely powerful insights.
For Aurora Hospital’s Khandheria, “A health-care provider is bombarded with information—it’s so much, so you can’t keep pace with it. And if you can’t keep pace with it, you tend to make mistakes.”
“We need to evolve and better understand how AI can work for us, so we can interpret patients’ health-care conditions, giving them good recommendations and guiding them through a longitudinal process.”
Implementing AI in health-care operations, like any significant organizational transformation, presents multiple challenges. Respondents reported multiple hurdles that they found significantly challenging. Among those was skepticism about the provable benefit and overall cost of AI as top factors hindering its adoption. Hospital administration is generally more skeptical than medical staff. Another hurdle is the disruptive impact that AI has on existing processes; a third is the difficulty of integrating AI applications into existing systems.
“Traditional” technology adoption hurdles—and in particular, the all-important buy-in from top management—are ongoing issues for survey respondents still in the planning stage. Leadership and strong business cases need to be established in order to execute an AI adoption strategy. But this is changing: as AI allows for the synthesis and more precise distribution of the growing wealth of medical data and insight the industry is generating, it will allow for more efficient sharing and collaboration in pursuit of better, less expensive outcomes.
“It’s challenging working to move the field forward in a transformative way with artificial intelligence. There has to be alignment in vision, commitment to exploration, and mutual excitement. Everyone involved needs to be willing to push forward into a sometimes unproven space. If we are afraid to fail on a project and thus, don’t take it on, the opportunity to change the future will pass us by.”
Medical staff with AI cite concerns about the challenges of data privacy half as often as those without AI. Health-care professionals who have not deployed AI are challenged not only by access to accurate data, but also by issues related to maintaining patient privacy and data integrity.
“It’s not about whether it will benefit the consumer, the patient, the health-care system. We want to ensure the health of a population.”
The growth of AI and automated processes often creates concerns that the human touch will be removed from the health-care delivery process. What the industry is finding, however, is the opposite is becoming true: AI can extend the resources and capabilities of overworked health-care professionals and vastly improve processes. Clinicians and administrators alike are providing care in a network of AI-enabled tools and insight that lets them engage colleagues, automated resources, and patients to make more accurate, more proactive care decisions. “In AI, we are interested in partnerships in which the improvements that are made can both scale locally at our institution, but also impact health care worldwide,” says Rachael Callcut, associate professor of surgery at the University of California, San Francisco (UCSF) Medical Center and director of data science at the Center for Digital Health Innovation. “We also need to do this work with a solid scientific foundation with all the important protections for our patients and providers.”
Faster access to better patient data is the most critical benefit AI-enabled health-care teams are seeing.
agree that AI has improved the speed and accuracy with which patient data is analyzed and shared.
AI can synthesize multiple perspectives from data and insight provided by patients and health-care providers, not only for immediate diagnosis and treatment, but for monitoring wellness on a continual basis.
AI needs to work for health-care professionals as part of a robust, integrated ecosystem. It needs to be more than deploying technology—in fact, the more humanized the application of AI is, the more it will be adopted and improve results and return on investment. After all, in health care, the priority is the patient. As Khandheria confirms, “humans are not going away; they are just going to make smarter decisions, with fewer errors.”