Artificial intelligence (AI) in healthcare is no longer a futuristic idea. Organizations are benefiting—on administrative and clinical levels—from practical adoptions that make cost savings and better patient care a reality. But industry experts say there’s still a lot of untapped potential to look forward to, which will only be realized when trust in AI is more firmly established and when healthcare organizations become more skilled at integrating it into their daily work flows.
“In other industries where the stakes are lower, the trust is higher,” says Dan Housman, chief technology officer of ConvergeHEALTH by Deloitte and managing director of Deloitte Consulting LLP. “AI deals with ambiguity, but in medicine the rules are clear cut. There needs to be work on both sides (with both technology companies and healthcare organizations) to make sure AI tools are easier to understand and the healthcare community is more comfortable with a fully untraceable answer.” For example, while consumers embrace AI voice-enabled technology devices like Siri and Alexa, Housman says they, and providers, may not be as accepting of an AI-assisted speaker at the patient’s bedside because the consequences of error are greater.
Here are some of the most noteworthy ways AI is being used in healthcare, and how organizations could use it in the future.
Many organizations are partnering with tech companies to tackle clinical issues with AI. In March 2018, the Mayo Clinic and IBM Watson announced that using IBM’s clinical trial matching AI platform, Mayo Clinic increased enrollment for breast cancer clinical trials by 80%, and reduced screening time for patients looking for clinical trial matches. This is an important development, as only 5% of cancer patients participate in clinical trials, and low attendance leads to many trials being incomplete or cancelled, according to Mayo Clinic.
“Novel solutions are necessary to address this unmet clinical need, advance cancer research and treatments, and, in turn, improve the health outcomes of patients,” says Tufia Haddad, MD, a Mayo Clinic oncologist and physician leader for the Watson for Clinical Trial Matching project.
Mayo Clinic is also using AI and machine learning to make stroke diagnoses quicker and more efficient (use of AI to read CT scans decreases the time needed by at least 30 minutes, according to Mayo Clinic). With physicians estimating that 1.9 neurons die each minute during a stroke, the more precise scans can lead to life-saving interventions.
Another example is Microsoft’s partnership with Ochsner Health System in Louisiana to use AI to create predictive models that foresee patient deterioration. Using Microsoft’s cloud service, Azure, the models communicate directly with Ochsner’s rapid response team through its Epic EHR, allowing clinicians to make early interventions. Within a 90-day trial period, the technology was able to reduce codes, or emergency cardiac events, by 44%.
“By working side-by-side with the healthcare industry’s most pioneering players, we are bringing Microsoft’s capabilities in groundbreaking research and product development to help healthcare providers, biotech companies, and organizations around the world use AI and the cloud to innovate,” says John Doyle, director of Worldwide Health for Microsoft Corporation.
On the administrative side, the technology company CrossChx is using its AI-assisted automation tool called Olive in a partnership with Meadows Health in Georgia. Olive is called a “digital employee” and automates order management, eligibility, prior authorizations, and claims processing tasks for the health system. By logging into EHRs and other existing technology, Olive can learn through previous human processes and optimize them to make them more efficient. Working with Olive, Meadows Health created an automation support operations center, and has identified more than 35 processes that can be automated within the organization.
Because of the increase in automation, CrossChx reports that Meadows Health has been able to train staff to focus on more direct patient interaction, especially in their understaffed call center. CrossChx also works with Hancock Regional Hospital in Indiana to optimize the eligibility process, and reports that the hospital has cut the patient billing cycle from 30 days to three days.
Sean Lane, CEO of CrossChx, says that innovative AI solutions can also work “out of the box” for healthcare organizations. “These AI solutions need to get to work right away,” Lane says. “In the beginning of implementing AI, it’s important to address the administrative tasks, though the clinical AI tools have all of the sex appeal.”