In the meantime, several AI tech startups have developed, either using platforms developed by larger technology companies or using their own platforms to address healthcare issues.
Anil Jain, MD, vice president and chief health informatics officer for IBM Watson Health, says that the current capabilities of cognitive computing stem from having a history dealing with healthcare providers and systems.
“We see a lot of people doing artificial intelligence in the market. We like to call our operations augmented intelligence. We are not trying to replace traditional insights. We have a proven place in the market as offering cognitive solutions in the marketplace. The longer this technology is in place, the smarter it becomes,” Jain says, adding that the combination of using data analytics, traditional and cognitive insights in a population health setting allows for the best results. “It’s not just technology, our expertise is surrounded with data technicians, scientists. It’s an ecosystem, not just pure technology.”
Cognitive computing capabilities
Grewal says that health organizations looking to make an investment in cognitive computing and AI should think about patients’ needs first before getting caught up in all the diverse capabilities.
“Operationalizing analytics, which is what cognitive computing and AI do, can be used across the spectrum of care. But the simplest step to take first is in patient engagement,” Grewal says. “Cognitive computing provides insights that help guide a case manager to decide how to interact with a patient.” Insights include best practices for care plans, optimized length of hospital stay, personalized care plans and identification of gaps in care planning.
Using AI to manage non-face-to-face interactions, including pain assessments and appointment and medication reminders, can help offload these activities from providers, allowing them to interact and engage more with patients. For example, AI might be used to identify and auto-enroll patients with chronic diseases into care plans to reduce readmission, and engage those same patients at a higher rate. By identifying those patients, the AI technology also automatically schedules transportation for patients at high risk of no shows, and can send medication reminders.
“Relying on AI to engage with a patient having a heart attack is reckless today, but probably not in the future. There are too many variables that cannot yet be coded into a computing model,” Grewal says. “But standardized, more mundane interactions that do not require a face-to-face engagement are prime candidates for the automation AI can provide.”
Jain agrees, adding that the decision-making capabilities of AI technology assist various stakeholders with data and cognitive intelligence.
“Our engagement manager helps providers … understand what processes are and are not working. It helps them determine things like possibly using a text message to communicate with patients instead of email,” Jain says. “We can take those who are in the most need and reach out to them. By applying cognitive thinking, advanced machine learning and cognitive insights, you can only reach out to the folks that you can make the biggest impact with. We can scale that type of expertise to all corners of healthcare.”