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Healthcare organizations can harness machine learning to schedule vaccines, streamline patient communications and even prioritize access – but the technology is hardly infallible.
The question of who should get access to COVID-19 vaccines first has varied from state to state, with some governments prioritizing those with high-risk conditions and others lowering the age of eligibility.
One South Dakota-based system, Sanford Health, is using a machine learning model to identify which individuals are at greatest risk of having severe COVID-19 outcomes – and applying the algorithm to eligible groups.
“With [those] 85,000 people what we can do is take a real-time picture that evolves over time, using computer learning to tell us what patients or what people in the Midwest get the sickest from COVID-19,” said Sanford chief physician Dr. Jeremy Cauwels to Minnesota Public Radio.
Cauwels told MPR that he believes an artificial intelligence approach is more equitable than random choice for administering the vaccine.
Sanford isn’t alone. Experts say AI has big potential to assist with the COVID-19 vaccine rollout.
“The pace and scale of the vaccine rollout is unprecedented, and we are seeing AI play a role,” said Lori Jones, chief revenue officer and president for the provider market at Olive, an AI-as-a-service vendor.
Rather than using AI to identify at-risk patients, Jones noted its potential to promote efficiency within existing workflows.
“The biggest areas of focus for organizations that we’re working with have all related to managing the organization, scheduling, preregistration and communications activity around the testing and vaccines themselves, with additional automation activity to streamline patient communications and drive better vaccine efficacy by ensuring patients are aware, prepared and present to receive second doses,” Jones explained.
“We’ve got an important mission ahead of us still, and if we can’t expand the capacity of organizations delivering the vaccines to take on more patients faster, then there is a very real risk that this process could take years, not months,” she said.
Jones pointed to chatbots as a prime example of the way AI can be used in conjunction with other tools, specifically when it comes to patient engagement.
“AI-enabled digital call centers are helping organizations manage the significant level of interest in key vaccine information,” she said. “FAQs can be converted into chatbots to refresh the available information to be COVID-19-specific.”
“If the healthcare industry continues to rely on paper forms, phone calls, mobile apps, portals and email campaigns, process bottlenecks will create long lines, confusion and frustration,” agreed Greg Johnsen, CEO of LifeLink, which powers conversational solutions for healthcare organizations.
“Additional complexities around new documentation, specific follow-up vaccination windows and an influx of people that are new patients could overwhelm current intake and scheduling processes,” said Johnsen. “Building a handful of digital assistants versus training thousands of individuals is also a key consideration when it comes to efficiency and cost.”
That said, there are unmistakable downsides to relying on AI – namely, expecting the technology to be infallible.
“Pursuing AI strategies can certainly bring about adoption challenges, and adoption is critical to any AI strategy,” said Jones. “One roadblock to AI adoption is understanding that AI tools aren’t replacing human healthcare workers: They’re actually empowering them and helping them work better, faster.”
There are also the ever-present dangers of reproducing bias or using faulty algorithms. In December of this past year, Stanford Medical Center came under fire for prioritizing administrators over frontline health workers due to an error in the rule-based formula it was using to help calculate who would get vaccinated first.
Sanford, in South Dakota, is not using race or ethnicity as a factor in its algorithms, theorizing that individuals with higher rates of chronic disease will be elevated in the prioritization.
But given the disproportionate effect of COVID-19 on patients of color – especially Black, Latinx and Native people – other health systems in nearby states say it’s important to take those demographics into consideration.
The University of Wisconsin-Madison did use a race-based algorithm to prioritize employee vaccines in its initial distribution, Shiva Bidar-Sielaff, chief diversity officer at UW Health, told MPR.
“It’s incredibly important to realize that all data points to the fact that, unfortunately, race and ethnicity have been shown to create a much higher risk of hospitalization and death for COVID-19,” said Bidar-Sielaff.
“So when we looked at our algorithm, we saw that if you add age and SVI, which has that component of race and ethnicity, it’s a multiplier effect in how much higher risk an individual is at for hospitalization and death,” she said.
Some companies are stressing the need for caution when it comes to using AI for vaccine allocation.
Representatives from Salesforce, which launched its Vaccine Cloud tool this past month to assist clients with managing vaccine administration, said they were working to ensure equitable distribution.
“Vaccine Cloud can deliver integrated and customized solutions for our customers, including the ability to use data and insights to support [the] distribution, management and administration of vaccines,” a Salesforce spokesperson told Healthcare IT News via email.
“However, our Principles for the Ethical Use of COVID-19 Vaccine Technology Solutions explicitly state that AI should not be used to predict personal characteristics or beliefs that would affect a person’s or group’s prioritization for access to vaccines, and we work closely with our partners and teams on this guidance.”
Still, it’s clear that AI – when deployed responsibly – may be able to make the COVID-19 vaccine rollout faster and more effective for at-risk patients.
“The vaccine rollout is the ultimate test for AI to showcase the breadth of time-saving and efficacy capabilities, and demonstrate its full value for healthcare leaders,” said Jones. “When organizations emerge from the COVID crisis, we see AI becoming an integral part of their digital strategy.”
Originally published by
Kat Jercich | February 11, 2021
Healthcare IT News
