Panelists at the AHIP Institute & Expo Online discussed whether algorithms will play a greater role in the future in reallocating health care resources.
One of the positive things to come out of the catastrophic COVID-19 pandemic may be an acceleration in the belief of mathematical models and algorithms to help health care systems deliver more precise care and also to plan for the next pandemic, according to a panel earlier this week at the AHIP Institute & Expo Online.
“I think developing algorithms that can predict population risk and project utilization would be very important so that you can take care of patients’ critical care needs,” said Mohammed Saeed, MD, PhD, chief medical officer at the artificial intelligence firm HEALTH[at]SCALE, which presented the panel. In addition, he said, chronic health conditions also need attention so that conditions do not worsen over time.
The session was moderated by Zahoor Elahi, chief operating officer at the firm.
Disease doesn’t stop just because you are sheltering in place at home, noted Garry Choy, MD, MBA, deputy chief medical officer, Clinical Systems, Office of Chief Medical Officer and Medical Affairs, UnitedHealth Group.
But since “one of the most vital resources within the healthcare system are the people,” he said, he is interested in knowing which technologies can most efficiently reallocate resources to where they are most needed.
“I think COVID exposed the need for technologies and capabilities across your enterprises, whether you're a healthcare company, payer, startup—how do you take care of patients?” he said. These things could help “redistribute the right care at the right time to the right part of the healthcare stack,” he said.
Moreover, health care won’t go back to old ways of doing business or taking care of people when the pandemic ends, predicted John Guttag, PhD, chief technology officer for the firm and distinguished professor, Electrical Engineering and Computer Science, Massachusetts Institute of Technology.
What he hopes, as someone who has spent his career in machine learning math, is that previously health care was slow to move to more advanced technological ways of operating. With the pandemic, he said, “all of a sudden people care about models.”
"So I think we're going to see a lot more willingness for people to use mathematically sound models as decision-support tools,” Guttag said. The key is knowing what assumptions are built into the model and not use them blindly," he said.