Skip to content

COVID-19 vaccinations and artificial intelligence

COVID-19 vaccinations and artificial intelligence

“What is this thing that has happened to us? It’s a virus, yes. In and of itself it holds no moral brief. But it is definitely more than a virus. Some believe it’s God’s way of bringing us to our senses.”

Arundhati Roy, author of Azadi: Freedom, Fascism, Fictions

The COVID-19 pandemic seems to be better under control these wintry days, but the execution of the vaccinations seems to be almost as unpredictable as the pandemic itself. The algorithms used to help facilitate the distribution of the vaccines in the United States failed to a large degree, with media showing horrific pictures of the elderly in wheelchairs waiting over 10 hours in the arctic cold for their turn. The lessons learned from this real life deployment of simple algorithms are worth noting for the future….

The health agencies at the local, state, and federal levels have all developed their own allocation algorithms so that there was no consistency or coherence in the allocation schemes. In addition, the Tiberius algorithm used for nationwide vaccine distribution seemed to lack transparency and has been criticized.

Lesson: Algorithms need to generalize somewhat independent of venue and to be transparent.

The myriad of formulas used by hospitals and health organizations to allocate vaccines in some ways exacerbated the inequities that already existed in morbidity and mortality in African- Americans and Latinos. These vulnerable populations have a fraction of the vaccination rates as whites and this is complicated by the at-risk populations being undercounted.

Lesson: Algorithms need to in place to neutralize inequities as much as possible.

At Stanford, an early vaccine distribution algorithm was severely criticized for the very low number of frontline residents that were assigned slots for vaccinations (while senior administrators with little front line exposure managed to get to the front of the line). This prioritization strategy should have been observed directly and proactively by human administrators to detect any potential bias.

Lesson: Algorithms need to have human oversight with their common sense to detect any bias.

One state implemented an interesting incentive to gather the elderly for vaccinations: the accompanying person can concomitantly get a vaccination. Perhaps an algorithm would not be able to come up with that creative solution as it involves a solution with the complexity of human group behavior.

Lesson: Algorithms cannot yet be a source of creative solutions to existing complex problems.

It appears that we should take these lessons learned during the vaccination process and remember these valuable lessons for future deployment of algorithms in other clinical scenarios.

Dr. Anthony Chang’s book is available on Amazon now! 

Intelligence-Based Cardiology and Cardiac Surgery Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (Intelligence-Based Medicine: Subspecialty Series) Data Science, Artificial

Subscribe to our mailing list and receive a FREE e-book!

Written by Dr. Anthony Chang, the Medical Intelligence Compendium and Glossary provides a comprehensive oversight into the terms and concepts that are crucial to the growing field of Artificial Intelligence in healthcare. Subscribe for a free copy today!

E-Book Request
Show Buttons
Hide Buttons