Surgo Health Study Offers New AI-Driven Insights into COVID-19 Vaccine Decision-Making

 Surgo Health, a new healthcare technology company dedicated to personalized healthcare,announced that original data and analysis from its socio-behavioral analytics platform appeared in Scientific Reports, a peer-reviewed journal published by Nature.

The study, “Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network,” used the form of artificial intelligence (AI) called Bayesian network to map out the complex, intertwined drivers of COVID-19 vaccine hesitancy across the United States during the pandemic.

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“Efforts to change healthcare-related behavior, such as vaccine adherence, have had limited success because the obstacles are often multifaceted. AI-based mapping provides a system-level understanding of barriers and behavioral drivers to help practitioners, leaders, and governments design more effective, holistic intervention strategies,” said Vincent Huang, Director of Data Science and AI at Surgo Health and co-author of the study.

Surgo Health’s innovative research approach revealed that when it comes to vaccine hesitancy and refusal, individuals’ political affiliation did not have as big of a causal impact on decision-making as previously suggested by correlative analyses.

Contribution to social responsibility or protecting others in their community was identified as the most significant causal factor in terms of motivating people to get a COVID-19 vaccine. Addressing vaccine safety concerns and showing people they may regret not taking the vaccine play a crucial role in promoting adherence as well.

“Our research confirmed that the causal pathways for vaccine intention are complex and interconnected, but contrary to popular belief, blaming conservative political ideology  alone for the low vaccine uptake is an oversimplification. While political affiliations have some remote impact, they are weak compared to the more direct, psychobehavioral factors such as trust in the vaccine development process and anticipated regret for not taking the vaccine,” said Henry Fung, Data Scientist at Surgo Health, co-author of the study.

“These findings not only uncover important ways to optimize public health and population health strategies but validate an emerging approach that combines advanced technology with novel psychobehavioral data to understand healthcare decision-making on a deeper level than previously possible,” said Sema Sgaier, CEO & Co-Founder of Surgo Health. “We believe there is tremendous potential for leveraging this methodology in other areas of healthcare. ”

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Experimental or randomized controlled trials (RCT) remain the gold standard for the rigorous evaluation of comparative variables and determination of causal relationships in health-related data sets. Still, challenges exist, such as ethics and resource limitations. With the rise in AI and machine learning, there is an increasing opportunity to complement RCT with technology to better inform intervention design.

Surgo Health’s analysis leveraged psycho-behavioral data collected from a Surgo Health survey conducted in early 2021 involving a representative sample of more than 2,700 US residents. The company’s proprietary analytics and comprehensive suite of AI tools enable powerful insights and identification not only of correlations in data but of causal relationships and has also been leveraged in other public health initiatives such as efforts to understand factors in maternal decision-making about hospital vs. home delivery of babies in rural India.

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