Remote care: Distance may not be the biggest challenge > Overcoming the challenges of AI to improve health inequity

Overcoming the challenges of AI to improve health inequity

The conversation

In a recent article, For fair and equal healthcare, we need fair and bias-free AI, Henk van Houten, Executive Vice President, Chief Technology Officer, Royal Philips, wrote that Artificial intelligence (AI) has the potential to make healthcare more accessible, affordable, and effective. He pointed out that it can also inadvertently lead to erroneous conclusions and thereby amplify existing inequalities. Mitigating these risks requires awareness of the bias that can creep into AI algorithms.  However, by being aware of any bias in AI algorithms, and through careful design and implementation, AI has the promise to create a healthier and fairer future for all. 
  • Researchers at the University of California revealed that an algorithm used to flag high-risk patients for enhanced medical attention displayed significant racial bias.
  • Bias can occur at any phase of AI development and deployment, from using biased datasets to deploying algorithms in a different context than the one it was trained for.

Our perspective

Why this matters for sleep and respiratory patients

 

The role of AI in identifying at-risk populations in the community could potentially open up new treatment and care pathways for people suffering from sleep and respiratory conditions. However, in the quest to bring equality to health systems, a growing issue is whether AI can be effective in identifying the needs of underserved communities, where the prevalence of respiratory conditions is higher. Already, we have witnessed how the algorithms that AI technologies rely on can be skewed against certain populations.

The importance of building trust

In a widely reported study published in Science last year,1 a group of researchers at the University of California revealed that an algorithm used by US hospitals to flag high-risk patients for enhanced medical attention exhibited significant racial bias. Black patients were less likely to be flagged for additional care than white patients, even when they were equally sick. Through removing disparities in health data that often result in a one-size-fits-all approach, we support bridging some of the divides in healthcare outcomes.  


Sleep and respiratory patients could embrace the role of AI technology in their healthcare — and the platforms and systems they use — if they trust those methods will help treat their sleep and respiratory conditions in the long run. If we get this right, we can reach patients in all corners of our communities and change millions of lives. 

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References:

 

1. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447–45. https://doi.org/10.1126/science.aax2342

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