Can AI empower health equity?

Whole person care takes a broad view of people, integrating factors beyond traditional healthcare, often dubbed as the social determinants of health into health care planning. This gives us the great ability of having a more complete, accurate picture of community need as well the impact of the services provided, especially in the public sector.

Questions of health equity (differences in access and quality of care between populations) can be enhanced and answered more effectively with this whole person approach. At the same time, we must be ethical with the data used to inform both need and impact. Biased data can lead to biased artificial intelligence (AI). That defeats the fundamental notion of data for good and improving our communities and world!

In my new Health IT Outcomes article, I discuss three ways that we can use AI to empower health equity:

1. Algorithm transparency
2. Data transparency
3. Natural language processing

A theme of all of this is empowering dialogue about interpretation and impact. Multiple eyes, perspectives, and voices can help keep us honest (as long as we engage that process honestly). I also personally get really excited about the third way (NLP), as it's an opportunity to raise up qualitative, contextual data to enrich the quantitative data we're used to relying on.

I’d love to hear your thoughts and feedback. Can AI empower health equity, or are these mutually exclusive? What will help us be sure to use data for good?