There is increased recognition for better methods to measure, predict and adjust for social risk factors in healthcare and population health. Current performance and quality measures generally do not take SRFs into account in the bonus/penalty structure, nor are SRFs generally included in most risk adjustment formulas.
This can lead to unintended consequences, including the potential to perpetuate bias and disparities in health outcomes. To mitigate these issues, RTI International is developing an “artificially intelligent” approach to risk adjustment for SRFs using random forests to understand life expectancy variances at the census tract level.
So how can AI help recognize how local area factors are independently associated with many health outcomes and may be informative either in conjunction with individual-level data or on their own?