There is growing interest in ensuring equity and guarding against bias in the use of risk scores produced by machine learning and artificial intelligence models. Risk scores are used to select patients who will receive outreach and support. Inappropriate use of risk scores, however, can perpetuate disparities. Commonly advocated solutions to improve equity are nontrivial to implement and may not pass legal scrutiny. In this article, we introduce pragmatic tools that support better use of risk scores for more equitable outreach programs. Our model output charts allow modeling and care management teams to see the equity consequences of different threshold choices and to select the optimal risk thresholds to trigger outreach. For best results, as with any health equity tool, we recommend that these charts be used by a diverse team and shared with relevant stakeholders.
Keywords: artificial intelligence; civil rights; health care disparities; health equity; health status disparities; structural inequity.