The importance of clinical context in evaluating algorithmic fairness: insights from a medication adherence prediction algorithm.

Among 34 697 patients (13% Black, 10% Hispanic, 65% White), algorithm accuracy was similar across nSES and racial subgroups.

The algorithm also assigned fewer false negatives (0.63 [0.59-0.67]) to Black vs White subgroups.

When applied to 3 hypothetical use cases, worsening of existing disparities was pertinent for clinical applications where false positives could be particularly harmful (e.g, if predictions of nonadherence prompted lower treatment priority).