A study re-implements 12 AI algorithms for electronic health records in a shared framework, evaluating their performance on two clinical datasets. The results suggest that aggregate pairwise comparisons transfer across evaluation settings, and that clinically meaningful tasks exhibit task-method interaction, which can explain modeling choices. This study may inform the development of more effective AI algorithms for healthcare.