This document discusses the need for algorithmvigilance, which is the systematic monitoring of algorithms used in healthcare to identify unintended impacts and ensure equitable outcomes. As the use of AI and machine learning grows in healthcare decision-making, there is evidence of data and algorithmic biases that can differentially impact populations. The author argues that just as pharmacovigilance monitors drug effects, algorithmvigilance is increasingly important to evaluate algorithms, understand adverse effects, and prevent inequities. An algorithm-driven healthcare learning cycle is proposed that continuously monitors effects, identifies needed changes, and improves algorithms based on real-world use.