Machine learning-driven efficiency is increasingly cast as a way to insure the future sustainability of understaffed healthcare systems. This article offers a glimpse into the potential practical repercussion of efficiency achieved through machine learning by examining how nurses’ practices of care provision are reappraised under machine learning logics. Building on a 4-month ethnographic fieldwork in the context of an innovation project in Dutch intensive care units (ICUs), this article centers, simultaneously, ICU nurses’ daily work, and the problem definitions and assumptions guiding the development of a machine learning dashboard for ICU ward-capacity management. It argues that nurses’ practices are centrally guided by attunement: embodied, indeterminate, affectively laden knowledge-making and action. Conversely, the dashboard torques these practices into a matter of attention: the passive, cognitive filtering of stimuli, disconnected from action. While paving the way for efficiency narratives, attention disregards nurses’ invisible and data work, and it might translate into labor intensification. This article thus proposes that moving from attention to attunement might both do justice to the complexity of nurses’ practices and provide more empirically solid policy foundations in the wake of healthcare workforce shortages.