  While artificial intelligence (AI) and other automation technologies might
lead to enormous progress in healthcare, they may also have undesired
consequences for people working in the field. In this interdisciplinary study,
we capture empirical evidence of not only what healthcare work could be
automated, but also what should be automated. We quantitatively investigate
these research questions by utilizing probabilistic machine learning models
trained on thousands of ratings, provided by both healthcare practitioners and
automation experts. Based on our findings, we present an analytical tool
(Automatability-Desirability Matrix) to support policymakers and organizational
leaders in developing practical strategies on how to harness the positive power
of automation technologies, while accompanying change and empowering
stakeholders in a participatory fashion.
