AUTHOR=Ulloa Mara , Rothrock Blaine , Ahmad Faraz S. , Jacobs Maia TITLE=Invisible clinical labor driving the successful integration of AI in healthcare JOURNAL=Frontiers in Computer Science VOLUME=4 YEAR=2022 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2022.1045704 DOI=10.3389/fcomp.2022.1045704 ISSN=2624-9898 ABSTRACT=

Artificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges of developing AI, rather than the system integration. As a result, clinical teams' role in developing and deploying these tools has been overlooked. We look to three case studies from our research to describe the often invisible work that clinical teams do in driving the successful integration of clinical AI tools. Namely, clinical teams support data labeling, identifying algorithmic errors and accounting for workflow exceptions, translating algorithmic output to clinical next steps in care, and developing team awareness of how the tool is used once deployed. We call for detailed and extensive documentation strategies (of clinical labor, workflows, and team structures) to ensure this labor is valued and to promote sharing of sociotechnical implementation strategies.