Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes - Robotics Institute Carnegie Mellon University

Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes

Qian Yang, Aaron Steinfeld, and John Zimmerman
Conference Paper, Proceedings of CHI Conference on Human Factors in Computing Systems (CHI '19), May, 2019

Abstract

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of Unremarkable Computing, that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience.

BibTeX

@conference{Yang-2019-121251,
author = {Qian Yang and Aaron Steinfeld and John Zimmerman},
title = {Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes},
booktitle = {Proceedings of CHI Conference on Human Factors in Computing Systems (CHI '19)},
year = {2019},
month = {May},
}