A survey of clinicians in two departments shows that prediction is mainly important in complex cases, that it is discussed among colleagues, and that use is changing with experience.
IA-assisted decision-making studies often focus on non-specialists, simplified tasks or prototypes tested only once. A team of six researchers wanted to observe the actual practice: an online questionnaire (12 responses) and ten in-depth interviews with clinicians from two different medical cohorts, each using a daily decision support system.
There are several findings. How to interact with the tool changes as experience increases; predictions gain more weight in complicated cases; decisions are often discussed with colleagues rather than taken alone on screen; and how the tool fits into the workflow directly influences its use. The title echoes a feeling expressed by the participants: relying entirely on the machine seems strange to them.
The authors argue for a socio-technical reading of the trust given to these systems, combining human, technical, contextual and organizational factors, and draw recommendations for their design. The sample remains small, making it an exploratory study.
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