JMIR Publications

ABSTRACT

Background:

Clinical artificial intelligence tools, including ambient AI scribes and AI-assisted preassessment systems, are increasingly being introduced to reduce documentation and consultation time. However, task-level time savings do not show whether usable time is released, how clinicians use it, or whether patients and staff benefit. Current evaluations often measure efficiency without specifying the service goal that the saved time is intended to support.

Objective:

To propose a practical framework for evaluating how time released by clinical artificial intelligence is used, and whether it produces measurable benefits for patients, clinicians, and health services.

Methods:

We developed an evidence-informed viewpoint by synthesising recent studies of ambient AI scribes, AI-assisted clinical preassessment, documentation burden, clinical time scarcity, and digital health implementation. We distinguish task-level efficiency, net time released across the care team, subsequent use of that time, and patient or staff outcomes. We also propose an implementation allocation plan and an evaluation design for comparing this approach with usual implementation practice.

Results:

Existing studies show reductions in documentation or consultation time, but they do not consistently establish how much usable time is released, whether work is transferred to other staff, or which patient and service outcomes result. We propose that services specify the intended use of released time before deployment, identify the accountable decision-maker and review point, and measure time release, time use, workload, access, patient understanding, and safety according to the service goal.

Conclusions:

Clinical AI should be evaluated not only by whether a task becomes faster, but by whether the service can show who benefits from the change. Making the intended use of released time explicit can support more accountable procurement, implementation, and decisions to continue, modify, or stop deployment.