Healthcare AI use evaluation: eligible work, review burden and acceptance evidence

To assess whether AI fits healthcare work, define who may use it and for which eligible tasks. Logins, clicks, generated drafts and completed tasks describe different events. Daily use alone does not establish clinical suitability or quality.

This guide helps institutional teams plan usage and workload records; it supplies no Taiwan adoption survey or use-case ranking. Clinical safety, quality, intended use and applicable requirements need separate assessment by appropriate institutional owners.

Separate five recording units

An institution is an organization, a system is a tool, a use case is a purpose, a user is a person, and the observation period is time. Buying a tool does not establish use across departments. A system may support several purposes or only some authorized users.

Identify each task with system version, approved roles, eligible opportunities and period. Preserve changes rather than mixing people, purposes or versions into one adoption rate. Newly piloted work and incomplete follow-up remain insufficiently observed.

Ask about six contexts before measuring use

These are review questions, not a recommended order, deployed hospital examples or a clinical product offer.

TFDA's scope guidance considers actual use, functions and risk. Documentation, draft or assistance labels do not settle device classification. The computer-aided detection/diagnosis update concerns use-specific evidence. This table is not clearance or clinical acceptance.

Measure completed work, not only generation speed

For a hypothetical internal SOP lookup tool, eligible opportunities are approved-source queries by designated staff, not the entire hospital headcount. Record applicable sources, task completion, returns to alternatives and reasons.

Workload includes preparation, waiting, inspection, review, correction, escalation and issue handling. Fast text generation may add verification work or support a previously difficult task. Establish comparable task and quality evidence before interpreting time changes.

Record material omissions, false acceptance and data/system exceptions. Citations and review records aid inspection, not prove correctness. Clinical outcomes require separately defined use-specific references and suitable reviewers; usage counts cannot substitute.

Investigate reasons without ranking barriers first

Feedback can identify workflow, interface, source, training, capacity or responsibility issues. Model quality, integration and data also need investigation. Several mechanisms can coexist; interview frequency is not causal importance or evidence that models are already adequate.

Record recruitment, responding roles, questions and period for user feedback. Separate observed behavior, self-report and system records. One interview does not describe a hospital population or Taiwan's market.

The NIST AI RMF supports contextual governance, measurement and monitoring, not hospital adoption thresholds or clinical safety inferred from routine use.

End reviews with a decision

Assign issue owners and next work: add evidence, correct, continue controlled observation, narrow use, obtain appropriate assessment or stop. Data, permissions and interface changes need responsible review. Hospitals and clinics have different tasks and conditions; speed comparisons require comparable evidence.

Read the chart-summary and candidate-code guide for source, version and review integration, or the healthcare scope for this site's administrative and reviewed-preparation boundary.