aceA transdisciplinary AI-based framework for usability evaluation in digital health systems
Introduction. As healthcare systems undergo rapid digital transformation, the usability of clinical and patient-facing technologies has become critical to adoption, user satisfaction, and patient safety. Traditional usability evaluation methods are limited in capturing the real-time cognitive, emotional, and behavioural complexities of interaction in high-stakes environments. Method. This study presents a transdisciplinary evaluation of an AI-augmented usability framework for healthcare interfaces. The framework…
An evaluation framework combining eye-tracking, confusion prediction and sentiment analysis beat traditional usability testing on all core metrics for healthcare interfaces among 40 clinicians and patients.
Evidence
- Peer-reviewedInformation Research an international electronic journal2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1615, v1: “A transdisciplinary AI-based framework for usability evaluation in digital health systems.” Truvace, 2026-10-16. /record/TRV-2026-1615 (accessed at citation time). sha256 ed6210323d01248b…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1615 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.