Jul 2026· Information Hiding· pp. 1-5· 0 citations· 34 references
Computer Science
TL;DR
This position paper presents a manifesto for a longitudinal, three-fold methodological pivot in health human-AI interaction, and proposes moving beyond static satisfaction metrics towards relational metrics —Longitudinal Trust Calibration, Automation Bias Drift, and Error Recovery Velocity—that track the maturity and resilience of the human-AI partnership.
Abstract
Traditional usability assessments and questionnaires, such as the System Usability Scale (SUS), were designed for deterministic systems with predictable, linear outputs. However, AI-enabled medical devices are inherently probabilistic and co-evolve with the user through repeated interaction, rendering traditional usability assessments insufficient for guaranteeing the long-term safety in the use of high-risk probabilistic systems. Current literature reveals a striking absence of longitudinal studies, creating significant methodological blind spots regarding how trust calibrates over time and whether automation bias intensifies with habitual use. In this position paper, we present a manifesto for a longitudinal, three-fold methodological pivot in health human-AI interaction. We propose moving beyond static satisfaction metrics towards relational metrics —Longitudinal Trust Calibration (LTC), Automation Bias Drift (ABD), and Error Recovery Velocity (ERV)—that track the maturity and resilience of the human-AI partnership. This framework provides an actionable path toward a safety-in-use paradigm that acknowledges the temporal, dynamic nature of high-risk health AI.
Results identify a "symmetry of modality": qualitative interviews correlate with written text explanations, while Think-Aloud protocols better assess cognitively demanding tools like SHAP values.
M. A. D. De Oliveira, Constança Roquette, Nuno Matela et al.· Proceedings of the Internati...· 0 citations
The mandatory transition to electronic health records (EHRs) has precipitated a profound transformation in both military and civilian healthcare infrastructures. While introducing unprecedented capabilities for data storage and retrieval, this transition has concurrently triggered severe secondary impacts, including escalating clinician burnout, workflow disruption, cognitive overload, and interoperability friction. This paper rigorously evaluates the Armed Forces Health Longitudinal Technology Application Theater (AHLTA-T) through the dual theoretical lenses of the Constructive E-Health Evaluation Method (CeHEM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). By examining the perceptions and reported data from twenty-five stakeholders—providers, nurses, and medical technicians—stationed at the United States Air Force School of Aerospace Medicine (USAFSAM), this quantitative case study meticulously unpacks the critical intersections of EHR experience, working environment, systemic impact, subjective evaluation, and technical functionality. Particular analytical attention is devoted to the statistically significant differences in workload interference experienced across disparate clinician roles. The study reveals that while the AHLTA-T system is generally perceived as functional, providers and nurses report significantly higher levels of workload interference compared to medical technicians, underscoring critical disparities in EHR usability across distinct clinical roles. Ultimately, this essay explores the broader, systemic implications for EHR adoption, the necessity of continuous process improvement, and the imperative for future research specifically tailored to the unique demands of en route military patient care.
Brian Groll, Tina Evans· The AIUS Journal of Research...· 0 citations
GenAI-assisted processes can provide rapid, actionable design mitigations that reduce error likelihood and enhance patient autonomy, establishing a replicable pipeline for producing heuristic-driven design libraries across diverse medical device contexts.
F. Montalvo, Kathren Pavlov, Phuoc Thai et al.· Proceedings of the Internati...· 0 citations
This mixed-methods evaluation suggests that a deliberately constrained, language-focused AI system can improve the accessibility of medical notes while preserving clinical accuracy and safety while extending into clinical interpretation.
Nicholas Lamb· Frontiers in Digital Health· 0 citations
It is argued that usability engineering should be a formative, safety-critical discipline integrated throughout the product lifecycle, including aftermarket deployment, and not merely a compliance task.
Preetha Moorthy, T. Nagel, Eva Hornecker· i-com· 0 citations