Author

D. Austria

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Review Open access Sep 2026

Cross-System Legibility of a Practitioner-Derived Workflow-Error Taxonomy for Conversational AI: A Three-Comparator Agreement Study

Practitioner-derived taxonomies of conversational artificial intelligence (AI) workflow errors show low inter-rater agreement among human coders, leaving open whether the instrument is ill-specified or the judgments are inherently difficult. We delivered a locked eight-category workflow-error taxonomy verbatim, under s...

D. Austria, B. McCollister, J. Lindsey et al. · medRxiv · 0 citations

Review Open access Aug 2026

From Output Errors to Workflow Harm: A Practitioner-Audit Method for LLM-Mediated Research

TRACE (Tracking Reliability of AI-generated Conversational Evidence), a practitioner-audit framework for evaluating the downstream workflow reliability of conversational AI, is presented, suggesting taxonomy legibility under standardized conditions even where human judgment diverged.

D. Austria, B. McCollister, J. Lindsey et al. · medRxiv · 0 citations

Open access May 2026

A Clinician-Centered Evaluation Framework for Large Language Models in Patient Education: Integrating the Technology Acceptance Model and Medical Condition Regard Scale

An evaluation framework that pairs the Technology Acceptance Model (TAM) with the Medical Condition Regard Scale (MCRS) and is called the TAM-MCRS LLM Evaluation Framework, a novel clinician-centered approach for comparing which LLMs produce the highest-quality post-care patient education across accuracy, appropriatene...

D. Austria, Grace Williams, Christopher Girardo et al. · JMIR Formative Research · 0 citations