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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
The Cydoc smart patient intake form accelerates medical n...
Angela Hemesath, Kenyon Wright, Matthew Michael Draelos, Rachel · 2023-06-21 · via cs.HC updates on arXiv.org

Purpose: This study evaluates the effect of Cydoc software tools on medical note time-to-completion and quality. Methods: Medical students were recruited by email to participate in a video encounter with a standardized patient for three scenarios: writing a note from scratch (control), writing a note with the Cydoc educational tool, and writing a note with the Cydoc intake form. Notes were subsequently anonymized and rated by a resident physician across four quality measures. Note time-to-completion was analyzed using a one-way ANOVA with post-hoc Bonferroni correction, while note quality scores were compared using a Wilcoxon paired signed rank test. Results: Eighteen medical students participated in the study. The average note time-to-completion, which included the patient interview and note writing, was 17 +/- 7.0 minutes from scratch, 18 +/- 8.0 minutes with the educational tool, and 5.7 +/- 3.0 minutes with the intake form. Using the Cydoc intake form was significantly faster than writing from scratch (p = 0.0001) or using the educational tool (p = 8 x 10-5). Notes written with Cydoc tools had higher note comprehensiveness (3.24 > 3.06), pertinent positives (3.47 > 2.94), and pertinent negatives (3.47 > 2.67), although this trend did not reach statistical significance. Conclusions: Using the Cydoc smart patient intake form accelerated note writing by 2.98x while maintaining note quality. The Cydoc smart patient intake form has the potential to streamline clinical documentation and save clinicians' time. Future work is needed to evaluate Cydoc tools in an in-person outpatient setting with practicing clinician users.