📄 Category

health informatics

193 papers

A Qualitative Study of Patient and Healthcare Provider Perspectives on Mobile Health Assessments for Cervical Spondylotic Myelopathy

This qualitative study of 15 patients and 14 healthcare providers reveals that while current tools for monitoring cervical spondylotic myelopathy are inadequate, stakeholders recognize significant potential for smartphone-based mobile health assessments to improve care, provided they feature intuitive design, seamless electronic medical record integration, and broad accessibility.

Singh, P., Gonuguntla, S., Chen, E. + 18 more2026-03-08📄 health informatics

Population differences in wearable device wear time: Rescuing data to address biases and advance health equity

This study analyzes Fitbit data from over 11,000 participants to reveal how demographic and health factors influence wearable device wear time, demonstrating that standard compliance thresholds disproportionately exclude data from disease populations and proposing a flexible methodological framework to mitigate these biases and advance health equity.

Hurwitz, E., Connelly, E., Sklerov, M. + 5 more2026-03-06📄 health informatics

Longitudinal effects ambient AI scribe use on documentation burden and financial productivity: A quasi-experimental study

This quasi-experimental study demonstrates that the adoption of ambient AI scribes by primary care clinicians leads to gradual, persistent improvements over 150 days, including a 15% reduction in documentation time, decreased work outside scheduled hours, and a 2% increase in financial productivity, underscoring the importance of longitudinal assessment to capture the full impact of such tools.

Waken, R., Lou, S. S., Hofford, M. + 16 more2026-03-05📄 health informatics

Agent Role Structure and Operating Characteristics in Large Language Model Clinical Classification: A Comparative Study of Specialist and Deliberative Multi-Agent Protocols

This study demonstrates that, even with fixed model parameters, altering the internal role decomposition of multi-agent LLM systems from a generic deliberative to a feature-specialist structure acts as a structured inductive bias that systematically reshapes sensitivity-specificity trade-offs and error distributions across different clinical classification tasks.

Anderson, C. G.2026-03-05📄 health informatics

Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts

This paper presents a systematic red-teaming framework for evaluating medical AI safety, revealing that while standard guardrails effectively block most adversarial attacks, they remain significantly vulnerable to authority impersonation strategies—particularly those framing requests as educational inquiries—which trigger behavioral mode-switching rather than factual errors.

Ekram, T. T.2026-03-05📄 health informatics

Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning

This paper presents a hybrid deep learning framework that combines iterative XGBoost-based label refinement with a Convolutional-Bidirectional LSTM model to significantly improve prediabetes diagnosis from Continuous Glucose Monitoring data by correcting misclassifications in the AI-READI dataset and achieving high diagnostic accuracy with reduced clinical burden.

Arethiya, N. J., Krammer, L., David, J. + 6 more2026-03-05📄 health informatics

Class imbalance correction in artificial intelligence models leads to miscalibrated clinical predictions: a real-world evaluation

This study demonstrates that while class imbalance correction techniques may improve certain metrics like recall, they severely compromise the calibration of AI models for surgical risk prediction, leading to significant risk overestimation and reduced clinical net benefit compared to models trained on natural data distributions.

Roesler, M. W., Wells, C., Schamberg, G. + 4 more2026-03-05📄 health informatics

Show Your Work: Verbatim Evidence Requirements and Automated Assessment for Large Language Models in Biomedical Text Processing

This study evaluates the impact of requiring verbatim, mechanically checkable evidence from large language models in biomedical text processing, finding that while such "show your work" constraints enhance auditability and enable higher-trust selective automation, they also introduce model-dependent trade-offs between verifiability and coverage.

Windisch, P., Weyrich, J., Dennstaedt, F. + 3 more2026-03-04📄 health informatics

Personalized Insights Derived from Wearable Device Data and Large Language Models to Improve Well-Being

By analyzing a year of wearable and self-reported data from 3,139 interns, this study reveals that mood drivers are highly individualized rather than universal, leading to the development of "MoodDriver," a large language model system that generates personalized mental health feedback to advance precision digital interventions.

He, K., Fang, Y., Frank, E. + 4 more2026-03-04📄 health informatics

Evaluating a Locally Deployed 20-Billion Parameter Large Language Model for Automated Abstract Screening in Systematic Reviews

This study demonstrates that a locally deployed 20-billion parameter LLM, utilizing a sensitivity-enhanced prompting strategy, can significantly accelerate systematic review abstract screening with high accuracy and zero data privacy risks, though its performance varies by domain and is best used as a second screener alongside human experts.

Moreira Melo, P. H., Poenaru, D., Guadagno, E.2026-03-04📄 health informatics

Perceptions of Artificial Intelligence in the Editorial and Peer Review Process: A Cross-Sectional Survey of Traditional, Complementary, and Integrative Medicine Journal Editors

A cross-sectional survey of Traditional, Complementary, and Integrative Medicine journal editors reveals that while they recognize the potential of artificial intelligence to support routine editorial tasks, its actual adoption remains limited due to a lack of institutional policies, training, and ethical guidance.

Ng, J. Y., Bhavsar, D., Krishnamurthy, M. + 20 more2026-03-04📄 health informatics

Using the ECHILD Database to Explore Educational and Health Outcomes of Unaccompanied Asylum-Seeking Children living in England (2005 to 2021)

This study utilizes the ECHILD database to characterize a national cohort of 37,170 Unaccompanied Asylum-Seeking Children in England (2005–2021), revealing that while only 21% were enrolled in state-funded schools, this group provides a foundational, linked dataset of 6,890 individuals for future research into the socio-demographic, legal, and environmental factors influencing their health and educational outcomes.

Langella, R., Hardelid, P., Lewis, K. M.2026-03-04📄 health informatics

Leveraging Generative Artificial Intelligence for Enhanced Data Augmentation in Emotion Intensity Classification: A Comprehensive Framework for Cross-Dataset Transfer Learning

This paper proposes a comprehensive cross-dataset transfer learning framework for emotion intensity classification that leverages prompt-conditioned generative models and diverse augmentation strategies to synthesize stylistically consistent target examples, demonstrating that conditional generative augmentation significantly outperforms other methods in accuracy and F1 scores while highlighting critical trade-offs between linguistic fluency and affective authenticity.

Wieczorek, J., Jiang, X., Palade, V. + 1 more2026-03-03📄 health informatics

Making sleep behaviors interpretable: adapting the two-process model of sleep regulation to longitudinal Fitbit sleep and activity behaviors for health insights

This study proposes a framework that adapts the neurobiological two-process model of sleep regulation to interpret large-scale longitudinal Fitbit data, successfully mapping wearable behaviors to circadian and homeostatic scores that align with known biological factors and demonstrate significant associations with depression diagnosis and severity.

Coleman, P., Annis, J., Master, H. + 4 more2026-03-03📄 health informatics