📄 Category

health informatics

180 papers

Health informatics sits at the vibrant intersection of medicine, data science, and technology, transforming how we store, analyze, and utilize health information. This rapidly evolving field empowers clinicians and researchers to uncover patterns in patient data, improve diagnostic accuracy, and personalize treatment plans without getting lost in complex databases. By turning raw medical records into actionable insights, these innovations are reshaping the future of healthcare delivery and population health management.

At Gist.Science, we bridge the gap between cutting-edge research and public understanding by curating the latest preprints from medRxiv specifically within this domain. Our team processes every new submission in this category, providing both accessible plain-language explanations and detailed technical summaries to ensure the science is clear for everyone, from policymakers to curious readers. Below are the latest papers in health informatics, freshly distilled and ready for you to explore.

📄 health informatics

Selective prediction as a triage gate for primary-care depression screening: quantifying and mitigating selection bias in CHARLS-2011

This study demonstrates that cumulative selection bias in primary-care depression screening inflates machine-learning metrics while distorting epidemiological associations, and proposes a decoupled selective prediction framework using a four-variable CART rule to safely triage only the most reliable 20% of patients for algorithmic scoring while routing the remainder to human evaluation.

Wang, Z., liu, y.2026-07-22
📄 health informatics

Behavioural readiness, not demographics, predicts wearable adoption and digital medicine integration in a diverse multinational population: a cross-sectional study of 3,004 adults in Qatar

This cross-sectional study of 3,004 adults in Qatar demonstrates that behavioural readiness, particularly daily exercise and willingness to share data, is a stronger predictor of wearable adoption and digital medicine integration than traditional demographic factors like education, highlighting the need to prioritize behavioural engagement over demographic targeting for equitable digital health implementation.

Zaghloul, H., Arabi, B., Al-Ani, M., Abdullah, A., El-Masri, R., AboMuslim, O., Al-Ahdab, F., Rizwan, M. R. M., Tag, Z. (…)2026-07-20
📄 health informatics

Assessing electronic health record potential for adaptive learning in multimorbidity care in Sub-Saharan Africa: a mixed-methods study of Zimbabwe's Impilo system

This mixed-methods study of Zimbabwe's Impilo EHR system reveals that while frontline health workers generate adaptive learning for multimorbidity care through a hybrid of digital and paper-based tools, the lack of socio-technical arrangements to stabilize and institutionalize this learning prevents the system from evolving into a true Learning Health System capable of driving broader care adaptation.

Dhodho, E., Choga, K., Mundoga, F., Chimberengwa, P. T., Gongora, R. T., Webb, K., Chinyanga, T. T., Banda, F., Masiye (…)2026-07-19
📄 health informatics

Chart review and genetic validation of electronic medical record dementia diagnoses in VA: The impact of CMS data

This study evaluates the impact of incorporating CMS data on electronic medical record algorithms for Alzheimer's disease and related dementias within the VA system, finding that while CMS data increases case detection and sensitivity, a broad algorithm without CMS data is optimal for epidemiology, whereas a strict algorithm with CMS data yields the strongest genetic associations for late-onset AD.

Logue, M., Lee, S. O., Gillis, M., Zhang, R., Lee, M., Marra, D., Lopez, F. V., Lynch, J., Panizzon, M. S., Tsuang, D. W (…)2026-07-17
📄 health informatics

Comparing Human and Large Language Model Responses to Patients Online Questions: Towards Multi-dimensional Patient-centered Support

This study empirically compares large language models and peer responses to patients' online questions about laboratory test results, finding that while LLMs excel at providing clear, structured medical explanations, peers offer more personalized emotional support, suggesting that LLMs could effectively complement peer communities if they improve their emotional depth, reasoning transparency, and alignment with community norms.

Hussein, M. A., Doshi, R., He, L., Reynolds, T.2026-07-17
📄 health informatics

FootNet: A Multi-View Smartphone Dataset and Four-Model Benchmark for Clinical Foot Segmentation

This paper introduces FootNet, a multi-view smartphone dataset with expert-annotated masks for clinical foot segmentation, and establishes a benchmark demonstrating that a U-Net with a MobileNetV2 encoder significantly outperforms other models, including DeepLabV3, UNet++, and SAM ViT-B, in terms of segmentation accuracy.

Vijay, A., Prabhune, A., Srihari, V. R., Rayampalli, A.2026-07-17
📄 health informatics

Beyond Intensity: Cross-Dataset Consistency of Temporal Facial Action-Unit Dynamics as Transferable Markers of Depression

This study demonstrates that while high-intensity facial action unit features often fail to generalize across diverse datasets, slower temporal dynamics and specific co-activation patterns (such as eye-mouth decoupling) serve as robust, transferable markers of depression, suggesting that directional consistency is a superior criterion for selecting features in multi-site affective research.

Jeong, I., Jang, M., Kim, J.-w., Kim, H., Park, S., Kim, D.-K., Park, J.-H., Kim, Y., Kim, J.-M., Lee, H., Jhon, M.2026-07-15