health informatics 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

Social Determinants of Health and Chronic Disease Risk Prediction in the All of Us Research Program

Analyzing data from nearly 260,000 participants in the All of Us Research Program, this study demonstrates that integrating social determinants of health with demographics significantly improves chronic disease risk prediction, revealing that mental health outcomes are primarily driven by experiential factors like stress and discrimination, whereas cardiometabolic conditions are more strongly influenced by structural neighborhood characteristics, thereby supporting the adoption of condition-specific social screening and targeted interventions to reduce health disparities.

Kammer-Kerwick, M., Dave, Y., Parekh, V., McDonald, L., Watkins, S. C.2026-03-23

📄 health informatics

Impact of a Social Media Derived Digital Self Management Platform on Population Level Irritable Bowel Syndrome Emergency Utilization: A Controlled Interrupted Time Series Analysis Using South Korean National Health Insurance Data

This study demonstrates that a social media-informed digital self-management platform, "Jang Geongang," significantly reduced population-level irritable bowel syndrome emergency department visits and unplanned hospitalizations in South Korea, particularly among younger adults and those with the diarrhea-predominant subtype, as evidenced by a controlled interrupted time series analysis of national health insurance data.

Park, J.-H., Lim, A.2026-03-23

📄 health informatics

Automated Extraction of Cancer Registry Data from Pathology Reports: Comparing LLM-Based and Ontology-Driven NLP Platforms

This study demonstrates that an LLM-based platform (Brim Analytics) achieves high accuracy and efficient processing for extracting cancer registry data from pathology reports, outperforming an ontology-driven system (DeepPhe) particularly in T stage classification across pancreatic and breast cancer cases.

McPhaul, T., Kreimeyer, K., Baris, A., Botsis, T.2026-03-23

📄 health informatics

A clinic-updated digital twin for Parkinson's disease progression: governed Bayesian forecasting with uncertainty-gated reporting

This paper presents a governed Bayesian digital twin for Parkinson's disease that integrates sequential updating with a confidence-based suppression mechanism to deliver calibrated, equitable, and auditable multi-domain progression forecasts while explicitly withholding unreliable predictions.

Hemedan, A. A.2026-03-22

📄 health informatics

Aggregate benchmark scores obscure patient safety implications of errors across frontier language models

This study demonstrates that aggregate benchmark scores fail to capture critical patient safety risks in frontier language models for healthcare, as significant and unpredictable variations in error directionality, contextual bias, and crisis response across models reveal that overall accuracy alone cannot predict clinical safety.

Linzmayer, R., Ramaswamy, A., Hugo, H., Nadkarni, G., Elhadad, N.2026-03-20

📄 health informatics

Improving Medicare Fraud Detection Accuracy in Deep Learning by Exploring Feature Selection and Data Sampling Techniques.

This study demonstrates that combining deep learning with Chi-square feature selection and Synthetic Minority Over-sampling (SMOTE) techniques significantly improves Medicare fraud detection accuracy to 95.4% by effectively addressing dataset imbalance and irrelevant features.

Ahammed, F.2026-03-20

📄 health informatics

Joint Longitudinal-Survival Modelling of Patient-Reported Gastrointestinal Symptom Trajectories and Treatment Discontinuation in Irritable Bowel Syndrome: A Prospective Cohort Study from the Canadian Gut Project

This prospective cohort study of 2,847 Canadian IBS patients utilizes joint longitudinal-survival modeling to demonstrate that individual symptom trajectories are dynamically linked to treatment discontinuation, revealing that higher baseline severity and slower rates of symptom improvement significantly increase the risk of stopping therapy.

Thornton, E., Kellerman, J.2026-03-19

📄 health informatics

HybridNet-XR: Efficient Teacher-Free Self-Supervised Learning for Autonomous Medical Diagnostic Systems in Resource-Constrained Environments.

This paper introduces HybridNet-XR, a memory-efficient, teacher-free self-supervised hybrid CNN that achieves state-of-the-art diagnostic accuracy on chest radiographs with minimal VRAM usage, offering a robust solution for autonomous medical systems in resource-constrained environments.

Mayala, S., Mzurikwao, D., Suluba, E.2026-03-19

📄 health informatics

Clinician Experiences with Ambient AI Scribe Technology in Singapore: A Qualitative Study

This qualitative study of 28 clinicians at Singapore's Alexandra Hospital reveals that while ambient AI scribe technology offers significant potential to reduce administrative burden and enhance patient engagement, its successful implementation in Singapore's multilingual healthcare system requires addressing critical challenges related to documentation accuracy, workflow adaptation, and compliance with local privacy regulations.

Shankar, R., Goh, A., Xu, Q.2026-03-19

📄 health informatics

OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery

The paper introduces OpenScientist, an open-source agentic AI co-scientist that significantly accelerates biomedical discovery by autonomously executing complex data analyses and generating verifiable clinical insights across diverse case studies, reducing tasks that typically take weeks to mere minutes.

Roberts, K. F., Abrams, Z. B., Cappelletti, L., Moqri, M., Heugel, N., Caufield, J. H., Bourdenx, M., Li, Y., Banerjee (…)2026-03-18

← Previous Next →