📄 Category

health informatics

256 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

People living with multiple long-term conditions have different pathways of unscheduled care in hospital: findings from an analysis of routinely-collected clinical data

An analysis of routine clinical data from a large UK hospital reveals that adults with multiple long-term conditions experience more complex unscheduled care pathways, including longer stays, higher mortality, and different ward transfer patterns compared to those without such conditions, suggesting they may receive suboptimal care.

Witham, M., Evison, F., Bellass, S., Cooper, R., Gallier, S., Pretorius, S., Sapey, E., Suklan, J., Sayer, A. A.2026-09-01
📄 health informatics

Artificial Scientific Intelligence for Measurement-burden-aware Modelling and Interpretation of Multi-site Bone Mineral Density

The study introduces DXA Agent, an agentic workflow that optimizes bone mineral density modeling by balancing predictive performance with measurement burden, demonstrating that cost-efficient models outperform conventional approaches in the UK Biobank while maintaining comparable results in NHANES.

Xiang, S., He, H., Xie, Z., Cheng, C.-Y., Li, H., Liu, D.2026-09-01
📄 health informatics

Drivers of Oncologist Preference of AI-Generated Literature Review in a Randomized Mixed-Methods Study

This randomized mixed-methods study reveals that oncologists' trust and preference for AI-generated literature reviews depend more on report design features—such as conciseness, verifiable citations, and explicit uncertainty—than on accuracy alone, as evidenced by significantly lower utility ratings for evidence-graded reports compared to standard formats despite similar reference quality.

Bunning, B. J., Weng, Y., Wu, D. J., Hui, G., Hope, J. E., Pandurangan, V., Lopez, I., Everett, S., Chen, J. H., Desai (…)2026-08-27
📄 health informatics

Offline Reinforcement Learning for Out-of-Distribution ICU Sepsis Decision Support

This paper demonstrates that offline reinforcement learning policies for ICU sepsis management maintain stable, action-sensitive decision-support signals under increasing out-of-distribution severity shifts, as evidenced by consistent model-predicted survival rates and improved physiological stabilization scores despite declining observed clinical outcomes.

Arasteh, E., Mirian, M. S., Tavakol, M.2026-08-25
📄 health informatics

Evaluating Clinical Concept Extraction and Evidence-Bounded Terminology Linking: Multisite Model Comparison and Pilot Ablation Study

This paper evaluates clinical concept extraction and evidence-bounded terminology linking through a multisite pilot study, demonstrating that while extraction performance varies significantly by matching criteria, the availability of retrieved evidence critically influences linking decisions and that initial unmatched terms often represent plausible existing concepts rather than true ontology novelties.

Chen, Y., Popescu, M.2026-08-24
📄 health informatics

Does Data Preprocessing Affect Tree-Based Super Learners? An Investigation of Ensemble Optimization and Oracle Properties in Clinical Classification.

This study demonstrates that for Super Learner ensembles composed of tree-based algorithms, data preprocessing yields negligible improvements in predictive performance and oracle behavior across most clinical datasets, suggesting that such preprocessing is not universally necessary and should be guided by specific dataset characteristics.

Darko, R., Dwumah, D., Agyapong, K. S., Agyenim-Boateng, Y., Darko Anim, R., Wisdom Jakper, J., Owusu-Ansah, N. K., Owus (…)2026-08-24
📄 health informatics

Grounding Health AI: Architecture and Evaluation of a Domain-Expert Metabolic Health Agent

This paper presents the HPP Personal Health Agent, a domain-specialized AI system that combines population-level phenotypic data, expert clinical tools, and declarative behavioral constraints to eliminate hallucinations and achieve high accuracy in metabolic health reporting, demonstrating that trustworthy medical AI requires a rigorous, eval-driven systems architecture rather than relying solely on general-purpose language models.

Diament, A., Sapir, G., Gorodetski, M., Wolf, A., Rice, A., Azouri, D., Etzion-Fuchs, A., Gelbard Solodkin, D., Talmor-B (…)2026-08-14