📄 Category

health informatics

225 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

Stakeholder perspectives on the use of enhanced mobile phone capabilities for public health surveillance for non-communicable disease risk factors: A qualitative study

This qualitative study of stakeholders in Uganda highlights that while mobile phone-based tools offer significant potential for improving non-communicable disease surveillance in low-resource settings, their successful and responsible implementation depends on proactively addressing critical ethical, legal, and social challenges related to privacy, equity, and data governance.

Mwaka, E. S., Nabukenya, S., Kasiita, V., Bagenda, G., Rutebemberwa, E., Ali, J., Gibson, D.2026-04-23
📄 health informatics

Decision Curve Analysis for Evaluating Machine Learning Models for Next-Day Transfer Out of ICU

This study demonstrates that Decision Curve Analysis provides a superior framework for evaluating machine learning models predicting next-day ICU transfers by quantifying clinical decision utility and optimizing operational thresholds to align with real-world workflow constraints, outperforming traditional discrimination metrics and simple clinical rules.

Pozo, M., Pape, A., Locke, B., Pettine, W. W.2026-04-21
📄 health informatics

Leveraging Predictive AI and LLM-Powered Trial Matching to Improve Clinical Trial Recruitment: A Usability Assessment of Trialshub

A usability assessment of Trialshub, an LLM-powered chat platform for clinical trial matching, found that while its intuitive design and workflow efficiency are highly valued by users, addressing technical reliability issues and refining conversational memory are critical for successful real-world deployment.

Blankson, P.-K., Hussien, S., Idris, F., Trevillion, G., Aslam, A., Afani, A., Dunlap, P., Chepkorir, J., Melgarejo, P. (…)2026-04-20
📄 health informatics

On Estimating Age and Gender from Parkinson's Disease Diagnostic-Oriented Recordings Using Wav2Vec 2.0

This study demonstrates that a pretrained Wav2Vec 2.0 model can robustly estimate gender and preserve age-related patterns in pathological speech across multilingual datasets, achieving high accuracy for gender and significant age correlations in connected speech while revealing task-dependent limitations for sustained vowel phonation.

Klempir, O., Tichopad, A., Krupicka, R.2026-04-15
📄 health informatics

Attitudes and Perceptions of Generative Artificial Intelligence Chatbots in the Scientific Process of Traditional, Complementary, and Integrative Medicine Research: A Large-Scale, International Cross-Sectional Survey

This large-scale international survey reveals that while a majority of Traditional, Complementary, and Integrative Medicine (TCIM) researchers recognize the potential of Generative AI chatbots to enhance research efficiency and reduce workload, they also identify significant challenges regarding bias and errors, highlighting a critical need for institutional training programs to support their effective integration.

Ng, J. Y., Tan, J., Syed, N., Adapa, K., Gupta, P. K., Li, S., Mehta, D., Ring, M., Shridhar, M., Souza, J. P., Yoshino (…)2026-04-15
📄 health informatics

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

This retrospective study utilizing a large national EHR dataset demonstrates that machine learning models, particularly XGBoost, can accurately predict unused outpatient appointments with an AUC of 0.95, suggesting that integrating such predictive tools into scheduling workflows could significantly improve healthcare efficiency.

Miran, S. A., Cheng, Y., Faselis, C., Brandt, C., Vasaitis, S., Nesbitt, L., Zanin, L., Tekle, S., Ahmed, A., Nelson, S. (…)2026-04-13
📄 health informatics

Spine Reviews: Crowdsourcing Global Spine Expert Knowledge via Digital Ledger Technology

This prospective study demonstrates that a novel blockchain-based crowdsourcing platform using Soulbound Tokens successfully aggregated rapid, cost-effective, and clinically coherent treatment recommendations from a global panel of spine specialists, revealing significant inter-clinician variability and establishing the need for diverse, multi-reviewer data in predictive modeling.

Challier, V., Diebo, B., Lafage, V., Dehouche, N., Lonjon, G., Cristini, J., SpineDAO,2026-04-13