📄 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

Routine health information system data quality in a hill and mountain district of Nepal: a cross-sectional desk review of four years of DHIS2 data from Sankhuwasabha, 2021-2025

This cross-sectional desk review of four years of DHIS2 data from Sankhuwasabha district, Nepal, reveals that while reporting completeness and timeliness are generally high, the data harbor persistent immunization anomalies driven by systematic recording processes and the limitations of validation-rule-dependent consistency checks, underscoring the need to classify indicator pairs against active validation rules before assessing data quality.

Shrestha, D., Adhikari, P., Bhatta, M.2026-11-11✓ Author reviewed ⓘ
📄 health informatics

Room-Aware Transformer for Activities of Daily Living Recognition in Adults with Mild Neurocognitive Disorder Using Ambient Sensors: Development and Evaluation Study

This study demonstrates that a room-aware Transformer architecture, which preserves location-specific sensor structures, offers context-dependent improvements in recognizing activities of daily living for adults with mild neurocognitive disorder using ambient sensors, though performance varies significantly across homes and activity types, highlighting the need for personalized model development before clinical application.

Abdalnabi, N., Addepalli, V. r., Roessler, A., Kiselica, A., Kummerfeld, E., Sarker, S., Rao, P., Lee, K.2026-11-10
📄 health informatics

Accelerometer Data Harmonization and Feature Engineering for Gait Analytics

This study presents an open-source Python framework and data warehouse for integrating heterogeneous gait accelerometer data, demonstrating that while standardized processing enables cross-study analysis, significant feature variability persists across different sensor configurations and datasets, limiting the immediate comparability of derived metrics for tasks like gender classification.

Carrle, F. P., Schmidt, F., Reichenbach, A.2026-11-09
📄 health informatics

ShortKit-ML: an open toolkit for measuring shortcut potential in medical imaging embeddings

ShortKit-ML is an open toolkit that introduces a calibrated conditional probe to measure shortcut potential in frozen medical imaging embeddings, demonstrating that while its scores correlate with the reliance of confounded classifiers on sensitive attributes like sex, the tool alone cannot confirm or eliminate such reliance without also testing the classifier head itself.

Cajas, S., Marzullo, A., Kapadia, S., Santos, F., Ocampo Osorio, F., Kong, Q., Quarta, A., Kuo, P.-C., Patel, M., Rojas (…)2026-11-07
📄 health informatics

Machine Learning Based Detection of Chronic Kidney Disease using Full Blood Count from a Ghanaian Hospital Population with an Integrated Web Application

This study demonstrates that while machine learning models using Full Blood Count data can detect Chronic Kidney Disease in a Ghanaian population, their reported performance is critically dependent on preprocessing decisions, with an improper application of SMOTE inflating accuracy by 22.4 points compared to a rigorous cross-validation protocol, leading to the deployment of the explainable web tool NephroScan GH.

Akwetey, B. Y., Lawluvi, E. Y., Equagoo, T. T., Kumako, S., Tsinyo, E. A., Kumi, R. T., Mensah, I. R., Amedume, F. S., A (…)2026-10-31
📄 health informatics

From clinician review to measurable human oversight of AI-drafted paediatric discharge documents

In a randomized trial of 117 pediatric discharges, clinician review of AI-drafted documents successfully prevented serious errors and improved vocabulary accessibility, though it did not significantly enhance family understanding compared to conventional templates, highlighting the need for better metrics to measure human oversight of clinical AI.

Morquin, D., Bochu, G., Robert, L., Loi, Z., Buhnila, I., Ferreira, R., Jeziorski, E., Messaoui, L., Piarroux, R., Yauy (…)2026-10-22
📄 health informatics

Drug Safety Warnings in English, Russian, Arabic and Hebrew Wikipedia: A Cross-Sectional Audit of 33 Drugs Across Three Risk Domains

This cross-sectional audit of 33 drugs across English, Russian, Arabic, and Hebrew Wikipedia editions reveals that actionable drug-safety warnings are generally weaker in the non-English versions, with Russian articles often lacking content and Arabic articles frequently failing to escalate mentioned risks to explicit directives.

Berlin, M., Berkovitch, M., Benassayag Kaduri, N., Gorelik, E., Gorelik, B.2026-10-22
📄 health informatics

A Review of Suicide-Related Risks through Propensity Score Methods using the Maryland Suicide Data Warehouse

This study utilizes propensity score methods, including inverse propensity weighting and entropy balancing, on a large Maryland dataset to demonstrate that these techniques provide more nuanced and robust estimates of suicide risk factors and care utilization patterns compared to traditional multivariate logistic regression or sample matching.

Kitchen, C., Kush, J., Zirikly, A., Belouali, A., Nestadt, P., Wilcox, H., Kharrazi, H.2026-10-21
📄 health informatics

BanglaDiaHallu: Hallucinations of large language models and the effect of guideline-grounded retrieval-augmented generation in Bangla diabetes question answering

This study demonstrates that while large language models exhibit significant hallucination rates in Bangla diabetes question answering, grounding a locally deployable model with Bangladesh's National Guideline on Diabetes Mellitus via retrieval-augmented generation substantially improves medical accuracy and safety, though clinical oversight remains essential as no configuration is entirely free of errors.

Nahid, K., Anam, M., Saon, M. T. H., Any, O. H., Hassan, K. M. R., Anwar, M. M.2026-10-16