ArticleStudies in health technology and informatics2025
Predicting Secure Messaging Traffic in Clinical Settings.
Article in Studies in health technology and informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Beyond the pager: Rethinking trainee workload in the era of clinical messaging.Journal of hospital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Asynchronous text-based communication, secure messaging, has become one of the preferred modes of communication despite its potential to disrupt workflow and increase burden. Identifying peak communication can guide interventions to reduce communication overload among healthcare workers. The objective of this study is to predict future secure messaging volume among healthcare professionals utilizing a graph-based transformer model. The data consisted of secure messaging metadata from 14 inpatient hospitals and 381 outpatient clinics for the period February 1, 2023, through April 30, 2024. The model architecture consists of a multi-headed graph transformer to embedded hourly communication structures followed by transformer decoder architecture to predict the future messaging volume. Results show successful predictions across test sets, with improved performance using domain-specific time-dependent positional encoding. This approach integrates network topology, temporal patterns, and relational context (who is communicating and at what time) for accurate message traffic forecasting and underscore its novel design.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.