ArticleNPJ digital medicine2021
Real-time clinician text feeds from electronic health records.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Language-informed treatment recommendations in psychiatry: bridging the gap in individual predictability.Frontiers in psychiatry · 2026Article
- Natural language processing data services for healthcare providers.BMC medical informatics and decision making · 2024Review
- Hospital-wide natural language processing summarising the health data of 1 million patients.PLOS digital health · 2023Article
- Early detection of COVID-19 outbreaks using textual analysis of electronic medical records.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2022Article
- Best practices in the real-world data life cycle.PLOS digital health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Analyses of search engine and social media feeds have been attempted for infectious disease outbreaks, but have been found to be susceptible to artefactual distortions from health scares or keyword spamming in social media or the public internet. We describe an approach using real-time aggregation of keywords and phrases of freetext from real-time clinician-generated documentation in electronic health records to produce a customisable real-time viral pneumonia signal providing up to 4 days warning for secondary care capacity planning. This low-cost approach is open-source, is locally customisable, is not dependent on any specific electronic health record system and can provide an ensemble of signals if deployed at multiple organisational scales.
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.