ArticleHealth informatics journal
Electronic surveillance of patient safety events using natural language processing.
Article in Health informatics journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Clinician-in-the-loop screening saturation: predicting annotation yield for efficient EHR review.BMC medical informatics and decision making · 2025Article
- Article
- Article
- Artificial intelligence in healthcare: Opportunities come with landmines.BMJ health & care informatics · 2024Article
- Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Advancements in materiovigilance: A comprehensive overview.Perspectives in clinical researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveWe describe our approach to surveillance of reportable safety events captured in hospital data including free-text clinical notes. We hypothesize that a) some patient safety events are documented only in the clinical notes and not in any other accessible source; and b) large-scale abstraction of event data from clinical notes is feasible. MATERIALS AND
methodsWe use regular expressions to generate a training data set for a machine learning model and apply this model to the full set of clinical notes and conduct further review to identify safety events of interest. We demonstrate this approach on peripheral intravenous (PIV) infiltrations and extravasations (PIVIEs).
resultsDuring Phase 1, we collected 21,362 clinical notes, of which 2342 were reviewed. We identified 125 PIV events, of which 44 cases (35%) were not captured by other patient safety systems. During Phase 2, we collected 60,735 clinical notes and identified 440 infiltrate events. Our classifier demonstrated accuracy above 90%.
conclusionOur method to identify safety events from the free text of clinical documentation offers a feasible and scalable approach to enhance existing patient safety systems. Expert reviewers, using a machine learning model, can conduct routine surveillance of patient safety events.
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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.