ArticleBMJ open quality2026
Using AI to identify in-hospital falls: a comparative analysis with incident reports and manual chart review.
Article in BMJ open quality, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
introductionFalls remain a problem in hospitalised patients, especially in older adults. In-hospital falls occasionally lead to injuries. Currently, the rate of falls in hospitals is monitored using incident reports, which often leads to underreporting. Recently, natural language processing (NLP) models gained interest. In this study, an NLP model (AI algorithm) to detect falls in nursing records was evaluated.
methodsIn this retrospective study conducted in a large teaching hospital in the Netherlands, hospitalised patients aged 18 years and older were included. Data were collected from free text in nursing records; all data were manually checked for fall incidents. For comparative analysis, the AI algorithm and incident reports were used. A descriptive comparison of the three different methods is provided by using absolute counts. For analysis, descriptive statistics and an area under the curve (AUC) were calculated. For time efficiency a comparative analysis was performed.
results76 fallers and 1547 non-fallers with a total of 30 615 nursing records were included. Using manual chart review, a total of 124 falls were identified. The AI algorithm showed good sensitivity (79.8%), good specificity (98.3%), excellent negative predictive value (NPV, 99.9%) and moderate positive predictive value (PPV, 16.4%). Incident reports had a sensitivity of 37.0% and a specificity of 99.9%, with a good PPV and NPV (97.9% and 99.7%, respectively). The AI algorithm had good discriminatory ability with an AUC of 0.891; incident reports had a moderate discriminatory ability with an AUC of 0.685. Utilising the algorithm, only 604 nursing records needed to be manually reviewed to identify 79.2% of all fall incidents, a higher detection rate than that achieved through incident reports (36.8% of all fall incidents). The semi-automated workflow using the AI algorithm requires significantly less time compared with incident reports, with a 92.6% reduction in time.
conclusionThe AI algorithm has a good discriminatory ability to timely detect in-hospital falls. Using this technique, more falls are identified than by using incident reports, while the administrative burden of nursing personnel is reduced. Moreover, it will enable more effective, near real-time preventive measurements.
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.