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36/100

✓ Peer-reviewed alzhub usefulness score 36 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

We used 1387 safety event reports for model development, 989 (71.3%) reports related to situational factors and 119 (8.6%) reports related to active failures. The model for situational factors achieved a precision of 0.843 and a recall of 0.826. The F1 score was 0.834, indicating a balance of precision and recall performance. The specificity of the model was 0.639 and the area under the receiver operating characteristic curve (ROC AUC) was 0.833. The final model for active failure achieved a precision of 0.333 and a recall of 0.056. The F1 score was 0.095, reflective of imbalanced precision and recall performance. The specificity of the model was 0.992, indicating a strong ability to identify negative cases, and the ROC AUC was 0.817. ML techniques can provide insights into situational factors and active failures that drive dementia-related safety events. These insights can inform targeted interventions such as specialised staff training for behavioural symptoms management and pharmacist-led medication optimisation, to enhance care and safety for hospitalised people living with dementia.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Bangerter, L. R., Fong, A., Zabala, G., Kim, Y. K., Tabaie, A., Werner, N. E., et al. (2025). Artificial intelligence approach to optimise safety for hospitalised patients with dementia. BMJ Open Quality. https://doi.org/10.1136/bmjoq-2024-003270

Vancouver

Bangerter LR, Fong A, Zabala G, Kim YK, Tabaie A, Werner NE, et al. Artificial intelligence approach to optimise safety for hospitalised patients with dementia. BMJ Open Quality. 2025. doi:10.1136/bmjoq-2024-003270. PMID: 40903180.

BibTeX

@article{bangerter2025-40903180,
  author  = {Bangerter, Lauren R. and Fong, Allan and Zabala, Garrett and Kim, Yijung K. and Tabaie, Azade and Werner, Nicole E. and De Jonge, Karl Eric and Ratwani, Raj M.},
  title   = {{Artificial intelligence approach to optimise safety for hospitalised patients with dementia}},
  journal = {BMJ Open Quality},
  year    = {2025},
  doi     = {10.1136/bmjoq-2024-003270},
  pmid    = {40903180},
  url     = {https://doi.org/10.1136/bmjoq-2024-003270}
}

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

Key findings

We used 1387 safety event reports for model development, 989 (71.3%) reports related to situational factors and 119 (8.6%) reports related to active failures.
The model for situational factors achieved a precision of 0.843 and a recall of 0.826.
The F1 score was 0.834, indicating a balance of precision and recall performance.
The specificity of the model was 0.639 and the area under the receiver operating characteristic curve (ROC AUC) was 0.833.
The final model for active failure achieved a precision of 0.333 and a recall of 0.056.

Why it matters

A machine learning model analyzed 1387 safety event reports from hospitalised patients with dementia, performing well for situational factors (precision 0.843, recall 0.826) but poorly for active failures (precision 0.333, recall 0.056). It aims to inform safety interventions and does not treat, cure, or prevent Alzheimer's disease.

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Study details

Effect size71.3%; 8.6%
Evidence strengthLow

How this study scored 36/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (BMJ Open Quality)
Journal's citation record	0 / 4	BMJ Open Quality: journal h-index 44 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 2.5× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Raj M. Ratwani: h-index 35 (OpenAlex, whole career)
Institution's track record	1 / 3	Vanderbilt University: institution h-index 1,199 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40903180)
Free to read	2 / 2	Free to read
Total	36 / 100	—

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