Journal of the American Medical Directors Association · 2026
Using Natural Language Processing to Improve Fall Documentation in VA Nursing Home Residents.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Graham Laura A, Liu Xiaojuan, Jing Bocheng, Lee Sei J, Steinman Michael A, Liu Christine Kee, Dave Chintan V, Li Yongmei, Fung Kathy, Odden Michelle C
Аннотация
OBJECTIVE: Falls can be deadly for older adults, making accurate documentation essential for prevention and quality improvement. The Minimum Data Set (MDS) is used in nursing homes to document falls; however, it fails to capture fall dates and has been critiqued for underreporting and misclassification. We developed a natural language processing (NLP) algorithm to extract fall dates from electronic health records (EHRs). This study outlines the NLP algorithm development and compares its findings with the MDS to evaluate misclassification and underreporting.
DESIGN: Retrospective cohort study.
SETTING AND PARTICIPANTS: All veterans residing in long-term care between January 7, 2012 and December 31, 2024.
METHODS: A rule-based NLP algorithm was developed to identify fall dates in the EHR and validated with manual chart review. The NLP results (reference) were compared with the MDS v3.0 assessment to assess the concordance between the 2 data sources.
RESULTS: We identified 38,852 long-term care residents with a median length of stay of 204 days. About half (49.0%) of residents experienced at least 1 fall during their stay, as identified by either data source. The NLP algorithm achieved 96% accuracy against manual chart review. Using the NLP output as the reference standard, the MDS correctly identified 86.1% of residents with a fall in the EHR (F1-score = 0.885, and specificity = 0.930). However, 2430 (6.3%) residents had falls documented in their medical records that were never reported in the MDS, and 3.9% of MDS falls were not identified in the EHR (misclassification). The median time between a fall and an MDS assessment documenting a fall was 17 days.
CONCLUSIONS AND IMPLICATIONS: The MDS identified 86% of NH residents with EHR documentation of a fall, but it did not provide the specific date of each fall. By supplementing MDS fall reporting with NLP, we may improve the sensitivity of fall detection and gain more precise information regarding when a fall occurs.
DESIGN: Retrospective cohort study.
SETTING AND PARTICIPANTS: All veterans residing in long-term care between January 7, 2012 and December 31, 2024.
METHODS: A rule-based NLP algorithm was developed to identify fall dates in the EHR and validated with manual chart review. The NLP results (reference) were compared with the MDS v3.0 assessment to assess the concordance between the 2 data sources.
RESULTS: We identified 38,852 long-term care residents with a median length of stay of 204 days. About half (49.0%) of residents experienced at least 1 fall during their stay, as identified by either data source. The NLP algorithm achieved 96% accuracy against manual chart review. Using the NLP output as the reference standard, the MDS correctly identified 86.1% of residents with a fall in the EHR (F1-score = 0.885, and specificity = 0.930). However, 2430 (6.3%) residents had falls documented in their medical records that were never reported in the MDS, and 3.9% of MDS falls were not identified in the EHR (misclassification). The median time between a fall and an MDS assessment documenting a fall was 17 days.
CONCLUSIONS AND IMPLICATIONS: The MDS identified 86% of NH residents with EHR documentation of a fall, but it did not provide the specific date of each fall. By supplementing MDS fall reporting with NLP, we may improve the sensitivity of fall detection and gain more precise information regarding when a fall occurs.