Evidence map›Paper›PMID 41956437›Full record

ArticleJournal of the American Medical Directors Association2026

Using Natural Language Processing to Improve Fall Documentation in VA Nursing Home Residents.

Laura A Graham, Xiaojuan Liu, Bocheng Jing, Sei J Lee, Michael A Steinman, Christine Kee Liu, Chintan V Dave, Yongmei Li, Kathy Fung, Michelle C Odden

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Directors Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Laura A GrahamHealth Economics Resource Center, VA Palo Alto Health Care System, Palo Alto, CA, USA; S-SPIRE, Department of Surgery, Stanford University, Stanford, CA, USA. Electronic address: Laura.graham@va.gov.
Xiaojuan LiuDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Bocheng JingGeriatrics, Palliative, and Extended Care Service Line, San Francisco VA Medical Center, San Francisco, CA, USA.
Sei J LeeGeriatrics, Palliative, and Extended Care Service Line, San Francisco VA Medical Center, San Francisco, CA, USA; Division of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Michael A SteinmanGeriatrics, Palliative, and Extended Care Service Line, San Francisco VA Medical Center, San Francisco, CA, USA; Division of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Christine Kee LiuGeriatric Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, USA; Section of Geriatrics, Division of Primary Care and Population Health, Stanford University, School of Medicine, Stanford, CA, USA.
Chintan V DaveDepartment of Pharmacy Practice and Administration, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA; Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, USA.
Yongmei LiGeriatric Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
Kathy FungGeriatrics, Palliative, and Extended Care Service Line, San Francisco VA Medical Center, San Francisco, CA, USA; Division of Geriatrics, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Michelle C OddenGeriatric Research Education and Clinical Center, VA Palo Alto Health Care System, Palo Alto, CA, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.

Funding

VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI W. JOHN BOSCARDIN · 2013 to 2026
$19.9M
U.S. Deprescribing Research NetworkR24AG064025 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI BOYD, CYNTHIA MELINDA, STEINMAN, MICHAEL A. · 2019 to 2023
$9.9M
Evaluating the role of multimorbidity in modulation medication effects in older adultsR01HL163163 · NHLBI · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · PI Chintan Dave · 2023 to 2026
$2.7M
The U.S. Deprescribing Research NetworkR33AG086944 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI CYNTHIA Melinda BOYD, MICHAEL A. STEINMAN · 2024 to 2026
$2.5M
Deprescribing Cardiovascular Medications among Persons with and without Alzheimer's Disease and Related Dementias in Long-Term CareR01AG062568 · NIA · PALO ALTO VETERANS INSTIT FOR RESEARCH · PI Christine Kee Liu, Michelle Christina Odden · 2024 to 2026
$2.3M
Prescribing cascades in older adults with and without dementiaK24AG049057 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEINMAN, MICHAEL A. · 2015 to 2025
$1.8M
Prognostic calculators for patients with Alzheimers disease and related dementiasR01AG057751 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI LEE, SEI, SMITH, ALEXANDER KELIIMOEANU · 2018 to 2021
$1.5M
NHLBI NIH HHS R01 HL163163NIA NIH HHS K24 AG049057NIA NIH HHS P30 AG044281NIA NIH HHS R01 AG057751NIA NIH HHS R01 AG062568NIA NIH HHS R24 AG064025NIA NIH HHS R33 AG086944
6 · The paper itself

Abstract

objectiveFalls 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.

designRetrospective cohort study. SETTING AND

participantsAll veterans residing in long-term care between January 7, 2012 and December 31, 2024.

methodsA 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.

resultsWe 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.

Indexed as

Accidental FallsDocumentationNatural Language ProcessingNursing Home ResidentsAgedAged, 80 and overAlgorithmsElectronic Health RecordsFemaleHumansMaleNursing HomesRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsFallsNLPnursing home

Identifiers

PMID41956437
PMCPMC13078714

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.