Evidence map›Paper›PMID 41944392›Full record

ArticleClinical nursing research2026

Predicting Falls With the Morse Fall Scale: A Retrospective Assessment at the Veterans Health Administration.

Peter J Hoover, Terri L Blumke, Anna D Ware, Malvika Pillai, Amber L Wake, Kiran Rai, Jennifer S Lee

Abstract read
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Article in Clinical nursing research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Peter J HooverVA Palo Alto Healthcare System, CA, USA.ORCID 0000-0001-6235-3141
Terri L BlumkeVA Palo Alto Healthcare System, CA, USA.
Anna D WareVA Palo Alto Healthcare System, CA, USA.
Malvika PillaiVA Palo Alto Healthcare System, CA, USA.
Amber L WakeVA Palo Alto Healthcare System, CA, USA.
Kiran RaiVA Palo Alto Healthcare System, CA, USA.
Jennifer S LeeVA Palo Alto Healthcare System, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accidental falls are the second most prevalent adverse event within hospital settings, including U.S.'s Veterans Health Administration (VHA), often resulting in injury and prolonging patient recovery. The Morse Fall Scale (MFS) is widely used to assess fall risk within patient settings. Despite its known limitations, it remains a key tool for determining which patients are at risk for falls across healthcare institutions. Veterans with a completed MFS, Care Assessment Need (CAN), and Charlson Comorbidity Index (CCI), admitted to a VHA hospital from July 1, 2020, to June 30, 2022, were included in this retrospective analysis. MFS total scores were captured using electronic health record data from the VHA's national Corporate Data Warehouse. Veterans were identified as having a documented fall through clinical notes. Logistic regression models were used to evaluate the tool's performance at predicting falls. Among 440,771 Veterans, 14,458 (3.3%) had a documented fall during their inpatient stay. On average, those who experienced a fall had higher MFS scores. However, despite this trend, this tool exhibited poor discrimination in the likelihood of falls. Although incorporating patient demographics, the CAN, and CCI into the model improved performance, the model still performed poorly when assessing fall risk. Although the MFS is a required component of clinical workflow in many hospital systems, including VHA, its suboptimal discrimination of fall risk undermines its practical utility. Improved risk models are needed to assess fall risk and from which appropriate fall prevention measures can be initiated.

Indexed as

Accidental FallsVeteransAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentUnited StatesUnited States Department of Veterans Affairsfall preventionfall risk assessmentMorse Falls ScaleVeteran Health Administration

Identifiers

PMID41944392
PMCPMC13149994

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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.