ArticleBMC health services research2026
A five-year retrospective study of patient falls in a tertiary hospital: frequency, characteristics, and contributing factors.
Article in BMC health services 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.
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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFalls in hospitalized patients are among the most common preventable adverse events and pose a major threat to patient safety. Despite preventive protocols, falls remain frequent and are associated with increased morbidity, prolonged hospitalization, and higher healthcare costs. Understanding the frequency, characteristics, and contributing factors of falls is essential to improve patient safety strategies.
aimThis study aimed to examine the frequency and characteristics of inpatient falls over a five-year period and to identify conributing factors and reported causes based on adverse event notifications.
methodsA retrospective descriptive study was conducted using hospital adverse event notification records between 2020 and 2024. A total of 211 fall incidents were identified and analyzed in terms of demographic characteristics, clinical settings, fall locations, risk scores, and root causes. The Itaki Fall Risk Scale was used to assess patient risk levels, and contributing factors were categorized into patient-related, caregiver-related, equipment-related, environmental, staff-related, and procedural causes.
resultsAmong the 211 reported falls, the mean patient age was 50.4 ± 29.3 years, and most cases involved male patients (62.6%). The highest fall incidence was observed in 2024 (n = 78), indicating an increasing trend across the years. The majority of patients (92.4%) were classified as high risk according to the Itaki Fall Risk Scale. Most falls occurred in patient rooms (71.1%), followed by procedure/examination rooms (14.7%) and bathrooms/toilets (11.8%). Analysis of perceived causes reported by staff revealed that falls were primarily patient-related (59.7%), followed by caregiver-related (33.2%), equipment-related (4.7%), environmental factors (1.4%), and rarely staff- or procedure-related factors (0.5% each).
conclusionPatient falls remain a significant safety concern in hospitals, with most cases occurring among high-risk patients and being preventable. The analysis of reported causes highlights the multifactorial nature of falls, emphasizing the need for comprehensive preventive strategies that address patient behavior, caregiver awareness, environmental safety, and staff compliance with protocols. Strengthening fall prevention programs is crucial to improving patient safety and reducing healthcare costs. CLINICAL TRIAL NUMBER: Not applicable.
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.