Evidence map›Paper›PMID 21045097›Full record

Trial reportJAMA2010

Fall prevention in acute care hospitals: a randomized trial.

Patricia C Dykes, Diane L Carroll, Ann Hurley, Stuart Lipsitz, Angela Benoit, Frank Chang, Seth Meltzer, Ruslana Tsurikova, Lyubov Zuyov, Blackford Middleton

3 registry-linked trialsAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in JAMA, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 89 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 17 pooled it
52.1field-weighted citation impact, top 1% of its field
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.

NCT00675935 nacompletednot on this map

Translating Fall Risk Status Into Interventions to Prevent Patient Falls: Falls TIPS (Tailoring Interventions for Patient Safety)

TypeinterventionalSponsorBrigham and Women's HospitalRan2007 to 2009Enrolled10,264ConditionsPatient FallsArmsFall Prevention Tool Kit prototype using a randomized design
NCT06339125 nacompletednot on this mapstarted 2024, after this paper: background citation

Predictive Analytics Combined With Computer Visualization Enhances Patient Safety and Eases Nurse Burden for Preventing Falls

TypeinterventionalSponsorMassachusetts General HospitalRan2024 to 2025Enrolled5,350ConditionsFall Patients, Fall InjuryArmsFall prevention algorithm, Inspiren camera visualization
NCT07092176 narecruitingnot on this mapstarted 2026, after this paper: background citation

Randomized Controlled Study on Fall Prevention in Community-Dwelling Older Adults: Physiatry Based Steady Strides Intervention vs Primary Care

TypeinterventionalSponsorSteady Strides: Fall Prevention and Stroke Rehabilitation Medical InstituteRan2026 to 2029Enrolled102ConditionsFall Prevention, FallsArmsSteady Strides
3 · Its place in the literature

Who cites it

89 citing papers in PubMed, 17 syntheses or guidelines pooled it, 360 citations in OpenAlex.

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  12. Effects of health information technology on patient outcomes: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016
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29 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Patricia C DykesBrigham and Women's Hospital/Partners HealthCare System, Harvard Medical School, Boston, MA, USA. pdykes@partners.org
Diane L Carroll
Ann Hurley
Stuart Lipsitz
Angela Benoit
Frank Chang
Seth Meltzer
Ruslana Tsurikova
Lyubov Zuyov
Blackford Middleton
Brigham and Women's Hospital · US

Funding

NINR NIH HHS L30 NR010962
6 · The paper itself

Abstract

contextFalls cause injury and death for persons of all ages, but risk of falls increases markedly with age. Hospitalization further increases risk, yet no evidence exists to support short-stay hospital-based fall prevention strategies to reduce patient falls.

objectiveTo investigate whether a fall prevention tool kit (FPTK) using health information technology (HIT) decreases patient falls in hospitals. DESIGN, SETTING, AND PATIENTS: Cluster randomized study conducted January 1, 2009, through June 30, 2009, comparing patient fall rates in 4 urban US hospitals in units that received usual care (4 units and 5104 patients) or the intervention (4 units and 5160 patients).

interventionThe FPTK integrated existing communication and workflow patterns into the HIT application. Based on a valid fall risk assessment scale completed by a nurse, the FPTK software tailored fall prevention interventions to address patients' specific determinants of fall risk. The FPTK produced bed posters composed of brief text with an accompanying icon, patient education handouts, and plans of care, all communicating patient-specific alerts to key stakeholders.

main outcome measuresThe primary outcome was patient falls per 1000 patient-days adjusted for site and patient care unit. A secondary outcome was fall-related injuries.

resultsDuring the 6-month intervention period, the number of patients with falls differed between control (n = 87) and intervention (n = 67) units (P=.02). Site-adjusted fall rates were significantly higher in control units (4.18 [95% confidence interval {CI}, 3.45-5.06] per 1000 patient-days) than in intervention units (3.15 [95% CI, 2.54-3.90] per 1000 patient-days; P = .04). The FPTK was found to be particularly effective with patients aged 65 years or older (adjusted rate difference, 2.08 [95% CI, 0.61-3.56] per 1000 patient-days; P = .003). No significant effect was noted in fall-related injuries.

conclusionThe use of a fall prevention tool kit in hospital units compared with usual care significantly reduced rate of falls.

trial registrationclinicaltrials.gov Identifier: NCT00675935.

Indexed as

Hospital Information SystemsHospitals, UrbanPatient Education as TopicAccidental FallsAgedCommunicationFemaleHumansMaleMiddle AgedRisk AssessmentSoftwareTreatment OutcomeWounds and Injuries

Identifiers

PMID21045097
PMCPMC3107709
OpenAlexW1964122721

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.