Literature DB >> 31948814

Patient Activation Related to Fall Prevention: A Multisite Study.

Taylor L Christiansen, Stuart Lipsitz, Maureen Scanlan, Shao Ping Yu, Mary Ellen Lindros, Wai Yin Leung, Jason Adelman, David W Bates, Patricia C Dykes.   

Abstract

BACKGROUND: Falls are a major problem in hospitals. The fall prevention program Fall TIPS (Tailoring Interventions for Patient Safety) has been shown to be effective in preventing inpatient falls and related injuries. The purpose of this study was to examine the impact of the Fall TIPS program on patient activation related to fall prevention.
METHODS: Patient activation was measured by surveying a random sample of adult patients before and after implementation of Fall TIPS at three health care systems. Researchers used the short form Patient Activation Measure (PAM-13) adapted for fall prevention. This 13-item survey assesses a patient's knowledge, skill, and confidence in managing his or her fall prevention. Patients (N = 343) were surveyed on medical units at Brigham and Women's Hospital (BWH), Montefiore Medical Center (MMC), and New York-Presbyterian (NYP) Hospital. Statistical analysis using the robust t-test was done to compare patient activation in the preintervention and postintervention groups.
RESULTS: Patient activation improved from preintervention to postintervention at all sites (BWH, p < 0.0001; NYP, p = 0.0373; MMC, p < 0.0001). Overall, the mean PAM score improved from 63.82 (standard deviation [SD] ± 17.35) to 80.88 (SD ± 17.48), p < 0.0001.
CONCLUSION: Patients with access to the Fall TIPS program are more activated and engaged in their fall prevention plan. Members of the care team should engage patients in their fall prevention plan to increase their knowledge, skill, and confidence.
Copyright © 2019 The Joint Commission. Published by Elsevier Inc. All rights reserved.

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Year:  2020        PMID: 31948814     DOI: 10.1016/j.jcjq.2019.11.010

Source DB:  PubMed          Journal:  Jt Comm J Qual Patient Saf        ISSN: 1553-7250


  2 in total

1.  A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition.

Authors:  Zhenxiao Zhao; Lei Zhang; Huiliang Shang
Journal:  Sensors (Basel)       Date:  2022-07-22       Impact factor: 3.847

2.  Evaluation of a Patient-Centered Fall-Prevention Tool Kit to Reduce Falls and Injuries: A Nonrandomized Controlled Trial.

Authors:  Patricia C Dykes; Zoe Burns; Jason Adelman; James Benneyan; Michael Bogaisky; Eileen Carter; Awatef Ergai; Mary Ellen Lindros; Stuart R Lipsitz; Maureen Scanlan; Shimon Shaykevich; David Westfall Bates
Journal:  JAMA Netw Open       Date:  2020-11-02
  2 in total

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