Authors: (including presenting author): :
Hon YL (1); Wong WYE(1); Chan LK (1); Tang HY(1); Lai YB (1); Leung WM(1); Poon KH (1)
Affiliation: :
(1) Fall Prevention Committee, Kwai Chung Hospital
Keyword 1: :
Fall prevention
Keyword 2: :
Patient safety
Keyword 3: :
Quality improvement
Keyword 4: :
Data-driven intervention
Keyword 5: :
Intervention strategies
Introduction: :
Falls represent a critical safety concern across healthcare settings, with significant implications for patient outcomes, healthcare costs, and institutional liability. Kwai Chung Hospital (KCH), as a major psychiatric facility, faces distinct fall-risk patterns across specialized units. Despite widespread implementation of fall-prevention protocols, many hospitals continue to experience suboptimal reductions in fall rates. This study implements a systematic, data-driven fall-prevention ecosystem that integrates advanced analytics, evidence-based interventions, and multi-platform dissemination strategies to achieve sustainable reductions in falls.
Objectives: :
This study pursues three principal objectives: (1) To reduce the incidence through targeted, evidence-based interventions; (2) To identify unit-specific risk patterns using precision analytics; (3) To establish real-time data dissemination, empowering frontline staff to execute timely intervention.
Methodology: :
A quasi-experimental study was conducted across all KCH units using data from the Advanced Incident Report System. Fall incidents were analyzed comparing the pre-intervention period (April 2024-March 2025) with the post-intervention period (April 2025-November 2025). The comprehensive intervention program was implemented in a phased approach beginning in October 2024. Six evidence-based intervention strategies were systematically implemented: (1) structured patient education programs for enhanced fall risk awareness; (2) staff competency development through SPRING Program and continuous education forums; (3) standardized fall risk assessment protocols; (4) integrated digital platforms for real-time data collection and predictive analytics; (5) Fall Prevention Day campaigns promoting awareness; (6) Data dissemination through structured meetings to frontline staff.
Result & Outcome: :
Statistical analysis demonstrated a highly significant reduction in fall incidence across all KCH units. Independent samples t-test revealed a significant difference between the pre-intervention (M=3.25 falls/month, SD=0.754, N=12) and post-intervention periods (M=2.13 falls/month, SD=0.641, N=8), t (18) = 3.462, p = 0.003. The intervention achieved a mean reduction of 1.125 falls per month, representing a 34.5% decrease with a large effect size (Cohen's d = 1.580, 95% CI [0.533, 2.595]). Breakthrough achievements included PACU sustaining zero nighttime falls for 8 consecutive months, while PICU nighttime falls decreased by 75% (0.5 to 0.125/month). Future efforts will focus on sustaining these improvements and further refining fall prevention interventions to ensure ongoing patient safety.
