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Updated: Jun 11, 2026

Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
From evidence-based guidelines to practice: Using natural language processing to identify impactful interventions in
Shanoja Naik1, Hasitha Wimalarathna1, Alexa Parousis1
1Registered Nurses' Association of Ontario, Toronto, Canada.
Objectives:
This study aimed to identify and characterize implementation strategies for falls prevention in long-term care (LTC) homes using natural language processing (NLP) with a focus on the Registered Nurses' Association of Ontario's (RNAO) Preventing Falls and Reducing Injury from Falls (2017) Best Practice Guideline (BPG).
Methods:
A retrospective quality improvement study was conducted using narrative reports submitted to RNAO MyBPSO reporting system from 63 Ontario LTC homes (2017 to 2022). Free-text data describing implementation activities were analyzed using an NLP pipeline that included text preprocessing, tokenization, and keyword extraction using Rapid Automatic Keyword Extraction (RAKE). Extracted terms were aggregated into candidate themes and further analyzed using a lexicon-informed sentiment analysis approach adapted for clinical language. Themes were iteratively refined through a structured consensus process involving multidisciplinary experts. Inter-rater agreement was assessed using Cohen's kappa.
Results:
A total of 153 reports generated 235 preliminary themes, which were refined through sentiment analysis and expert consensus into 10 themes. Commonly identified strategies included falls risk assessment, fall prevention strategies, risk factors of falls, care plan development, post-fall assessments, falls tools, fall documentation, education and training, huddles, organizational policies, rounding, reflecting substantial uptake of key BPG recommendations. Sentiment analysis demonstrated predominantly neutral or positive sentiments across implementation narratives, with variability indicating differences in implementation experience. Gaps were most evident in documentation, i.e., post-fall follow-up actions and policy-level strategies, suggesting opportunities to strengthen implementation fidelity.
Conclusions:
The study identified a core set of frequently implemented falls prevention strategies in LTC settings. These findings provide actionable guidance for clinical practice by emphasizing the need to standardize risk screening and reassessment processes, strengthen post-fall evaluation and follow-up, improve documentation systems to support continuity of care, and implement ongoing, competency-based education for staff. Together, these targeted strategies can support more consistent translation of evidence-based guidelines into routine practice and enhance quality-improvement efforts in falls prevention in LTC settings.
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