Abstract
Background: Falls are leading causes of injury, hospitalization, and reduced quality of life among older adults in long-term care (LTC) settings(2,8) .This study explores the use of Natural Language Processing (NLP) to evaluate the uptake and impact of Preventing Falls and Reducing Injury from Falls (2017) best practice guideline (BPG)(4,5,6) in Ontario LTC homes.
Methods: A retrospective qualitative content analysis design was employed, and data were collected from 153 LTC reports submitted between 2015 to 2022 to a qualitative nursing data reporting system(3,4). Unstructured narrative text was processed through tokenization, part-of-speech tagging, stop-word removal, stemming/lemmatization, and keyword extraction via Rapid Automatic Keyword Extraction(1,7). Thematic analysis was supported by expert review, and sentiment analysis to assess alignment with the guideline.
Results: A total of 235 unique themes were extracted. Common themes included personalized care planning, revisions to falls documentation, post-incident evaluations, scheduled rounding, staff education, and policy integration. Sentiment analysis revealed mostly neutral to positive tones, with polarity scores used to visualize differences across submissions. However, gaps were found in documentation of post-fall procedures and policy execution.
Conclusion: NLP offers a scalable and efficient alternative to manual audits for monitoring evidence-based practice in LTC homes. Its ability to process multilingual data and incorporate expert validation enhances its utility across diverse care environments. Embedding the developed NLP algorithms into implementation science offers a practical approach to real-time monitoring of guideline adoption and supports the development of advanced decision-support systems, ultimately improving resident safety and care quality.
Notes
Acknowledgment: This work is funded by the Government of Ontario, Canada. All work produced by the RNAO is editorially independent of its funding source.
References:
1. Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: Analyzing text with the natural language toolkit. O'Reilly Media
2. Kuhnow J, Hoben M, Weeks LE, Barber B, Estabrooks CA. Factors associated with falls in Canadian long term care homes: a retrospective cohort study. Canadian geriatrics journal. 2022 Dec 1;25(4):328.
3. Naik, S., Voong S., Bamford, M., Smith, K., Joyce, A., & Grinspun D. (2020). Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index. Journal of the American Medical Informatics Association, 27(5), pp. 776-782.
4. Naik S, Grinspun D. RNAO’s artificial intelligence innovations: a novel strategy to advance evidence-based nursing practice. MedUNAB. 2024 July 31;27(1):42–51.
5. MedUNAB R. RNAO’s Artificial Intelligence Innovations: A Novel Strategy to Advance Evidence-Based Nursing Practice. MedUNAB [Internet]. 2024 Dec 6 [cited 2025 Sept 3]; Available from: https://www.academia.edu/126122294/RNAO_s_Artificial_Intelligence_Innovations_A_Novel_Strategy_to_Advance_Evidence_Based_Nursing_Practice
6. Registered Nurses’ Association of Ontario (RNAO). Preventing Falls and Reducing Injury from Falls. 4th ed. Toronto (ON): RNAO; 2017.
7. Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic keyword extraction from individual documents. In M. W. Berry & J. Kogan (Eds.), Text Mining: Applications and Theory (pp. 1–20). John Wiley & Sons. https://doi.org/10.1002/9780470689646.ch1
8. Stefanacci RG, Phillips C. Shifting Paradigms in Fall Management for Long-Term Care: From Prevention to Injury Mitigation. Journal of the American Medical Directors Association. 2025 Jun 1;26(6).
Sigma Membership
Non-member
Type
Presentation
Format Type
Text-based Document
Study Design/Type
Retrospective
Research Approach
Qualitative Research
Keywords:
Long-Term Care, Long Term Care, Interprofessional, Interdisciplinary, Accidental Falls, Accidental Falls--Prevention and Control, Natural Language Processing, Natural Language Processing--Utilization
Recommended Citation
Naik, Shanoja; Wimalarathna, Hasitha; and Grinspun, Doris, "Evaluating Fall Prevention Implementation Approaches Through Natural Language Processing" (2026). International Nursing Research Congress (INRC). 113.
https://www.sigmarepository.org/inrc/2026/presentations_2026/113
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-08-03
Evaluating Fall Prevention Implementation Approaches Through Natural Language Processing
Toronto, Ontario, Canada
Background: Falls are leading causes of injury, hospitalization, and reduced quality of life among older adults in long-term care (LTC) settings(2,8) .This study explores the use of Natural Language Processing (NLP) to evaluate the uptake and impact of Preventing Falls and Reducing Injury from Falls (2017) best practice guideline (BPG)(4,5,6) in Ontario LTC homes.
Methods: A retrospective qualitative content analysis design was employed, and data were collected from 153 LTC reports submitted between 2015 to 2022 to a qualitative nursing data reporting system(3,4). Unstructured narrative text was processed through tokenization, part-of-speech tagging, stop-word removal, stemming/lemmatization, and keyword extraction via Rapid Automatic Keyword Extraction(1,7). Thematic analysis was supported by expert review, and sentiment analysis to assess alignment with the guideline.
Results: A total of 235 unique themes were extracted. Common themes included personalized care planning, revisions to falls documentation, post-incident evaluations, scheduled rounding, staff education, and policy integration. Sentiment analysis revealed mostly neutral to positive tones, with polarity scores used to visualize differences across submissions. However, gaps were found in documentation of post-fall procedures and policy execution.
Conclusion: NLP offers a scalable and efficient alternative to manual audits for monitoring evidence-based practice in LTC homes. Its ability to process multilingual data and incorporate expert validation enhances its utility across diverse care environments. Embedding the developed NLP algorithms into implementation science offers a practical approach to real-time monitoring of guideline adoption and supports the development of advanced decision-support systems, ultimately improving resident safety and care quality.
Description
This study applies Natural Language Processing to assess falls prevention guideline uptake in Ontario Long Term Care facilities, revealing key themes and sentiment trends that support scalable monitoring of nursing practice.