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Computational Models of Complex Systems pp 59–73Cite as

Determining the ‘Fault Zone’ of Fall Events in Long Term Care

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 53))

Abstract

Background: Fall incidents in long-term care facilities are complex events, involving an interplay of factors at the individual and organisational level. However, research and care interventions have primarily focussed on examining specific intrinsic and extrinsic risk factors, rather than capturing a holistic understanding of the fall event. As a result, there is a paucity of research which has captured the contributory factors of falls at the personal, interpersonal and organisational level. Such understandings are important for broadening the evidence base and developing effective interventions which address potential causes at all levels. Methods: An ecological, systems based approach to analysing falls in long-term care allows a more holistic and systemic understanding of the fall event to emerge. This chapter applies a fuzzy cognitive map (FCM) modelling approach to understanding falls within long-term care, using an ecological analysis to demonstrate the inter-relationships between the various contributory factors at the micro individual level. The model is driven by case study data collected of a fall incident taking place in a long-term care facility in Greater Vancouver. Results: For experimentation, various real-life scenarios have been created to test the model. The results demonstrate that FCM models provide a potentially valuable tool for conceptualising the complexity of falls within long-term care, providing a means for better educating healthcare practitioners and designing targeted interventions. Conclusions: The ecological model provides the framework for analysing falls at various levels within the context of the long-term care facility and beyond (e.g., culture of falls prevention and falls prevention policy). The FCM technique provides a tool for visually conceptualising this complexity, highlighting the inter-relationships between individual factors and presenting the results in such a way that can be easily interpreted by academicians, practitioners and support staff.

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Acknowledgments

We thank Gerontology Research Centre and MoCSSy program at SFU for providing financial support for the project. The authors are also grateful to the IRMACS Centre, Simon Fraser University, BC, Canada for the technical support.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Memphis, Memphis, TN, USA

    Vijay Kumar Mago

  2. Gerontology Research Centre, Simon Fraser University, Harbour Centre, Vancouver, Canada

    Ryan Woolrych

  3. Department of Gerontology, Simon Fraser University, Harbour Centre, Vancouver, Canada

    Andrew Sixsmith

  4. Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, Canada

    Stephen N. Robinovitch

Authors
  1. Vijay Kumar Mago
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  2. Ryan Woolrych
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  3. Stephen N. Robinovitch
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  4. Andrew Sixsmith
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Corresponding author

Correspondence to Vijay Kumar Mago .

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Editors and Affiliations

  1. Department of Computer Science, Troy University, Troy, Alabama, USA

    Vijay Kumar Mago

  2. The IRMACS Centre, Simon Fraser University The MoCSSy Program, Burnaby, British Columbia, Canada

    Vahid Dabbaghian

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Mago, V.K., Woolrych, R., Robinovitch, S.N., Sixsmith, A. (2014). Determining the ‘Fault Zone’ of Fall Events in Long Term Care. In: Mago, V., Dabbaghian, V. (eds) Computational Models of Complex Systems. Intelligent Systems Reference Library, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-01285-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-01285-8_5

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