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AI-based decision support to optimize complex care for preventing medication-related falls

Nature Medicine volume 30, pages 620–621 (2024)Cite this article

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Preventing and managing falls in older adults is a pressing global concern, especially in light of the world’s aging population. Falls are the leading cause of injury-related mortality and hospitalization among older adults. Even without injury, falls can substantially affect a person’s quality of life, by leading to reduced engagement in social and physical activities, physical decline, disability or institutionalization1. Moreover, falls lead to substantial healthcare costs, accounting for 0.85–1.5% of the total health care costs in high-income countries1.

One of the key modifiable risk factors for falls is the use of some medications, commonly referred to fall-risk increasing drugs (FRIDs)2. There is strong evidence that these medications include all types of psychotropic, antiepileptic, anticholinergic and several classes of cardiovascular medication2. Medication review with appropriate deprescribing is one of the most effective components of falls prevention1,2. However, deprescribing is often not attempted or performed successfully in cases where it could be performed safely. Physicians cite several reasons for their reluctance towards deprescribing, including lack of knowledge, skills and time, and fear of negative consequences3. This is understandable, given that deprescribing requires a personalized and patient-centered approach owing to the heterogeneity of older adults. Moreover, effective deprescribing requires physicians to carefully weigh the potential benefits and risks of medications, which is often complicated by polypharmacy and multimorbidity.

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Acknowledgements

The authors thank the members of the ADFICE _IT study team.

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Authors and Affiliations

  1. Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Bob van de Loo & Natasja M. van Schoor

  2. Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands

    Bob van de Loo, Wesna Belimbegovski, Lotta J. Seppala & Nathalie van der Velde

  3. Amsterdam Public Health Research Institute, Amsterdam, The Netherlands

    Bob van de Loo, Stephanie Medlock, Wesna Belimbegovski, Lotta J. Seppala, Ameen Abu-Hanna, Natasja M. van Schoor & Nathalie van der Velde

  4. Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands

    Annemiek J. Linn & Julia C. M. van Weert

  5. Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands

    Stephanie Medlock & Ameen Abu-Hanna

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  1. Bob van de Loo
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  9. Nathalie van der Velde
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Correspondence to Nathalie van der Velde.

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Competing interests

The ADFICE_IT study is supported by funding from the Netherlands Organization for Health Research and Development (ZonMw, grant 848017004), The Hague and the Amsterdams Universiteitsfonds: Gepersonaliseerde Medicatieaanpassing bij Oudere Vallers.

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van de Loo, B., Linn, A.J., Medlock, S. et al. AI-based decision support to optimize complex care for preventing medication-related falls. Nat Med 30, 620–621 (2024). https://doi.org/10.1038/s41591-023-02780-z

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