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[Preprint]. 2024 Jun 26:2024.06.25.24309480.

doi: 10.1101/2024.06.25.24309480.

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives

Malvika Pillai^{1

2}, Terri L Blumke³, Joachim Studnia⁴, Yuqing Wang², Zachary P Veigulis⁵, Anna D Ware³, Peter J Hoover³, Ian R Carroll⁶, Keith Humphreys⁷, Thomas F Osborne^{3

8}, Steven M Asch^{1

9}, Tina Hernandez-Boussard², Catherine M Curtin^{1

10}

Affiliations

¹ Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA.
² Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA.
³ National Center for Collaborative Healthcare Innovation, Palo Alto VA Healthcare System, Palo Alto, CA, USA.
⁴ Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
⁵ Claimable Inc., Sacramento, CA, USA.
⁶ Department of Anesthesiology, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Psychiatry and the Behavioral Sciences, Stanford University, Stanford, CA USA.
⁸ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.

PMID: 38978655
PMCID: PMC11230313
DOI: 10.1101/2024.06.25.24309480

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives

Malvika Pillai et al. medRxiv. 2024.

[Preprint]. 2024 Jun 26:2024.06.25.24309480.

doi: 10.1101/2024.06.25.24309480.

Authors

Affiliations

¹ Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA.
² Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA.
³ National Center for Collaborative Healthcare Innovation, Palo Alto VA Healthcare System, Palo Alto, CA, USA.
⁴ Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
⁵ Claimable Inc., Sacramento, CA, USA.
⁶ Department of Anesthesiology, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Psychiatry and the Behavioral Sciences, Stanford University, Stanford, CA USA.
⁸ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.

PMID: 38978655
PMCID: PMC11230313
DOI: 10.1101/2024.06.25.24309480

Abstract

Postsurgical falls have significant patient and societal implications but remain challenging to identify and track. Detecting postsurgical falls is crucial to improve patient care for older adults and reduce healthcare costs. Large language models (LLMs) offer a promising solution for reliable and automated fall detection using unstructured data in clinical notes. We tested several LLM prompting approaches to postsurgical fall detection in two different healthcare systems with three open-source LLMs. The Mixtral-8×7B zero-shot had the best performance at Stanford Health Care (PPV = 0.81, recall = 0.67) and the Veterans Health Administration (PPV = 0.93, recall = 0.94). These results demonstrate that LLMs can detect falls with little to no guidance and lay groundwork for applications of LLMs in fall prediction and prevention across many different settings.

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Conflict of interest statement

Competing Interests None declared.

Figures

**Figure 1.**
Cohort flowchart detailing study participants included in the fall detection analysis

See this image and copyright information in PMC

References

1. Rubenstein L. Z. Falls in older people: epidemiology, risk factors and strategies for prevention. Age and Ageing 35, ii37–ii41 (2006). 10.1093/ageing/afl084 - DOI - PubMed
1. James S. L. et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017. Injury Prevention 26, i3–i11 (2020). 10.1136/injuryprev-2019-043286 - DOI - PMC - PubMed
1. Baek S., Piao J., Jin Y. & Lee S. M. Validity of the Morse Fall Scale implemented in an electronic medical record system. J Clin Nurs 23, 2434–2440 (2014). 10.1111/jocn.12359 - DOI - PubMed
1. Montero-Odasso M. et al. World guidelines for falls prevention and management for older adults: a global initiative. Age and Ageing 51 (2022). 10.1093/ageing/afac205 - DOI - PMC - PubMed
1. Florence C. S. et al. Medical Costs of Fatal and Nonfatal Falls in Older Adults. J Am Geriatr Soc 66, 693–698 (2018). 10.1111/jgs.15304 - DOI - PMC - PubMed

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This is a preprint.

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives

Affiliations

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives

Authors

Affiliations

Abstract

Conflict of interest statement

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References

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