Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach
© 2024 The American Geriatrics Society..
BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires.
METHODS: Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors.
RESULTS: Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models.
CONCLUSIONS: The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:72 |
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| Enthalten in: |
Journal of the American Geriatrics Society - 72(2024), 4 vom: 08. Apr., Seite 1145-1154 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Song, Wenyu [VerfasserIn] |
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| Links: |
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| Themen: |
Community‐dwelling older adults |
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| Anmerkungen: |
Date Completed 16.04.2024 Date Revised 25.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/jgs.18776 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367079046 |
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| 245 | 1 | 0 | |a Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach |
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| 520 | |a © 2024 The American Geriatrics Society. | ||
| 520 | |a BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires | ||
| 520 | |a METHODS: Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, we conducted a case-control study to develop and evaluate prediction models for fall-related injuries in older adults. Questionnaire-derived prediction with three questions from a commonly used fall risk screening tool was evaluated. We then developed four temporal machine learning models using routinely available longitudinal EHR data to predict the future risk of fall injury. We also developed a fall injury-prevention clinical decision support (CDS) implementation prototype to link preventative interventions to patient-specific fall injury risk factors | ||
| 520 | |a RESULTS: Questionnaire-based risk screening achieved area under the receiver operating characteristic curve (AUC) up to 0.59 with 23% to 33% similarity for each pair of three fall injury screening questions. EHR-based machine learning risk screening showed significantly improved performance (best AUROC = 0.76), with similar prediction performance between 6-month and one-year prediction models | ||
| 520 | |a CONCLUSIONS: The current method of questionnaire-based fall risk screening of older adults is suboptimal with redundant items, inadequate precision, and no linkage to prevention. A machine learning fall injury prediction method can accurately predict risk with superior sensitivity while freeing up clinical time for initiating personalized fall prevention interventions. The developed algorithm and data science pipeline can impact routine primary care fall prevention practice | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a fall and fall‐related injury | |
| 650 | 4 | |a machine learning | |
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| 700 | 1 | |a Latham, Nancy K |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Luwei |e verfasserin |4 aut | |
| 700 | 1 | |a Rice, Hannah E |e verfasserin |4 aut | |
| 700 | 1 | |a Sainlaire, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Min, Lillian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Linying |e verfasserin |4 aut | |
| 700 | 1 | |a Thai, Tien |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Min-Jeoung |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Siyun |e verfasserin |4 aut | |
| 700 | 1 | |a Tejeda, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Lipsitz, Stuart |e verfasserin |4 aut | |
| 700 | 1 | |a Samal, Lipika |e verfasserin |4 aut | |
| 700 | 1 | |a Carroll, Diane L |e verfasserin |4 aut | |
| 700 | 1 | |a Adkison, Lesley |e verfasserin |4 aut | |
| 700 | 1 | |a Herlihy, Lisa |e verfasserin |4 aut | |
| 700 | 1 | |a Ryan, Virginia |e verfasserin |4 aut | |
| 700 | 1 | |a Bates, David W |e verfasserin |4 aut | |
| 700 | 1 | |a Dykes, Patricia C |e verfasserin |4 aut | |
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