Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients.

BMC medical informatics and decision making·2026
Same author

Current Evidence of Acetyl-L-Carnitine Use in Mood Disorders-: A Systematic Review and Meta-Analysis.

Neuropsychiatric disease and treatment·2026
Same author

A Structured Consent Framework for Research of Electroconvulsive Therapy in Advanced Dementia: Consent Process for the ECT-AD Trial.

The journal of ECT·2026
Same author

The Rise of Small Language Models in Healthcare: A Comprehensive Survey.

Computer science review·2026
Same author

Socioeconomic deprivation, rurality, and travel distance negatively impact survival in early-stage pancreatic ductal adenocarcinoma but are not associated with stage at diagnosis.

Cancer epidemiology·2026
Same author

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

Journal of the National Medical Association·2026

Related Experiment Video

Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

682

Mobility Functional Status Ascertainment in Electronic Health Records using Large Language Models.

Xingyi Liu1, Muskan Garg1, Heling Jia1

  • 1Mayo Clinic.

Research Square
|August 6, 2025
PubMed
Summary

Large Language Models (LLMs) can accurately extract patient mobility status from clinical notes in Electronic Health Records (EHRs). This approach enhances precision medicine by standardizing functional status data for research and clinical use.

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.6K

Related Experiment Videos

Last Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

682
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.6K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Geriatric Medicine

Background:

  • Global population aging necessitates precise functional status assessment for personalized medicine.
  • Electronic Health Records (EHRs) contain rich, yet largely unstructured, patient mobility data.
  • Standardizing mobility information from clinical notes is crucial for clinical applications and research.

Purpose of the Study:

  • To investigate the efficacy of Large Language Models (LLMs) in extracting and standardizing patient mobility status from unstructured EHR clinical notes.
  • To evaluate different LLM prompting strategies for mobility data extraction and impairment classification.
  • To assess the trustworthiness and generalizability of an LLM-based approach across multiple healthcare institutions.

Main Methods:

  • Annotation of 600 clinical notes from three healthcare institutions focusing on mobility and impairment expressions.
  • Utilizing the open-source Llama 3 model with zero-shot, few-shot, and task decomposition prompting techniques.
  • Performance evaluation through error analysis and calculation of patient-level accuracy and F1-scores for mobility extraction and impairment classification.

Main Results:

  • Mobility Extraction achieved a micro-average accuracy of 0.952 and an F1-score of 0.962.
  • Impairment Classification achieved a micro-average accuracy of 0.912 and an F1-score of 0.948.
  • Error analysis indicated clinically reasonable inferences, even in ambiguous cases, with a local, deterministic setup enhancing trustworthiness and generalizability.

Conclusions:

  • LLM-based solutions are feasible for extracting functional mobility status from unstructured EHR data.
  • This methodology supports the integration of mobility data into precision medicine initiatives and clinical research.
  • The developed approach demonstrates cross-institution generalizability and enhances data privacy and consistency.