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Related Experiment Video

Updated: Jun 5, 2026

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

Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health

Sarah C Lotspeich1, Abbey N Collins2, Brian J Wells3

  • 1Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC 27109, United States.

JAMIA Open
|June 4, 2026
PubMed
Summary

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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

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Algorithms using large language models (LLMs) can recover missing electronic health record (EHR) data, mimicking expert chart reviews. These clinically-driven tools offer a scalable solution for improving EHR data quality.

Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Artificial Intelligence in Healthcare

Background:

  • Electronic health record (EHR) data frequently contain missing values and errors, impacting data quality and clinical research.
  • Traditional chart reviews are effective but resource-intensive, limiting their application to large patient cohorts.
  • Previous work introduced a roadmap protocol using auxiliary diagnoses to impute missing EHR data.

Purpose of the Study:

  • To evaluate the accuracy and scalability of a roadmap-driven algorithm for recovering missing EHR data.
  • To compare the performance of LLM-enhanced roadmaps against traditional chart reviews.
  • To assess the feasibility of applying these algorithms to large-scale EHR datasets.

Main Methods:

  • Developed and refined roadmap algorithms using International Classification of Diseases, 10th revision (ICD-10) codes.
Keywords:
chart reviewscomputable phenotypelearning health systemmissing datawhole-person health

Related Experiment Videos

Last Updated: Jun 5, 2026

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

  • Iteratively enhanced roadmaps with large language models (LLMs) and clinical expertise to identify auxiliary diagnoses.
  • Validated algorithm performance against expert chart reviews for 100 patients and tested scalability on 1000 patients from an extensive EHR.

    • Main Results:

    • Expert chart reviews recovered 12% (49/413) of missing EHR values in 100 patients.
    • LLM-enhanced roadmap algorithms recovered 20%-22% (83-89/413) of missing values.
    • A clinician-approved LLM-enhanced algorithm recovered 18% (73/413) of missing values, balancing expansion and clinical relevance.
    • Application to 1000 patients increased the median non-missing EHR values per patient from 6 to 7.

    Conclusions:

    • Clinically-driven algorithms, augmented by LLMs, can accurately recover missing EHR data, comparable to manual chart reviews.
    • These algorithms demonstrate scalability for application to large EHR datasets.
    • Future work could extend these methods to address other data quality issues in EHRs.