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Introduction to Documentation and Reporting01:20

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Documentation is the systematic process of formally recording, maintaining, and communicating information.
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Types of Reports III: Telephone and Verbal Reports01:26

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Telephone and Verbal Reports in healthcare settings are two communication methods for conveying therapeutic instructions from healthcare providers to nurses or other healthcare staff.
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Telephone Orders
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Methods of Documentation VI: Case Management Model01:15

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:  
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Related Experiment Video

Updated: Sep 8, 2025

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

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Leveraging Large Language Models for Accurate Retrieval of Patient Information From Medical Reports: Systematic

Angel Manuel Garcia-Carmona1, Maria-Lorena Prieto1, Enrique Puertas1,2,3

  • 1Research and Doctorate School, Universidad Europea de Madrid, Madrid, Spain.

JMIR AI
|July 3, 2025
PubMed
Summary

Large language models (LLMs) show high efficacy in extracting structured data from unstructured medical reports. GPT-4o achieved 91.4% accuracy, demonstrating LLMs

Keywords:
LangChain frameworkdata miningdigitalizationelectronic health recordshealth carelarge language modelsmodel evaluation

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Natural Language Processing
  • Health Informatics

Background:

  • Healthcare generates vast unstructured data, posing challenges for analysis.
  • Generative AI solutions are being explored for structured data extraction from medical reports.
  • Need for efficient methods to manage and utilize diverse medical data.

Purpose of the Study:

  • Investigate Large Language Models (LLMs) for automated structured information extraction from unstructured medical reports.
  • Evaluate LLM performance using the LangChain framework in Python.
  • Assess feasibility of integrating LLMs into healthcare workflows.

Main Methods:

  • Systematic evaluation of leading LLMs (GPT-4o, Llama 3, Llama 3.1, Gemma 2, Qwen 2, Qwen 2.5).
  • Utilized zero-shot prompting techniques and embedding results into a vector database.
  • Assessed extraction of patient demographics, diagnostic details, and pharmacological data.

Main Results:

  • High efficacy demonstrated across most data categories, with GPT-4o achieving 91.4% accuracy.
  • Notable differences in precision and recall observed between models, especially for names and age data.
  • LLMs show substantial improvements in data accessibility and clinical decision-making support.

Conclusions:

  • LLMs are feasible for healthcare integration, enhancing data accessibility and decision-making.
  • Retrieval-augmented generation techniques improve accuracy and address LLM limitations like hallucinations.
  • Future work requires larger datasets, advanced prompting, and domain-specific knowledge for better generalizability.