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Related Concept Videos

Introduction to Documentation and Reporting01:20

Introduction to Documentation and Reporting

Documentation is the systematic process of formally recording, maintaining, and communicating information.
Nursing documentation records essential information and details regarding a patient's care and treatment in written or electronic form. It is a critical aspect of nursing practice that involves documenting assessments, interventions, outcomes, and other relevant details about a patient's health status.
Documentation maps the patient's health journey by creating a comprehensive and precise...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Types of Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

Telephone and Verbal Reports in healthcare settings are two communication methods for conveying therapeutic instructions from healthcare providers to nurses or other healthcare staff.
Here's an overview of each type:
Telephone Orders
Types of Reports I: Hand-off Report01:25

Types of Reports I: Hand-off Report

A hand-off report, also known as a change-of-shift report, is a crucial nursing process that ensures the smooth transition of patient care responsibilities between nursing staff.
Following are the key components and categories of hand-off reports:
Purpose and Process:

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From Report to Record: Prompt-Based Information Extraction from Gynecology Oncology Reports Using LLMs.

Livia Lilli1,2, Massimo Criscione2,3,4, Giovanni Paolo Tobia1

  • 1Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

This study introduces a prompt-based framework using Large Language Models (LLMs) to extract structured data from ovarian cancer Multidisciplinary Tumor Board (MTB) reports. The method demonstrates high accuracy and reliability for clinical knowledge extraction in oncology.

Keywords:
Gynecology OncologyIn-Context Learning PromptingLarge Language Model (LLM)Multidisciplinary Tumor Board (MTB)

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Published on: September 20, 2018

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • Large Language Models (LLMs) are increasingly capable of processing clinical text.
  • Unstructured medical data, such as Multidisciplinary Tumor Board (MTB) reports, present challenges in data extraction.
  • Ovarian cancer treatment relies on comprehensive patient data.

Purpose of the Study:

  • To develop and evaluate a prompt-based framework for extracting structured information from Italian-language ovarian cancer MTB reports.
  • To assess the accuracy and robustness of LLM-based information extraction from clinical narratives.
  • To support the creation of structured clinical knowledge for oncology.

Main Methods:

  • A prompt-based framework was designed for information extraction from the anamnestic section of MTB reports.
  • Key features including demographics, scores, comorbidities, treatments, and gynecological history were targeted.
  • In-context learning prompts and an LLM-as-a-judge majority-voting scheme were employed for evaluation.
  • Occurrence-based analysis was used to assess the influence of data frequency on extraction reliability.

Main Results:

  • The framework achieved high accuracy in extracting information across different macroareas of the MTB reports.
  • Strong agreement was observed among the judge LLMs, confirming the robustness of the extraction approach.
  • Variable data frequency was found to influence extraction reliability, necessitating a balanced performance interpretation.

Conclusions:

  • The developed framework enables reliable extraction of structured clinical information from unstructured MTB reports using LLMs.
  • This approach facilitates the creation of structured clinical knowledge, beneficial for oncology research and patient care.
  • The study highlights the potential of LLMs in transforming clinical data processing and supporting evidence-based medicine.