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Related Concept Videos

Types of Reports II: Incident or Occurrence Report01:21

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An Incident or Occurrence Report in a healthcare setting is a crucial document used to record any unexpected occurrence that may or may not have affected a patient, employee, or visitor. Such reports are critical to improving patient safety and include all details leading up to and including the event.
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In the healthcare industry, reports play a crucial role in documenting incidents within an agency. The primary objective of these reports is to ensure patient safety, uphold the...
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Design and Analysis for Fall Detection System Simplification
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Identifying low-quality patterns in accident reports from textual data.

July B Macedo1,2, Plinio M S Ramos1,2, Caio B S Maior1,3

  • 1CEERMA - Center for Risk Analysis, Reliability Engineering and Environmental Modeling, Federal University of Pernambuco, Brazil.

International Journal of Occupational Safety and Ergonomics : JOSE
|August 18, 2022
PubMed
Summary

This study used machine learning and natural language processing to analyze accident reports, revealing data quality issues. The findings highlight the need for improved report structures to enhance safety decision-making.

Keywords:
accident analysisautomatic classificationmachine learningnatural language processingoccupational safetysafety culturetopic modeling

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Area of Science:

  • Data Science
  • Natural Language Processing
  • Machine Learning
  • Safety Engineering

Background:

  • Accident investigation reports are crucial for safety improvements but often contain errors, missing, or redundant data.
  • Existing accident report databases are large, complex, and difficult to analyze effectively.
  • Low-quality data hinders the extraction of actionable knowledge for preventive and mitigative measures.

Purpose of the Study:

  • To propose text mining and natural language processing techniques for investigating low-quality accident reports.
  • To apply machine learning (ML) for detecting and analyzing inconsistencies within accident reports.
  • To assess the effectiveness of ML in identifying data quality issues and guiding report structure improvements.

Main Methods:

  • Utilized text mining and natural language processing (NLP) to process accident investigation reports.
  • Employed machine learning (ML) algorithms to detect and analyze data inconsistencies.
  • Applied the methodology to a dataset of 626 accident reports from a hydroelectric power company.

Main Results:

  • Initial ML performance revealed significant data divergences and structural issues within the accident reports.
  • The investigation confirmed the supposition of low data quality in the analyzed accident reports.
  • Restructuring the accident database improved its form, validating the findings on report quality.

Conclusions:

  • The proposed ML and NLP approach serves as a valuable diagnostic tool for assessing accident report quality.
  • Improving the design of accident investigation reports is essential for creating a more reliable knowledge source.
  • Enhanced report quality supports better decision-making in safety management and risk mitigation.