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Comparison Between Manual Auditing and a Natural Language Process With Machine Learning Algorithm to Evaluate Faculty

Carolina V Guimaraes1, Robert Grzeszczuk2, George S Bisset3

  • 1Department of Radiology, Texas Children's Hospital, Houston, Texas; Department of Radiology, Stanford University, Stanford, California.

Journal of the American College of Radiology : JACR
|December 23, 2017
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Summary
This summary is machine-generated.

Automated auditing using natural language processing and machine learning accurately assessed radiologist compliance with standardized radiology reports. This technology offers a more efficient alternative to manual audits, saving time and resources.

Keywords:
Standardized reportscompliancemachine learningnatural language processing

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Area of Science:

  • Radiology informatics
  • Medical informatics
  • Artificial intelligence in healthcare

Background:

  • Monitoring faculty compliance with standardized radiology reports is crucial for quality assurance.
  • Manual audits are the traditional method but are labor-intensive and prone to sampling errors.

Purpose of the Study:

  • To evaluate the accuracy of a software program utilizing natural language processing (NLP) and machine learning (ML) for auditing radiologist compliance with standardized reports.
  • To compare the automated auditing results against traditional manual audit findings.

Main Methods:

  • A software program employing NLP and ML was used to analyze radiology reports from a one-month period.
  • Faculty compliance rates were calculated automatically and compared to manually audited data (25 reports per faculty member for 42 faculty).

Main Results:

  • Manual audits yielded a mean compliance rate of 91.2% (95% CI: 89.3%-92.8%).
  • Automated auditing calculated a mean compliance rate of 92.0%, which fell within the confidence interval of the manual audit results.

Conclusions:

  • NLP and ML algorithms can accurately audit radiologist compliance with standardized reporting templates and language.
  • Automated auditing presents a viable, accurate, and potentially cost-saving alternative to manual auditing processes in radiology departments.