Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up

Abstract

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.

Contribution highlights

• We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system.

Extracted from the public abstract; no claims have been added.

Code and data

Publication metadata

DOI

10.3233/shti220112

PMID

35673051

PMCID

PMC10792587

Pages / article

433-437

Version relationship

No separate preprint-to-publication relationship is currently recorded.

BibTeX

@article{Diamond2022Naturallanguage,
  title = {Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up},
  author = {Courtney J Diamond and John Laurentiev and Jie Yang and Amy Wint and Kimberly A Harris and Tin H Dang and Amrita Mecker and Emily B Carpenter and Anna N Tosteson and Adam Wright},
  journal = {Studies in Health Technology and Informatics},
  year = {2022},
  url = {https://ebooks.iospress.nl/doi/10.3233/SHTI220112},
  doi = {10.3233/shti220112},
  pmid = {35673051},
  pages = {433--437}
}