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Assessing Different Approaches to Leveraging Historical Smoking Exposure Data to Better Select Lung Cancer Screening Candidates: A Retrospective Validation Study.

Daniel J KatsYosra AdieAbdulhakim TlimatPeter J GrecoDavid C KaelberYasir Tarabichi
Published in: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco (2021)
EHRs are potentially well suited to aid in the risk-based selection of lung cancer screening candidates, but healthcare providers and systems may elect not to leverage EHR data due to prior work that has shown limitations in structured smoking exposure data quality. Our findings suggest that despite potential inaccuracies in the underlying EHR data, screening approaches that use multivariable models may perform significantly better than approaches that rely on simpler age and exposure-based criteria. These results should encourage providers to consider using pre-existing smoking exposure data with a model-based approach to guide lung cancer screening practices.
Keyphrases
  • electronic health record
  • healthcare
  • big data
  • smoking cessation
  • primary care
  • machine learning
  • risk assessment
  • social media
  • deep learning
  • health insurance
  • human health