Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility. (15th February 2022)

• Record Type: Journal Article
• Authors: Kukhareva, Polina V; Caverly, Tanner J; Li, Haojia; Katki, Hormuzd A; Cheung, Li C; Reese, Thomas J; Del Fiol, Guilherme; Hess, Rachel; Wetter, David W; Zhang, Yue; Taft, Teresa Y; Flynn, Michael C; Kawamoto, Kensaku
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 5(2022)
• Page Start: 779
• Page End: 788
• Publication Date: 2022-02-15
• Subjects: lung cancer screening -- self-reported smoking history -- electronic health records -- lung cancer screening eligibility -- pack-years; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac020
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data. Materials and Methods: In this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic ® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects. Results: Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening ( P < .001). Discussion: Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening. Conclusion: The use of longitudinal EHR smoking data could improve lung cancer screening.