Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility.
J Am Med Inform Assoc
; 29(5): 779-788, 2022 04 13.
Article
in English
| MEDLINE | ID: covidwho-1821748
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 ANDMETHODS:
In this cross-sectional study, we evaluated 16â874 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Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%).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Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening.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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Early Detection of Cancer
/
Lung Neoplasms
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Aged
/
Humans
/
Middle aged
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
Jamia
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