Evidence map›Paper›PMID 35167675›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility.

Polina V Kukhareva, Tanner J Caverly, Haojia Li, Hormuzd A Katki, Li C Cheung, Thomas J Reese, Guilherme Del Fiol, Rachel Hess, David W Wetter, Yue Zhang and 3 more

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07073898 (Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial), which is not on this map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed
11.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07073898 nacompletednot on this mapstarted 2025, after this paper: background citation

Population Health Management Approaches to Increase Lung Cancer Screening in Community Health Centers - UG3 Pilot Clinical Trial

TypeinterventionalSponsorUniversity of UtahRan2025 to 2026Enrolled65ConditionsLung CancerArmsRepeated Text Messages (TM+), Conversational Agent (CA), Educational Video, Proactive Patient Navigation (PPN), Reactive Patient Navigation (RPN)
3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 81 citations in OpenAlex.

  1. Trial
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  7. Improving lung cancer screening diagnostic efficiency.Current opinion in pulmonary medicine · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Polina V KukharevaDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.ORCID 0000-0002-5576-1486
Tanner J CaverlyCenter for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, Michigan, USA.ORCID 0000-0002-6560-0547
Haojia LiDepartment of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.
Hormuzd A KatkiDivision of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Li C CheungDivision of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Thomas J ReeseDepartment of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.ORCID 0000-0002-1081-1670
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Rachel HessDepartment of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.
David W WetterDepartment of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
Yue ZhangDepartment of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.
Teresa Y TaftDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Michael C FlynnDepartment of Pediatrics, University of Utah, Salt Lake City, Utah, USA.
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.ORCID 0000-0003-4282-9338
University of Utah · USNational Cancer Institute · USUniversity of Michigan · USVanderbilt University · US

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
Scalable decision support and shared decision making for lung cancer screeningR18HS026198 · AHRQ · UNIVERSITY OF UTAH · PI KAWAMOTO, KENSAKU · 2019 to 2021
$1.2M
AHRQ HHS R18 HS026198NCATS NIH HHS UL1 TR002538NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

objectiveThe 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

methodsIn 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.

resultsOver 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.

conclusionThe use of longitudinal EHR smoking data could improve lung cancer screening.

Indexed as

Early Detection of CancerLung NeoplasmsAgedAged, 80 and overCross-Sectional StudiesElectronic Health RecordsHumansMass ScreeningMiddle AgedSmokingelectronic health recordslung cancer screeninglung cancer screening eligibilitypack-yearsself-reported smoking history

Identifiers

PMID35167675
PMCPMC9006678
OpenAlexW4210252919

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.