Evidence map›Paper›PMID 38848065›Full record

ArticleJAMA network open2024

Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.

Polina V Kukhareva, Haojia Li, Tanner J Caverly, Angela Fagerlin, Guilherme Del Fiol, Rachel Hess, Yue Zhang, Jorie M Butler, Chelsey Schlechter, Michael C Flynn and 7 more

Registry-linked trialAbstract readControlled Clinical Trial
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04498052 (Evaluation of a Scalable Decision Support and Shared Decision Making Tool for Lung Cancer Screening), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

NCT04498052 nacompletednot on this map

Evaluation of a Scalable Decision Support and Shared Decision Making Tool for Lung Cancer Screening

TypeinterventionalSponsorUniversity of UtahRan2020 to 2024Enrolled1,855ConditionsEarly Detection of Cancer, Lung NeoplasmsArmsEHR-integrated Shared Decision Making Tool and Clinical Decision Support for Lung Cancer Screening
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Lung cancer screening: are race- and risk-aware criteria needed?Journal of the National Cancer Institute · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Polina V KukharevaDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Haojia LiStudy Design and Biostatistics Center, University of Utah, Salt Lake City.
Tanner J CaverlyCenter for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, Michigan.
Angela FagerlinDepartment of Population Health Sciences, University of Utah, Salt Lake City.
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Rachel HessDepartment of Population Health Sciences, University of Utah, Salt Lake City.
Yue ZhangStudy Design and Biostatistics Center, University of Utah, Salt Lake City.
Jorie M ButlerDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Chelsey SchlechterDepartment of Population Health Sciences, University of Utah, Salt Lake City.
Michael C FlynnDepartment of Internal Medicine, University of Utah, Salt Lake City.
Chakravarthy ReddyStudy Design and Biostatistics Center, University of Utah, Salt Lake City.
Joshua ChoiDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Christian BalbinDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Isaac A WarnerDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Phillip B WarnerDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Claude NanjoDepartment of Biomedical Informatics, University of Utah, Salt Lake City.
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City.

Funding

CTSA UM1 Program at University of UtahUM1TR004409 · NCATS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI RACHEL HESS, Jennifer Juhl Majersik · 2023 to 2026
$21.9M
Engaging patients to enable interoperable lung cancer decision support at scaleR18HS028791 · AHRQ · UNIVERSITY OF UTAH · PI KAWAMOTO, KENSAKU · 2022 to 2024
$3.0M
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 HS026198AHRQ HHS R18 HS028791NCATS NIH HHS UM1 TR004409
6 · The paper itself

Abstract

Importance: Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes. Objective: To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services. Design, Setting, and Participants: This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024. Interventions: Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS. Main Outcome and Measure: The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making. Results: The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2. Conclusions and Relevance: In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure. Trial Registration: ClinicalTrials.gov Identifier: NCT04498052.

Indexed as

Early Detection of CancerElectronic Health RecordsLung NeoplasmsAgedAged, 80 and overDecision Support Systems, ClinicalFemaleHumansInterrupted Time Series AnalysisMaleMiddle AgedTomography, X-Ray ComputedUtah

Identifiers

PMID38848065
PMCPMC11161845

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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.