Evidence map›Paper›PMID 38865702›Full record

ArticleJMIR formative research2024

Development of an Electronic Health Record Self-Referral Tool for Lung Cancer Screening: One-Group Posttest Study.

Garrett S Stang, Nichole T Tanner, Ashley Hatch, Jakarri Godbolt, Benjamin A Toll, Alana M Rojewski

Abstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Review
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

6 authors.

Garrett S Stang *Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, United States.ORCID https://orcid.org/0000-0001-7000-1277
Nichole T TannerHollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0000-0003-3768-1973
Ashley HatchHollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0009-0004-9090-6320
Jakarri GodboltHollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0009-0008-9161-7642
Benjamin A TollHollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0000-0001-7978-8416
Alana M RojewskiHollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0000-0003-1531-9287

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundApproximately 14 million individuals in the United States are eligible for lung cancer screening (LCS), but only 5.8% completed screening in 2021. Given the low uptake despite the potential great health benefit of LCS, interventions aimed at increasing uptake are warranted. The use of a patient-facing electronic health record (EHR) patient portal direct messaging tool offers a new opportunity to both engage eligible patients in preventative screening and provide a unique referral pathway for tobacco treatment.

objectiveThis study sought to develop and pilot an EHR patient-facing self-referral tool for an established LCS program in an academic medical center.

methodsGuided by constructs of the Health Belief Model associated with LCS uptake (eg, knowledge and self-efficacy), formative development of an EHR-delivered engagement message, infographic, and self-referring survey was conducted. The survey submits eligible self-reported patient information to a scheduler for the LCS program. The materials were pretested using an interviewer-administered mixed methods survey captured through venue-day-time sampling in 5 network-affiliated pulmonology clinics. Materials were then integrated into the secure patient messaging feature in the EHR system. Next, a one-group posttest quality improvement pilot test was conducted.

resultsA total of 17 individuals presenting for lung screening shared-decision visits completed the pretest survey. More than half were newly referred for LCS (n=10, 60%), and the remaining were returning patients. When asked if they would use a self-referring tool through their EHR messaging portal, 94% (n=16) reported yes. In it, 15 participants provided oral feedback that led to refinement in the tool and infographic prior to pilot-testing. When the initial application of the tool was sent to a convenience sample of 150 random patients, 13% (n=20) opened the self-referring survey. Of the 20 who completed the pilot survey, 45% (n=9) were eligible for LCS based on self-reported smoking data. A total of 3 self-referring individuals scheduled an LCS.

conclusionsPretest and initial application data suggest this tool is a positive stimulus to trigger the decision-making process to engage in a self-referral process to LCS among eligible patients. This self-referral tool may increase the number of patients engaging in LCS and could also be used to aid in self-referral to other preventative health screenings. This tool has implications for clinical practice. Tobacco treatment clinical services or health care systems should consider using EHR messaging for LCS self-referral. This approach may be cost-effective to improve LCS engagement and uptake. Additional referral pathways could be built into this EHR tool to not only refer patients who currently smoke to LCS but also simultaneously trigger a referral to clinical tobacco treatment.

Indexed as

cancer screeningdecision-makingdevelopmentEHRelectronic health recordelectronic health recordsHBMHealth Belief ModelLCSlung cancer screeningself-referself-referral tooltobacco treatment

Identifiers

PMID38865702
PMCPMC11208829

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Registered trials

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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.