Evidence map›Paper›PMID 41296479›Full record

ArticleJAMA network open2025

Electronic Visit Use and Resolution Rates for Adult Outpatient Conditions in an Integrated Care Setting.

Tracy A Lieu, Jie Huang, Khai Tran, Dao Nguyen, Mary E Reed

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Tracy A LieuDivision of Research, Kaiser Permanente Northern California, Pleasanton.
Jie HuangDivision of Research, Kaiser Permanente Northern California, Pleasanton.
Khai TranThe Permanente Medical Group (TPMG), Pleasanton, California.
Dao NguyenThe Permanente Medical Group (TPMG), Pleasanton, California.
Mary E ReedDivision of Research, Kaiser Permanente Northern California, Pleasanton.

Funding

Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
NIDDK NIH HHS P30 DK092924
6 · The paper itself

Abstract

Importance: Asynchronous structured electronic visits (e-visits) in which online systems ask defined questions about prespecified conditions are an emerging modality for providing highly convenient outpatient care. Scant information exists on their use and outcomes. Objectives: To (1) compare the percentage of initial visits made via e-visits with telephone, video, or office appointments for 4 common outpatient conditions; (2) compare rates of related follow-up visits within 7 days among visit modalities; and (3) identify patient characteristics associated with e-visit and follow-up visit use. Design, Setting, and Participants: Cross-sectional analysis of e-visits and telephone, video, and office visits made in Kaiser Permanente Northern California, where The Permanente Medical Group provides care for 4.6 million patients. The study population was defined based on initial outpatient visits (e-visits, telephone or video appointment visits, or physician's office visits) by persons aged 18 years and older between January 1 and December 31, 2024, for 4 prespecified conditions. Data from 2023 and 2025 were used to analyze selected baseline variables. Main Outcomes and Measures: The numbers of e-visits and other types of visits for 4 diagnoses (urinary tract infection [UTI], seasonal allergies, acne, and international travel advice) in adults from January through December 2024 identified in electronic health record data and, secondarily, telephone advice calls identified for UTI alone. Initial visits were those without previous visits or medications for the condition in the prior 365 days. Analyses of the rates of follow-up visits used modified Poisson regression and adjusted for patient characteristics. To simplify interpretation, adjusted percentages of different types of visits and adjusted rates of follow-up visits were calculated via marginal standardization. Results: In 73 560 initial visits (86% by females; 41% by participants aged 18-39 years), e-visits were used in 58% (27 268 of 47 264) of UTI, 24% (3718 of 15 646) of seasonal allergy, 24% (1532 of 6426) of acne, and 56% (2377 of 4224) of international travel visits. E-visit use was highest among patients aged 18 to 29 years or 30 to 39 years for most conditions. After e-visits, follow-up visits for the same diagnosis within 7 days were uncommon: for UTI, the adjusted rate was 2.48%; for seasonal allergy, 6.45%; for acne, 2.11%; and for international travel, 0.12%. Follow-up visit rates after e-visits were lower than after other visit modalities for UTI, higher for seasonal allergy, and similar for acne and travel. For UTI e-visits, the risk of follow-up visits was higher among patients aged older than 65 years (adjusted relative risk, 2.50; 95% CI, 1.60-3.92), Hispanic patients, Asian patients, or those who were Medicaid insured. For seasonal allergy and acne, the risk of follow-up visits did not vary with patient characteristics. Conclusions and Relevance: In this cross-sectional study, high percentages of patients selected asynchronous physician-managed e-visits for common outpatient conditions in a setting where other visit types were available. Both e-visits and other visit types attained high rates of resolution without follow-up within 7 days.

Indexed as

Ambulatory CareDelivery of Health Care, IntegratedOffice VisitsTelemedicineAdolescentAdultAgedCaliforniaCross-Sectional StudiesFemaleHumansMaleMiddle AgedOutpatientsTelephoneYoung Adult

Identifiers

PMID41296479
PMCPMC12658666

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.