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Record W4415992274 · doi:10.2196/75483

TeleAllergy: Potential of Telemedicine in Management of Patients With Allergies

2025· article· en· W4415992274 on OpenAlexvenueno aff
Hanna Lindemann, Emil Hammer, Luca Bonifacio, Christian Greis, Karin Hartmann

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineAllergyDisease managementMEDLINEPrimary care

Abstract

fetched live from OpenAlex

Background: The growing prevalence of allergic diseases alongside a shortage of trained allergists creates significant challenges in delivering timely care, especially for underserved populations. Telemedicine presents a promising solution, offering remote care through digital tools. While telemedicine has been widely adopted in other fields, its use in allergy care remains underexplored. Objective: This study aimed to assess the potential of telemedicine in managing allergic diseases by examining patient preferences and experiences. Methods: A survey of 27 questions was distributed to adult patients (>18 y) with allergic diseases attending the outpatient allergy clinic at the Division of Allergy, University Hospital Basel, Basel, Switzerland, between May and August 2024. The survey covered demographic information, prior use of telemedicine, and preferences for teleconsultation modalities. It also assessed patients' willingness to share various types of clinical data, including images and written reports, and explored which allergic diseases were considered appropriate for telemedicine. Results: A total of 102 patients participated in the survey, with a mean age of 44.4 years (SD 16.7 y). For further analysis, the patients were stratified into four age groups: 18-34 years (36/102), 35-49 years (26/102), 50-64 years (31/102), and ≥65 years (9/102). Among them, 44% (41/94; P=.22) had previously used telemedicine services, with 34% (32/94; P=.04) specifically using it for allergic diseases. When asked about consultation formats, 49% (49/100) of patients preferred in-person visits, while 41% (41/100) favored a hybrid model combining telemedicine and in-person care. Regarding telemedicine tools, 57% (51/89) preferred telephone consultations with a doctor. Patients would use telemedicine preferentially for mild compared to severe allergic diseases as well as for chronic compared to acute conditions. The spectrum of diseases for which patients would use telemedicine comprised a wide range of allergic conditions, with allergic rhinoconjunctivitis (16%; 14/85), Hymenoptera venom allergy (13%; 11/85), and food allergy/intolerance (13%; 11/85) cited most frequently. Only 7% (6/85) of patients indicated they would not use telemedicine for any allergic disease. Conclusions: This study emphasizes the growing adoption and importance of telemedicine in allergy care, with a significant proportion of patients already having experience using it for managing allergic diseases. Patients' inclination toward multiple communication formats underscores the growing need for individualized management of allergic diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.321
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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