Telehealth Approaches for Pediatric Otitis Media and Clinical Outcomes: Scoping Review

TL;DR

This review maps telehealth approaches for diagnosing pediatric ear infections, finding that remote methods can match in-person accuracy but require proper training and standardization.

Contribution

The study provides a comprehensive scoping review of telehealth models for pediatric otitis media, highlighting diagnostic accuracy and implementation outcomes.

Findings

Asynchronous tele-otoscopy achieves diagnostic accuracy comparable to in-person assessments with proper training.

Telemedicine improves access to care and can reduce unnecessary antibiotic use in some settings.

Diagnostic yield depends heavily on training level and image capture protocols.

Abstract

Otitis media (OM) is a common pediatric infection worldwide. Conventionally, accurate diagnosis depends on in-person pneumatic otoscopy, which is not always accessible, contributing to delayed care and inappropriate prescribing, especially in underserved settings. Rapid advances in telemedicine and digital tools have accelerated the development of remote approaches for assessing pediatric ear diseases, while diagnostic quality maintenance, care models, and real-world outcomes have not been comprehensively mapped in children.

This study aimed to map existing telehealth technologies and operational models used for pediatric OM and report their diagnostic and implementation outcomes to guide practice and further research.

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guided this scoping review (protocol not registered). MEDLINE (via PubMed), Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials were searched for original English-language studies (published between January 1, 2010, and February 9, 2026) reporting the clinical implementation of telemedicine or digital health in children (<18 years) with suspected or confirmed OM. We excluded reviews, editorials, and protocols; conference abstracts; adult-only studies; and purely technical evaluations. Two reviewers independently extracted and charted the study characteristics: telemedicine model, technology, users, comparators, outcomes, and limitations. Findings were synthesized using practice-oriented mapping that aligned technologies with care models and implementation conditions.

Fifty-two studies across 18 countries and various settings met the inclusion criteria. Sample sizes ranged from 6 to 3950, with heterogeneous reporting units (children, ears, episodes, or screening assessments). Asynchronous store-and-forward tele-otoscopy was the most common approach. The rest used synchronous or hybrid models. Image capture by trained personnel and review by experienced clinicians yielded substantial diagnostic consistency with in-person microscopy (κ 0.68‐0.89, sensitivity 72%‐94%, specificity 93%‐98%, where available). However, the diagnostic yield was highly dependent on the training level: structured instruction improved video capture by parents and nonspecialists, whereas brief or written-only guidance resulted in low rates of diagnostically useful videos. Telemedicine approaches improved access, supported perioperative follow-up, and, in some contexts, reduced reexaminations and promoted more judicious antibiotic use. Televisits without otoscopy were associated with lower confirmation rates of middle ear effusion during tympanostomy tube placement. Evidence was heterogeneous, with predominantly small single-site studies, variable reference standards and operator training, and rapidly evolving device ecosystems.

This review provides a practice-oriented map of telehealth approaches for pediatric OM. Tele-otoscopy and adjunct digital tools are feasible, achieving diagnostic accuracy comparable to in-person assessments while enhancing access and service efficiency. However, important evidence gaps remain, including the need for large multisite trials, evaluation of long-term child outcomes, economic evaluations, and robust external validation of artificial intelligence–based diagnostic tools. Standardization of image capture protocols and integration into hybrid care models should be prioritized for scaling up.

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