Gamified Training and AI Support for Improving Ear Infection Diagnosis in Primary Care

Acute otitis media (AOM), or middle ear infection, is one of the most common childhood infections and a leading cause of antibiotic prescribing in primary care. Diagnosing AOM can be challenging, as ear symptoms and eardrum appearances often overlap with mild or transient conditions. This uncertainty may lead to both unnecessary antibiotic use and missed cases requiring treatment, affecting patient safety and contributing to antibiotic resistance.

This study evaluates two digital tools designed to support more accurate diagnosis and treatment decisions in primary care:

1. AOM Dx \[diagnosis\] Trainer (Otitspelet) - a gamified digital training program for physicians that provides interactive exercises using eardrum images and patient cases, with direct feedback to improve diagnostic accuracy and adherence to guidelines. 2. AI-based diagnostic support - a system that analyses tympanic membrane images, with and without symptom and tympanometry data, to evaluate its potential for future diagnostic use.

The trial is coordinated by the Västra Götaland Region (VGR) in collaboration with Umeå University and conducted across four Swedish regions: Västra Götaland, Västerbotten, Östergötland, and Skåne. VGR leads the evaluation of the AOM Dx Trainer intervention, while Umeå University leads the AI development and retrospective diagnostic analyses. The study is carried out as a multicentre, cluster-randomised controlled trial in primary care, where participating primary care centres are randomised to either the training intervention or standard care.

Physicians are the research participants under evaluation. At intervention centres, physicians complete training with the AOM Diagnosis (Dx) Trainer before study start; at control centres, no training is provided. Each participating centre then conducts an 8-week observation period, during which physicians diagnose and manage patients with new-onset ear symptoms. Patients are included only to allow evaluation of physicians' diagnostic and treatment decisions and to provide data for AI analysis. Estimated patient enrollment is \~200. Depending on centre size and recruitment success, up to 20 primary care centres across four Swedish regions - Västra Götaland, Västerbotten, Östergötland, and Skåne - will participate. After each consultation, research nurses collect tympanic membrane images and tympanometry data from patients who have given informed consent. These data are used for expert panel reference diagnoses and retrospective AI analysis; no information is shared with treating physicians.

The primary outcome is diagnostic accuracy of tympanic membrane assessment by physicians trained with the AOM Dx Trainer compared with untrained physicians, using expert consensus as the reference standard. Secondary outcomes include adherence to treatment guidelines and antibiotic prescribing rates. The AI system's diagnostic performance will also be benchmarked against the expert panel and physician groups.

By combining educational and technological innovation, this study aims to enhance diagnostic precision, improve guideline adherence, and reduce unnecessary antibiotic use in primary care-strengthening antimicrobial stewardship and providing a scalable model for future infection management.