Effective Validation of Primary Care Patient Triage Classification Via Tele-Ophthalmology With Optometrist Support.

The triage classification and diagnosis of eye diseases in primary care patients through teleophthalmology were performed by ophthalmologists, optometrists, and family physicians. The agreement in and diagnosis between observers, as well as the quality of referrals from primary care to ophthalmology, were evaluated. Study to compare methods using a non-inferiority method approach. The sample comprised 220 patients with suspected ocular problems evaluated by family physicians. These patients were examined by an optometrist ) using a Visionix VX650 multi-diagnostic platform. The optometrist provided a suspected diagnosis and a referral. The tests were forwarded electronically to an ophthalmologist (observer 2), who provided a diagnosis and referral. Subsequently, they were reviewed in person by an ophthalmologist (observer 3) who provided a diagnosis and referral. The observers worked independently and at different times, without knowledge of each other's evaluations, ensuring the objectivity of the analysis. We analyzed interobserver agreement in and diagnosis using the Kappa index. The of patients by teleophthalmology coincides with that of in-person ophthalmology in 91.2% of the cases (Kappa = 0.819). The optician's coincided with in-person ophthalmology in 91.3% (Kappa = 0.858) and with teleophthalmology in 92.3% (Kappa = 0.851). The diagnostic categories, teleophthalmology and in-person ophthalmology coincided in 84.61% of the cases (Kappa = 0.80). The optician coincided with in-person ophthalmology in 90.65% (Kappa = 0.88) and with teleophthalmology in 90.9% of the diagnoses (Kappa = 0.87). There was a very high level interobserver agreement for both and diagnostic categories. This validates our teleophthalmology approach, conducted in a primary care setting by optometrists, family physicians, and ophthalmologists, for an effective triage classification of patients referred to ophthalmology services. This t also accurately categorizes patients based on their conditions, optimizing resources, reducing waiting times and waiting lists.