A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database

Abstract

Background: Validated methods to identify neuro-ophthalmologists in administrative data do not exist. The development of such method will facilitate research on the quality of neuro-ophthalmic care and health care utilization for patients with neuro-ophthalmic conditions in the United States. Methods: Using nationally representative, 20% sample from Medicare carrier files from 2018, we identified all neurologists and ophthalmologists billing at least 1 office-based evaluation and management (E/M) outpatient visit claim in 2018. To isolate neuro-ophthalmologists, the National Provider Identifier numbers of neuro-ophthalmologists in the North American Neuro-Ophthalmology Society (NANOS) directory were collected and linked to Medicare files. The proportion of E/M visits with International Classification of Diseases-10 diagnosis codes that best distinguished neuro-ophthalmic care (“neuro-ophthalmology–specific codes” or NSC) was calculated for each physician. Multiple logistic regression models assessed predictors of neuro-ophthalmology specialty designation after accounting for proportion of ophthalmology, neurology, and NSC claims and primary specialty designation. Sensitivity, specificity, and positive predictive value (PPV) for varying proportions of E/M visits with NSC were calculated. Results: We identified 32,293 neurologists and ophthalmologists who billed at least 1 outpatient E/M visit claim in 2018 in Medicare. Of the 472 NANOS members with a valid individual National Provider Identifier, 399 (84.5%) had a Medicare outpatient E/M visit in 2018. The model containing only the proportion of E/M visits with NSC best predicted neuro-ophthalmology specialty designation (odds ratio 1.05 [95% confidence interval 1.04, 1.05]; P < 0.001; area under the receiver operating characteristic [AUROC] = 0.91). Model predictiveness for neuro-ophthalmology designation was maximized when 6% of all billed claims were for NSC (AUROC = 0.89; sensitivity: 84.0%; specificity: 93.9%), but PPV was low (14.9%). The threshold was unchanged when limited only to neurologists billing ≥1% ophthalmology claims or ophthalmologists billing ≥1% neurology claims, but PPV increased (33.3%). Conclusions: Our study provides a validated method to identify neuro-ophthalmologists who can be further adapted for use in other administrative databases to facilitate future research of neuro-ophthalmic care delivery in the United States.