@article {1664947,
	title = {A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database},
	journal = {J Neuroophthalmol},
	volume = {43},
	number = {2},
	year = {2023},
	month = {2023 Jun 01},
	pages = {153-158},
	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 \&lt; 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.},
	keywords = {Aged, Delivery of Health Care, Humans, Medicare, Neurology, Ophthalmologists, Ophthalmology, United States},
	issn = {1536-5166},
	doi = {10.1097/WNO.0000000000001794},
	author = {Feng, Yilin and Lin, Chun Chieh and Hamedani, Ali G and De Lott, Lindsey B}
}
