A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database.
A Validated Method to Identify Neuro-Ophthalmologists in a Large Administrative Claims Database.
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在大型行政索赔数据库中识别神经眼科医生的经过验证的方法。
DOI：
10.1097/wno.0000000000001794
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
DeLott,LindseyB
中科院分区：
文献类型：
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作者：
Feng,Yilin;Lin,ChunChieh;Hamedani,AliG;DeLott,LindseyB
背景：在行政资料中识别神经眼科医生的有效方法尚不存在。这种方法的发展将促进美国神经眼科护理质量和神经眼科患者医疗保健利用的研究。方法：使用具有全国代表性的、来自2018年医疗保险载体文件的20%样本，我们确定了所有神经科医生和眼科医生在2018年至少开具了1份基于办公室的评估和管理（E/M）门诊就诊索赔。为了隔离神经眼科医生，收集了北美神经眼科学会（NANOS）目录中神经眼科医生的国家提供者识别码，并将其链接到医疗保险文件。计算每位医生使用国际疾病分类-10诊断代码（“神经眼科特异性代码”或NSC）最能区分神经眼科护理的E/M就诊比例。在考虑了眼科、神经病学、NSC索赔和主要专业指定的比例后，多元逻辑回归模型评估了神经眼科专业指定的预测因子。计算不同比例的NSC E/M就诊的敏感性、特异性和阳性预测值（PPV）。结果：我们确定了32293名神经科医生和眼科医生，他们在2018年的医疗保险中至少报销了1次门诊E/M就诊。在具有有效的个人国家提供者标识符的472名NANOS成员中，399名（84.5%）在2018年进行了医疗保险门诊E/M访问。仅包含NSC的E/M就诊比例的模型最能预测神经眼科专科指定（优势比1.05[95%置信区间1.04,1.05]；P< 0.001；受试者工作特征下面积[AUROC]= 0.91）。当所有账单索赔中有6%是NSC时（AUROC= 0.89；敏感性：84.0%；特异性：93.9%），模型对神经眼科指定的预测能力达到最大，但PPV较低（14.9%）。当仅限神经内科医生的眼科索赔≥1%或眼科医生的神经内科索赔≥1%时，阈值不变，但PPV增加（33.3%）。结论：我们的研究提供了一种有效的方法来识别神经眼科医生，这些医生可以进一步适用于其他管理数据库，以促进美国神经眼科护理服务的未来研究。
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.