Public Health Analytics Program, National Opinion Research Center at the University of Chicago, Chicago, Illinois.
Department of Ophthalmology, University of Washington, Seattle.
JAMA Ophthalmol. 2023 Jun 1;141(6):534-541. doi: 10.1001/jamaophthalmol.2023.1263.
Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown.
To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington-affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss.
Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review.
Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans.
Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70).
In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.
来自行政索赔和电子健康记录 (EHR) 数据的诊断信息可能是监测视力和眼健康的重要资源，但这些来源的准确性和有效性尚不清楚。
评估与回顾性病历审查相比，行政索赔和 EHR 中的诊断代码的准确性。
设计、地点和参与者：这项横断面研究比较了基于 EHR 和索赔记录中的诊断代码与 2018 年 5 月至 2020 年 4 月在华盛顿大学附属眼科或验光诊所进行的回顾性病历审查相比，眼部疾病的存在和流行情况。纳入了过去 2 年内进行过眼部检查的 16 岁及以上患者，对诊断出的主要眼部疾病和视力丧失进行了过度抽样。
根据美国疾病控制与预防中心视觉和眼健康监测系统 (VEHSS) 的诊断病例定义以及基于回顾性病历审查的临床评估，将患者分配到视力和眼健康状况类别中，基于计费索赔历史和 EHR 中的诊断代码。
准确性的衡量标准是索赔和基于 EHR 的诊断编码的接收器工作特征曲线 (AUC) 与回顾性临床评估和治疗计划的比较。
在 669 名参与者中（平均[范围]年龄，66.1 [16-99] 岁；357 [53.4%] 名女性），使用 VEHSS 病例定义，计费和 EHR 数据中疾病的识别对于糖尿病视网膜病变（索赔 AUC，0.94；95%CI，0.91-0.98；EHR AUC，0.97；95%CI，0.95-0.99）、青光眼（索赔 AUC，0.90；95%CI，0.88-0.93；EHR AUC，0.93；95%CI，0.90-0.95）、年龄相关性黄斑变性（索赔 AUC，0.87；95%CI，0.83-0.92；EHR AUC，0.96；95%CI，0.94-0.98）和白内障（索赔 AUC，0.82；95%CI，0.79-0.86；EHR AUC，0.91；95%CI，0.89-0.93）是准确的。然而，几个类别表现出低有效性，AUC 值低于 0.7，包括诊断性屈光和调节障碍（索赔 AUC，0.54；95%CI，0.49-0.60；EHR AUC，0.61；95%CI，0.56-0.67）、诊断性失明和低视力（索赔 AUC，0.56；95%CI，0.53-0.58；EHR AUC，0.57；95%CI，0.54-0.59）和眼眶和外眼疾病（索赔 AUC，0.63；95%CI，0.57-0.69；EHR AUC，0.65；95%CI，0.59-0.70）。
在这项对当前和近期眼科患者的横断面研究中，这些患者的眼部疾病和视力丧失发生率较高，基于计费和 EHR 记录中的诊断代码识别主要威胁视力的眼部疾病是准确的。然而，诊断代码在计费和 EHR 数据中对视力丧失、屈光不正和其他广泛定义或风险较低的疾病类别识别准确性较低。
