Cai Cindy X, Tran Diep, Tang Tina, Liou Wilson, Harrigian Keith, Scott Emily, Nagy Paul, Kharrazi Hadi, Crews Deidra C, Zeger Scott L
Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland.
Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland.
Ophthalmol Sci. 2023 Mar 4;3(3):100295. doi: 10.1016/j.xops.2023.100295. eCollection 2023 Sep.
To develop a novel methodology to identify lapses in diabetic retinopathy care in electronic health records (EHRs) and evaluate health disparities by race and ethnicity.
Retrospective cohort study.
Adult patients with diabetes mellitus who were evaluated at the Wilmer Eye Institute from January 1, 2013 to April 2, 2022.
The methodology to identify lapses in care first identified diabetic retinopathy screening or treatment visits and then compared the providers' recommended follow-up timeframe with the patient's actual time to next encounter. The association of race and ethnicity with odds of lapses in care was evaluated using a mixed-effects logistic regression model controlling for age, sex, insurance, severity of diabetic retinopathy, presence of other retinal disorders, and glaucoma.
Lapses in diabetic retinopathy care.
The methodology to identify diabetic retinopathy-related visits had a 95.0% (95% confidence interval, 93.0-96.6) sensitivity and 98.8% (98.1-99.3) specificity as compared with a gold standard grader. The methodology resulted in a 97.3% (96.2-98.4) sensitivity and 98.1% (97.3-98.9) specificity for detecting a follow-up recommendation, with an average error of -0.05 (-0.31 to 0.21) weeks in extracting the precise timeframe. A total of 39 561 patients with 91 104 office visits were included in the analysis. The average age was 61.4 years. More than 3 (77.6%) in 4 patients had a lapse in care. In multivariable analysis, non-Hispanic Black patients had 1.24 (1.19-1.30) odds and Hispanic patients had 1.26 (1.13-1.40) odds of ever having a lapse in care compared with non-Hispanic White patients ( < 0.001, respectively).
We have developed a reliable methodology for identifying lapses in diabetic retinopathy care that is tailored to a provider's recommended follow-up. Using this approach, we find that 3 in 4 patients experience a lapse in diabetic retinopathy care and that these rates are higher among non-Hispanic Black and Hispanic patients. Deploying this methodology in the EHR is one potential means by which to identify and mitigate lapses in critical ophthalmic care in patients with diabetes.
Proprietary or commercial disclosure may be found after the references.
开发一种新方法，以识别电子健康记录（EHR）中糖尿病视网膜病变护理的失误，并按种族和民族评估健康差异。
回顾性队列研究。
2013年1月1日至2022年4月2日在威尔默眼科研究所接受评估的成年糖尿病患者。
识别护理失误的方法首先确定糖尿病视网膜病变筛查或治疗就诊情况，然后将提供者建议的随访时间框架与患者下次就诊的实际时间进行比较。使用混合效应逻辑回归模型评估种族和民族与护理失误几率的关联，该模型控制了年龄、性别、保险、糖尿病视网膜病变的严重程度、其他视网膜疾病的存在情况以及青光眼。
糖尿病视网膜病变护理失误。
与金标准分级员相比，识别糖尿病视网膜病变相关就诊的方法敏感性为95.0%（95%置信区间，93.0 - 96.6），特异性为98.8%（98.1 - 99.3）。该方法检测随访建议的敏感性为97.3%（96.2 - 98.4），特异性为98.1%（97.3 - 98.9），提取精确时间框架时的平均误差为 -0.05（-0.31至0.21）周。分析共纳入39561例患者的91104次门诊就诊。平均年龄为61.4岁。每4名患者中超过3名（77.6%）存在护理失误。在多变量分析中，与非西班牙裔白人患者相比，非西班牙裔黑人患者出现护理失误的几率为1.24（1.19 - 1.30），西班牙裔患者为1.26（1.13 - 1.40）（P均<0.001）。
我们开发了一种可靠的方法来识别糖尿病视网膜病变护理中的失误，该方法是根据提供者建议的随访量身定制的。使用这种方法，我们发现四分之三的患者存在糖尿病视网膜病变护理失误，并且在非西班牙裔黑人和西班牙裔患者中这些发生率更高。在电子健康记录中应用这种方法是识别和减少糖尿病患者关键眼科护理失误的一种潜在手段。
专有或商业披露信息可在参考文献之后找到。
