Division of Ophthalmology, Department of Surgery, McMaster University, Hamilton, Ontario, Canada.
Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
JAMA Ophthalmol. 2019 Jun 1;137(6):690-692. doi: 10.1001/jamaophthalmol.2019.0571.
Because more patients are presenting with self-guided research of symptoms, it is important to assess the capabilities and limitations of these available health information tools.
To determine the accuracy of the most popular online symptom checker for ophthalmic diagnoses.
DESIGN, SETTING, AND PARTICIPANTS: In a cross-sectional study, 42 validated clinical vignettes of ophthalmic symptoms were generated and distilled to their core presenting symptoms. Cases were entered into WebMD symptom checker by both medically trained and nonmedically trained personnel blinded to the diagnosis. Output from the symptom checker, including the number of symptoms, ranking and list of diagnoses, and triage urgency were recorded. The study was conducted on October 13, 2017. Analysis was performed between October 15, 2017, and April 30, 2018.
Accuracy of the top 3 diagnoses generated by the online symptom checker.
The mean (SD) number of symptoms entered was 3.6 (1.6) (range, 1-8). The median (SD) number of diagnoses generated by the symptom checker was 26.8 (21.8) (range, 1-99). The primary diagnosis by the symptom checker was correct in 11 of 42 (26%; 95% CI, 12%-40%) cases. The correct diagnosis was included in the online symptom checker's top 3 diagnoses in 16 of 42 (38%; 95% CI, 25%-56%) cases. The correct diagnosis was not included in the symptom checker's list in 18 of 42 (43%; 95% CI, 32%-63%) cases. Triage urgency based on the top diagnosis was appropriate in 7 of 18 (39%; 95% CI, 14%-64%) emergent cases and 21 of 24 (88%; 95% CI, 73%-100%) nonemergent cases. Interuser variability for the correct diagnosis being in the top 3 listed was at least moderate (Cohen κ = 0.74; 95% CI, 0.54-0.95).
The most popular online symptom checker may arrive at the correct clinical diagnosis for ophthalmic conditions, but a substantial proportion of diagnoses may not be captured. These findings suggest that further research to reflect the real-life application of internet diagnostic resources is required.
由于越来越多的患者自行研究症状，因此评估这些可用健康信息工具的能力和局限性非常重要。
确定最受欢迎的在线症状检查器在眼科诊断中的准确性。
设计、地点和参与者：在一项横断面研究中，生成了 42 个经过验证的眼科症状临床病例，并将其简化为其核心表现症状。由接受过医学培训和未接受过医学培训的人员将这些病例输入 WebMD 症状检查器，这些人员对诊断结果均不知情。记录症状检查器的输出结果，包括症状数量、排名和诊断列表以及分诊紧急程度。研究于 2017 年 10 月 13 日进行。分析于 2017 年 10 月 15 日至 2018 年 4 月 30 日之间进行。
在线症状检查器生成的前 3 个诊断的准确性。
输入的平均（SD）症状数为 3.6（1.6）（范围，1-8）。症状检查器生成的中位数（SD）诊断数为 26.8（21.8）（范围，1-99）。症状检查器的主要诊断在 42 例（26%；95%CI，12%-40%）中是正确的。在 42 例中，正确的诊断包含在在线症状检查器的前 3 个诊断中（38%；95%CI，25%-56%）。在 42 例中，正确的诊断未包含在症状检查器的列表中（43%；95%CI，32%-63%）。基于顶级诊断的分诊紧急程度在 18 例（39%；95%CI，14%-64%）紧急病例中是适当的，在 24 例（88%；95%CI，73%-100%）非紧急病例中也是适当的。正确诊断在列出的前 3 名中的一致性至少为中等（Cohen κ=0.74；95%CI，0.54-0.95）。
最受欢迎的在线症状检查器可能会得出眼科疾病的正确临床诊断，但很大一部分诊断可能无法捕捉到。这些发现表明，需要进一步研究以反映互联网诊断资源的实际应用。
