P. Vinny, M. Padma, P. Sylaja, P. Kesav, V. Lal, L. Narasimhan, S. Dwivedi, P. Nair, T. Iype, Anuragini Gupta, A. Patil, V. Vishnu
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Measurements: Detecting diagnostic accuracy of residents and App measured as a proportion of correctly identified high likely gold standard DDx was prespecified as the main outcome. Proportions of correctly identified first high likely, first 3 high likely, first 5 high likely, and combined moderate plus high likely gold standard differentials by residents and App were secondary outcomes. Results: 1,000 vignettes were attempted by residents. Frequency of gold standard, high likely differentials correctly identified by residents was 27% compared to 72% by App (absolute difference 45%, 95% CI 35.7-52.8). When high and moderate likely differentials were combined, residents scored 17% compared to 57% by App (absolute difference 40%, 95% CI 33.8-50.0). Residents correctly identified first high likely gold standard differential as their first high likely differential in 34% compared to 18% by App (absolute difference 16%, 95% CI 1.2-25.4). Conclusion: App with predefined knowledge base can complement clinical reasoning of neurology residents. Portability and functionality of such Apps may further strengthen this symbiosis between humans and algorithms (CTRI/2017/06/008838).","PeriodicalId":93323,"journal":{"name":"Journal of stroke medicine","volume":"7 1","pages":"21 - 27"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of Diagnostic Accuracy in Vascular Neurology Between Neurology Residents and a Neurology Differential Diagnosis App: A Multi-Center Cross-Sectional Observational Study\",\"authors\":\"P. Vinny, M. Padma, P. Sylaja, P. Kesav, V. Lal, L. Narasimhan, S. Dwivedi, P. Nair, T. Iype, Anuragini Gupta, A. Patil, V. Vishnu\",\"doi\":\"10.1177/2516608520901388\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Background: Diagnostic errors in neurological diagnosis are a source of preventable harm. Software tools like Differential Diagnosis (DDx) apps in neurology that hold the potential to mitigate this harm are conspicuously lacking. Materials and Methods: A multicenter cross-sectional observational study was designed to compare the diagnostic accuracy of a Neurology DDx App (Neurology Dx) with neurology residents by using vascular neurology clinical vignettes. The study was conducted at 7 leading neurology institutes in India. Study participants comprised of 100 neurology residents from the participating institutes. Measurements: Detecting diagnostic accuracy of residents and App measured as a proportion of correctly identified high likely gold standard DDx was prespecified as the main outcome. Proportions of correctly identified first high likely, first 3 high likely, first 5 high likely, and combined moderate plus high likely gold standard differentials by residents and App were secondary outcomes. Results: 1,000 vignettes were attempted by residents. Frequency of gold standard, high likely differentials correctly identified by residents was 27% compared to 72% by App (absolute difference 45%, 95% CI 35.7-52.8). When high and moderate likely differentials were combined, residents scored 17% compared to 57% by App (absolute difference 40%, 95% CI 33.8-50.0). Residents correctly identified first high likely gold standard differential as their first high likely differential in 34% compared to 18% by App (absolute difference 16%, 95% CI 1.2-25.4). Conclusion: App with predefined knowledge base can complement clinical reasoning of neurology residents. 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引用次数: 0
摘要
背景:神经学诊断中的诊断错误是可预防伤害的一个来源。像神经病学的鉴别诊断(DDx)应用程序这样的软件工具显然缺乏减轻这种伤害的潜力。材料和方法:设计了一项多中心横断面观察性研究,通过血管神经学临床影像,比较神经内科DDx应用程序(Neurology Dx)与神经内科住院医师的诊断准确性。这项研究是在印度7家领先的神经病学研究所进行的。研究参与者包括来自参与机构的100名神经内科住院医师。测量:检测居民和App的诊断准确性,以正确识别的高可能金标准DDx的比例进行测量,预先指定为主要结果。居民和App正确识别第一个高可能性、前3个高可能性、前5个高可能性以及中等加高可能性金标准差异的比例是次要结果。结果:居民尝试了1000个小短片。居民正确识别金标准,高可能差异的频率为27%,而App为72%(绝对差为45%,95% CI 35.7-52.8)。当高度和中度可能差异合并时,居民得分为17%,而App得分为57%(绝对差为40%,95% CI 33.8-50.0)。居民正确识别第一个高可能金标准差异为其第一个高可能差异的比例为34%,而App为18%(绝对差值为16%,95% CI 1.2-25.4)。结论:具有预定义知识库的App可以补充神经内科住院医师的临床推理。这些应用程序的可移植性和功能性可能会进一步加强人类与算法之间的这种共生关系(CTRI/2017/06/008838)。
Comparison of Diagnostic Accuracy in Vascular Neurology Between Neurology Residents and a Neurology Differential Diagnosis App: A Multi-Center Cross-Sectional Observational Study
Abstract Background: Diagnostic errors in neurological diagnosis are a source of preventable harm. Software tools like Differential Diagnosis (DDx) apps in neurology that hold the potential to mitigate this harm are conspicuously lacking. Materials and Methods: A multicenter cross-sectional observational study was designed to compare the diagnostic accuracy of a Neurology DDx App (Neurology Dx) with neurology residents by using vascular neurology clinical vignettes. The study was conducted at 7 leading neurology institutes in India. Study participants comprised of 100 neurology residents from the participating institutes. Measurements: Detecting diagnostic accuracy of residents and App measured as a proportion of correctly identified high likely gold standard DDx was prespecified as the main outcome. Proportions of correctly identified first high likely, first 3 high likely, first 5 high likely, and combined moderate plus high likely gold standard differentials by residents and App were secondary outcomes. Results: 1,000 vignettes were attempted by residents. Frequency of gold standard, high likely differentials correctly identified by residents was 27% compared to 72% by App (absolute difference 45%, 95% CI 35.7-52.8). When high and moderate likely differentials were combined, residents scored 17% compared to 57% by App (absolute difference 40%, 95% CI 33.8-50.0). Residents correctly identified first high likely gold standard differential as their first high likely differential in 34% compared to 18% by App (absolute difference 16%, 95% CI 1.2-25.4). Conclusion: App with predefined knowledge base can complement clinical reasoning of neurology residents. Portability and functionality of such Apps may further strengthen this symbiosis between humans and algorithms (CTRI/2017/06/008838).