Müller T, Jerrentrup A, Schäfer J R
Zentrum für unerkannte und seltene Erkrankungen (ZusE), Universitätsklinikum Gießen und Marburg (UKGM), Baldingerstr. 1, 35043, Marburg, Deutschland.
Internist (Berl). 2018 Apr;59(4):391-400. doi: 10.1007/s00108-017-0218-z.
To establish a comprehensive diagnosis is by far the most challenging task in a physician's daily routine. Especially rare diseases place high demands on differential diagnosis, caused by the high number of around 8000 diseases and their clinical variability. No clinician can be aware of all the different entities and memorizing them all is impossible and inefficient. Specific diagnostic decision-supported systems provide better results than standard search engines in this context. The systems FindZebra, Phenomizer, Orphanet, and Isabel are presented here concisely with their advantages and limitations. An outlook is given to social media usage and big data technologies. Due to the high number of initial misdiagnoses and long periods of time until a confirmatory diagnosis is reached, these tools might be promising in practice to improve the diagnosis of rare diseases.
做出全面的诊断是医生日常工作中迄今为止最具挑战性的任务。尤其是罕见病对鉴别诊断要求很高,这是由大约8000种疾病及其临床变异性导致的。没有临床医生能够知晓所有不同的病种,将它们全部记住既不可能也效率低下。在这种情况下,特定的诊断决策支持系统比标准搜索引擎能提供更好的结果。本文简要介绍了FindZebra、Phenomizer、Orphanet和Isabel这几个系统及其优缺点。还展望了社交媒体的应用和大数据技术。由于初始误诊数量众多且确诊前需要很长时间,这些工具在实践中可能有助于改善罕见病的诊断。