Department of Emergency Medicine, Lehigh Valley Health Network, Allentown, PA, USA.
J Gen Intern Med. 2012 Feb;27(2):213-9. doi: 10.1007/s11606-011-1804-8.
Differential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.
We identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the New England Journal of Medicine (NEJM©) and the Medical Knowledge Self Assessment Program (MKSAP©).
We first identified evaluation criteria by consensus. Then we performed Google® and Pubmed searches to identify DDX generators. To be included, DDX generators had to do the following: generate a list of potential diagnoses rather than text or article references; rank or indicate critical diagnoses that need to be considered or eliminated; accept at least two signs, symptoms or disease characteristics; provide the ability to compare the clinical presentations of diagnoses; and provide diagnoses in general medicine. The evaluation criteria were then applied to the included DDX generators. Lastly, the performance of the DDX generators was tested with findings from 20 test cases. Each case performance was scored one through five, with a score of five indicating presence of the exact diagnosis. Mean scores and confidence intervals were calculated.
Twenty three programs were initially identified and four met the inclusion criteria. These four programs were evaluated using the consensus criteria, which included the following: input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The mean scores (95% Confidence Interval) from performance testing on a five-point scale were Isabel© 3.45 (2.53, 4.37), DxPlain® 3.45 (2.63-4.27), Diagnosis Pro® 2.65 (1.75-3.55) and PEPID™ 1.70 (0.71-2.69). The number of exact matches paralleled the mean score finding.
Consensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators.
鉴别诊断(DDX)生成器是一种根据各种临床数据生成 DDX 的计算机程序。
我们通过共识确定了评估标准,应用这些标准描述 DDX 生成器的特征,并使用《新英格兰医学杂志》(NEJM©)和《医学知识自我评估计划》(MKSAP©)中的病例进行了性能测试。
我们首先通过共识确定评估标准。然后,我们进行了 Google®和 Pubmed 搜索,以确定 DDX 生成器。要包括在内,DDX 生成器必须满足以下条件:生成潜在诊断列表,而不是文本或文章参考;对需要考虑或排除的关键诊断进行排名或指示;接受至少两个体征、症状或疾病特征;提供比较诊断临床表现的能力;并提供一般医学诊断。然后将评估标准应用于所包括的 DDX 生成器。最后,使用 20 个测试案例的结果测试了 DDX 生成器的性能。每个案例的表现都被评为一到五分,五分表示存在确切的诊断。计算了平均分数和置信区间。
最初确定了 23 个程序,其中 4 个符合纳入标准。使用共识标准对这四个程序进行了评估,这些标准包括:输入方法;移动访问;过滤和细化;实验室值、药物和地理位置作为诊断因素;循证医学(EBM)内容;参考文献;以及药物信息内容来源。在五分制上进行性能测试的平均分数(95%置信区间)为 Isabel©3.45(2.53,4.37)、DxPlain®3.45(2.63-4.27)、Diagnosis Pro®2.65(1.75-3.55)和 PEPID™1.70(0.71-2.69)。准确匹配的数量与平均分数相符。
制定了 DDX 生成器评估的共识标准。应用这些标准以及性能测试支持在其他当前可用的 DDX 生成器中使用 DxPlain®和 Isabel©。