Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina 29425, USA.
Department of Medicine, Medical University of South Carolina, Charleston, South Carolina 29425, USA.
J Am Med Inform Assoc. 2023 Jan 18;30(2):213-221. doi: 10.1093/jamia/ocac157.
Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results.
Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models.
E-phenotype validation rates varied according to experts' domains and query characteristics (mean = 61%, range 20-100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate.
Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users' confidence, but rather on creating highly specific e-phenotypes.
非信息学家调查人员进行电子（e）表型指定仍然是一个挑战。尽管对每个电子表型返回的患者进行验证可以确保队列代表性的准确性，但这种方法并不实际。了解导致成功电子表型指定的因素可能会揭示出可推广的策略，从而获得更好的结果。
招募了 21 名非信息学专家，使用 i2b2 通过诚实的数据经纪人（honest data-broker）和项目协调员来生成专家介导的电子表型。重新识别患者和就诊集，并向专家返回每个电子表型匹配的随机样本 20 份图表进行图表验证。捕获查询和专家特征的属性，并使用广义线性回归模型将其与图表验证率相关联。
电子表型验证率根据专家的领域和查询特征而有所不同（平均为 61%，范围为 20-100%）。表现较好的临床领域包括感染、风湿、新生儿和癌症，而其他领域表现较差（精神科、GI、皮肤和肺部）。当需要指定时间限制时，匹配率会受到负面影响。一般来说，电子表型特异性的增加对匹配率有积极影响。
临床专家和信息学家在构建电子表型时会遇到各种挑战，包括无法区分临床事件和患者特征，或者无法正确配置时间限制；缺乏对可用和高质量数据的访问；以及难以指定药物给药途径。信息学家和诚实的数据经纪人在设计电子表型时进行生物医学查询调解是非常重要的。尽管 i2b2 等工具可能对非信息学家广泛可用，但成功利用这些工具不仅取决于用户的信心，还取决于创建高度特定的电子表型。
