Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network
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DOI：
10.1016/j.jbi.2019.103293
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发表时间：
2019-11-01
影响因子：
4.5
通讯作者：
Weng, Chunhua
中科院分区：
文献类型：
--
作者：
Shang, Ning;Liu, Cong;Weng, Chunhua
背景资料：表型算法的实现需要表型工程师解释人类可读的算法，并将描述（文本和流程图）转换为可计算的表型-这是一个劳动密集型和容易出错的过程。为了解决减少实施工作的迫切需要，重要的是要开发便携式algorithm.Methods：我们进行了一个回顾性分析，在电子病历和基因组学（eMERGE）网络中开发的表型算法，并确定了常见的定制任务，需要实施。提出了一种新的评分系统，从知识转换、子句解释和编程（KIP）三个方面对可移植性进行量化。任务被分为20个具有代表性的类别。有经验的表型工程师被要求估计每个类别上花费的平均时间，并评估节省时间启用一个共同的数据模型（CDM），特别是观察性医疗成果合作伙伴关系（OMOP）模型，为每个category.Results：共485个不同的条款（表型标准）确定从55表型算法，对应于1153定制任务。除了25个非表型特异性任务外，46个任务与解释有关，613个任务与知识转换有关，469个任务与编程有关。每个方面的得分在0到2之间（0表示容易，1表示中等，2表示难以携带），得到的总KIP得分范围为0到6。反映可移植性的条款KIP平均得分为1.37 +/- 1.38。具体而言，平均知识（K）得分为0.64 +/- 0.66，口译（I）得分为0.33 +/- 0.55，编程（P）得分为0.40 +/- 0.64。5%的类别可以在1小时内完成（中位数）。70%的类别需要几天到几个月才能完成。OMOP模型可以协助词汇mappingtasks.Conclusion：本研究提出了第一手资料的大量实施工作，在表型，并介绍了一种新的度量（KIP）来衡量的表型算法的可移植性，量化这种努力在整个eMERGE网络。鼓励表型开发人员在知识，解释和编程方面分析和优化可移植性。CDM可以用来提高一些“面向知识”的任务的可移植性。
Background: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.Methods: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.Results: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 +/- 1.38. Specifically, the average knowledge (K) score is 0.64 +/- 0.66, interpretation (I) score is 0.33 +/- 0.55, and programming (P) score is 0.40 +/- 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.Conclusion: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.