Sivashankaran Srikanth, Borsi John P, Yoho Amanda
IBM Watson Health, Cleveland, OH.
AMIA Annu Symp Proc. 2020 Mar 4;2019:804-811. eCollection 2019.
Usage of ICD-10 codes in administrative data has continued to shift since mandatory adoption in 2015. Identifying changing patterns in coding behavior is imperative in producing reliable analyses and robust conclusions. We examined the granularity of ICD-10 coding over time in a cohort selected from the IBM Explorys Therapeutic Dataset, which contains the records of over 60 million patients. Our seasonality-aware trend model identified patterns of interest, such as increased use of laterality codes for pain and increased use of codes denoting concepts novel to ICD- 10 for screening encounters. Those relying on these codes should adjust for these 'learning curve' effects. This work should be extended to additional modalities of terminology usage and represents a starting point for researchers working with dynamic clinical ontologies.
自2015年强制采用以来，ICD-10编码在行政数据中的使用情况持续变化。识别编码行为的变化模式对于进行可靠的分析和得出有力的结论至关重要。我们在从IBM Explorys治疗数据集选取的队列中，研究了随时间推移ICD-10编码的粒度，该数据集包含超过6000万患者的记录。我们的季节性趋势模型识别出了一些有趣的模式，比如用于疼痛的侧别编码使用增加，以及用于筛查会诊的表示ICD-10新增概念的编码使用增加。依赖这些编码的人应该对这些“学习曲线”效应进行调整。这项工作应扩展到术语使用的其他模式，并且是从事动态临床本体研究的人员的一个起点。
