SUNY Polytechnic Institute, College of Health Sciences, Utica, NY, USA.
BMC Med Inform Decis Mak. 2021 Apr 9;21(1):124. doi: 10.1186/s12911-021-01485-y.
Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015.
This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of adoption challenges.
Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code.
A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons.
The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoption challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.
从旧的医疗编码系统过渡到新的系统可能具有挑战性，特别是当这两个编码系统有很大的不同时。美国在 2015 年就经历了这样的转变。
本研究旨在引入信息熵度量方法，通过识别和关注更有可能面临采用挑战的临床概念，帮助用户为向新的医疗编码系统过渡做好准备。
引入了两种编码复杂度的信息熵度量方法。第一种度量方法是新编码的字母表变化的函数。第二种度量方法基于旧代码的有效表示的可能数量。
使用 2015 年 ICD-9-CM 与 ICD-10-CM/PCS 之间的映射，展示了如何实施所提出的技术。讨论了所得信息熵度量在可能在文档记录、编码错误和纵向数据比较方面带来挑战的临床概念方面的意义。
所提出的信息熵技术适用于评估任何具有映射或交叉引用的两个医疗编码系统之间的复杂性。信息熵越大，采用挑战的可能性就越大。用户可以将建议的技术用作指导，优先进行培训工作，以提高文档记录的质量，并增加准确编码、代码有效性和纵向数据比较的机会。
