Campbell J R, Carpenter P, Sneiderman C, Cohn S, Chute C G, Warren J
Department of Internal Medicine, University of Nebraska, Omaha 68198-3331, USA.
J Am Med Inform Assoc. 1997 May-Jun;4(3):238-51. doi: 10.1136/jamia.1997.0040238.
To compare three potential sources of controlled clinical terminology (READ codes version 3.1, SNOMED International, and Unified Medical Language System (UMLS) version 1.6) relative to attributes of completeness, clinical taxonomy, administrative mapping, term definitions and clarity (duplicate coding rate).
The authors assembled 1929 source concept records from a variety of clinical information taken from four medical centers across the United States. The source data included medical as well as ample nursing terminology. The source records were coded in each scheme by an investigator and checked by the coding scheme owner. The codings were then scored by an independent panel of clinicians for acceptability. Codes were checked for definitions provided with the scheme. Codes for a random sample of source records were analyzed by an investigator for "parent" and "child" codes within the scheme. Parent and child pairs were scored by an independent panel of medical informatics specialists for clinical acceptability. Administrative and billing code mapping from the published scheme were reviewed for all coded records and analyzed by independent reviewers for accuracy. The investigator for each scheme exhaustively searched a sample of coded records for duplications.
SNOMED was judged to be significantly more complete in coding the source material than the other schemes (SNOMED* 70%; READ 57%; UMLS 50%; p < .00001). SNOMED also had a richer clinical taxonomy judged by the number of acceptable first-degree relatives per coded concept (SNOMED 4.56, UMLS 3.17; READ 2.14, p < .005). Only the UMLS provided any definitions; these were found for 49% of records which had a coding assignment. READ and UMLS had better administrative mappings (composite score: READ 40.6%; UMLS* 36.1%; SNOMED 20.7%, p < .00001), and SNOMED had substantially more duplications of coding assignments (duplication rate: READ 0%; UMLS 4.2%; SNOMED 13.9%, *p < .004) associated with a loss of clarity.
No major terminology source can lay claim to being the ideal resource for a computer-based patient record. However, based upon this analysis of releases for April 1995, SNOMED International is considerably more complete, has a compositional nature and a richer taxonomy. Is suffers from less clarity, resulting from a lack of syntax and evolutionary changes in its coding scheme. READ has greater clarity and better mapping to administrative schemes (ICD-10 and OPCS-4), is rapidly changing and is less complete. UMLS is a rich lexical resource, with mappings to many source vocabularies. It provides definitions for many of its terms. However, due to the varying granularities and purposes of its source schemes, it has limitations for representation of clinical concepts within a computer-based patient record.
比较三种潜在的受控临床术语来源（READ编码版本3.1、国际疾病分类标准（SNOMED）和统一医学语言系统（UMLS）版本1.6）在完整性、临床分类、管理映射、术语定义和清晰度（重复编码率）等方面的属性。
作者从美国四个医疗中心的各种临床信息中收集了1929条源概念记录。源数据包括医学术语以及丰富的护理术语。源记录由一名研究人员按照每种方案进行编码，并由编码方案所有者进行检查。然后由一个独立的临床医生小组对编码进行可接受性评分。检查编码方案中提供的定义。一名研究人员对源记录的随机样本中的编码进行分析，以确定方案中的“父”编码和“子”编码。父编码和子编码对由一个独立的医学信息学专家小组进行临床可接受性评分。对所有编码记录审查已发布方案中的管理和计费代码映射，并由独立评审人员分析其准确性。每种方案的研究人员详尽地搜索编码记录样本以查找重复项。
在对源材料进行编码方面，国际疾病分类标准（SNOMED）被判定比其他方案明显更完整（SNOMED* 70%；READ 57%；UMLS 50%；p <.00001）。根据每个编码概念的可接受一级亲属数量判断，国际疾病分类标准（SNOMED）的临床分类也更丰富（SNOMED 4.56，UMLS 3.17；READ 2.14，p <.005）。只有统一医学语言系统（UMLS）提供了一些定义；在有编码分配的记录中，49%的记录找到了定义。READ和UMLS有更好的管理映射（综合评分：READ 40.6%；UMLS* 36.1%；SNOMED 20.7%，p <.00001），并且国际疾病分类标准（SNOMED）的编码分配重复项明显更多（重复率：READ 0%；UMLS 4.2%；SNOMED 13.9%，*p <.004），这导致清晰度下降了。
没有一种主要的术语来源可以声称是基于计算机的患者记录的理想资源。然而，根据对1995年4月版本的分析，国际疾病分类标准（SNOMED）国际版要完整得多，具有组合性质和更丰富的分类。它因缺乏语法和编码方案的演变性变化而清晰度较低。READ有更高的清晰度，并且与管理方案（ICD - 10和OPCS - 4）的映射更好，正在迅速变化且不太完整。统一医学语言系统（UMLS）是一种丰富的词汇资源，与许多源词汇表有映射。它为许多术语提供了定义。然而，由于其源方案的粒度和目的各不相同，它在基于计算机的患者记录中表示临床概念方面存在局限性。
