Edinburgh Clinical Trials Unit (ECTU), The University of Edinburgh, Edinburgh, UK.
Asthma UK Centre for Applied Research, Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK.
Allergy. 2016 Nov;71(11):1594-1602. doi: 10.1111/all.12928. Epub 2016 Jun 3.
The UK's NHS intends to move from the current Read code system to the international, detailed Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) to facilitate more clinically appropriate coding of conditions and associated risk factors and outcomes. Given concerns about coding behaviour of general practitioners, we sought to study the current coding patterns in allergies and identify lessons for the future migration to SNOMED-CT.
Data from 2 014 551 primary care consultations in over 100 000 patients with one or more of 11 potentially allergic diseases (anaphylaxis, angioedema, asthma, conjunctivitis, drug allergies, eczema, food allergy, rhinitis, urticaria, venom allergy and other probable allergic disorders) from the Scottish Primary Care Clinical Informatics Unit Research (PCCIU-R) database were descriptively analysed and visualized to understand Read code usage patterns.
We identified 352 Read codes for these allergic diseases, but only 36 codes (10%) were used in 95% of consultations; 73 codes (21%) were never used. Half of all usage was for Quality and Outcomes Framework codes for asthma. Despite 149 detailed codes (42%) being available for allergic triggers, these were infrequently used.
This analysis of Read codes use suggests that introduction of the more detailed SNOMED-CT, in isolation, will not improve the quality of allergy coding in Scottish primary care. The introduction of SNOMED-CT should be accompanied by initiatives aimed at improving coding quality, such as the definition of terms/codes, the availability of terminology browsers, a recommended list of codes and mechanisms to incentivize detailed coding of the condition and the underlying allergic trigger.
英国国民保健制度（NHS）计划从当前的 Read 代码系统迁移到国际通用的详细医学术语系统分类（SNOMED-CT），以更准确地对疾病和相关风险因素及结果进行编码。鉴于对全科医生编码行为的担忧，我们试图研究过敏症的当前编码模式，并为未来向 SNOMED-CT 的迁移吸取经验教训。
我们对来自苏格兰初级保健临床信息学单位研究（PCCIU-R）数据库的 11 种潜在过敏疾病（过敏反应、血管性水肿、哮喘、结膜炎、药物过敏、湿疹、食物过敏、鼻炎、荨麻疹、毒液过敏和其他可能的过敏疾病）的 100000 多名患者中 2014 551 次初级保健咨询进行了描述性分析和可视化，以了解 Read 代码使用模式。
我们确定了 352 个用于这些过敏疾病的 Read 代码，但只有 36 个代码（10%）在 95%的咨询中使用；73 个代码（21%）从未使用过。所有使用的代码中，有一半是用于哮喘的质量和结果框架代码。尽管有 149 个详细的过敏诱因代码（42%）可用，但这些代码很少被使用。
对 Read 代码使用情况的分析表明，仅引入更详细的 SNOMED-CT 并不会提高苏格兰初级保健中过敏症编码的质量。引入 SNOMED-CT 应辅以旨在提高编码质量的举措，例如术语/代码的定义、术语浏览器的可用性、推荐的代码列表以及激励详细编码疾病和潜在过敏诱因的机制。
