Department of Health Care Management and Policy, Faculty of Business, Economics and Law, University of Surrey, Guildford, UK.
Diabet Med. 2012 Feb;29(2):181-9. doi: 10.1111/j.1464-5491.2011.03419.x.
To determine the effectiveness of self-audit tools designed to detect miscoding, misclassification and misdiagnosis of diabetes in primary care.
We developed six searches to identify people with diabetes with potential classification errors. The search results were automatically ranked from most to least likely to have an underlying problem. Eight practices with a combined population of 72,000 and diabetes prevalence 2.9% (n = 2340) completed audit forms to verify whether additional information within the patients' medical record confirmed or refuted the problems identified.
The searches identified 347 records, mean 42 per practice. Pre-audit 20% (n = 69) had Type 1 diabetes, 70% (n = 241) had Type 2 diabetes, 9% (n = 30) had vague codes that were hard to classify, 2% (n = 6) were not coded and one person was labelled as having gestational diabetes. Of records, 39.2% (n = 136) had important errors: 10% (n = 35) had coding errors; 12.1% (42) were misclassified; and 17.0% (59) misdiagnosed as having diabetes. Thirty-two per cent (n = 22) of people with Type 2 diabetes (n = 69) were misclassified as having Type 1 diabetes; 20% (n = 48) of people with Type 2 diabetes (n = 241) did not have diabetes; of the 30 patients with vague diagnostic terms, 50% had Type 2 diabetes, 20% had Type 1 diabetes and 20% did not have diabetes. Examples of misdiagnosis were found in all practices, misclassification in seven and miscoding in six.
Volunteer practices successfully used these self-audit tools. Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest, misclassification may affect treatment options and miscoding in omission from disease registers and the potential for reduced quality of care.
确定旨在检测初级保健中糖尿病编码错误、分类错误和诊断错误的自我审核工具的有效性。
我们开发了六个搜索来识别可能存在分类错误的糖尿病患者。搜索结果从最有可能存在潜在问题到最不可能存在潜在问题进行自动排序。八家诊所共有 72000 人，糖尿病患病率为 2.9%（n=2340），完成了审核表格，以验证患者病历中的其他信息是否证实或反驳了所识别的问题。
搜索共确定了 347 份记录，平均每个诊所 42 份。审核前，20%（n=69）患有 1 型糖尿病，70%（n=241）患有 2 型糖尿病，9%（n=30）的分类代码模糊，难以分类，2%（n=6）未编码，1 人被标记为患有妊娠糖尿病。在记录中，39.2%（n=136）有重要错误：10%（n=35）有编码错误；12.1%（42）分类错误；17.0%（59）误诊为糖尿病。32%（n=22）的 2 型糖尿病患者（n=69）被误诊为 1 型糖尿病；20%（n=48）的 2 型糖尿病患者（n=241）没有糖尿病；30 名诊断术语模糊的患者中，50%患有 2 型糖尿病，20%患有 1 型糖尿病，20%没有糖尿病。在所有诊所都发现了误诊的例子，在 7 家诊所发现了分类错误，在 6 家诊所发现了编码错误。
志愿诊所成功使用了这些自我审核工具。大约 40%的通过计算机搜索识别的患者（糖尿病患者的 5.8%）存在错误；误诊最常见，分类错误可能影响治疗选择，遗漏疾病登记册中的编码以及潜在的护理质量下降。
