Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care
Prevalence and characteristics in coding, classification and diagnosis of diabetes in primary care
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DOI：
10.1136/postgradmedj-2013-132068
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发表时间：
2014-01-01
影响因子：
5.1
通讯作者：
Khunti, Kamlesh
中科院分区：
文献类型：
--
作者：
Seidu, Samuel;Davies, Melanie J.;Khunti, Kamlesh
全世界约有3.66亿人患有糖尿病，预计这一数字还会上升。在正确的诊断，将有错误的诊断，分类和编码，造成不良的健康和财政implications.Aim要确定的患病率和特点的诊断错误的糖尿病患者在初级保健settings.Methods管理，我们进行了一项横断面研究，在9个一般的做法在英国莱斯特，从2011年5月至8月，使用一个经过验证的电子工具包。结果共有54088例患者，其中2434例（4.5%）诊断为糖尿病。在316名使用工具包识别出潜在错误的人中，有180人（57%）在手动审查记录后确认了错误，导致错误发生率为7.4%。登记册上正确编码的人糖化血红蛋白（HbA 1c）降低幅度更大。有和没有错误的患者之间没有显着差异，他们的HbA 1C，体重指数，年龄和实践规模。这些错误与按绩效付费计划也没有显着关联;然而，那些未在疾病登记册上的患者的血糖控制较差。结论使用药物、生化和人口统计数据证实了糖尿病诊断错误的高患病率。需要更大规模的研究来更准确地评估这一问题的规模。这些过程的自动化是可能的，这将使搜索更加方便用户。
Introduction Approximately 366 million people worldwide live with diabetes and this figureis expected to rise. Among the correct diagnosis, there will be errors in the diagnosis, classification and coding, resulting in adverse health and financial implications.Aim To determine the prevalence and characteristics of diagnostic errors in people with diabetes managed in primary care settings.Methods We conducted a cross-sectional study in nine general practices in Leicester, UK, from May to August 2011, using a validated electronic toolkit. Searches identified cases with potential errors which were manually checked for accuracy.Results There were 54088 patients and 2434 (4.5%) diagnosed with diabetes. Out of 316 people identified with potential errors with the toolkit, 180 (57%) had confirmed errors after manually reviewing the records, resulting in an error prevalence of 7.4%. Correctly coded people on registers had significantly greater glycated haemoglobin (HbA1c) reductions. There were no significant differences between patients with and without errors in their HbA1C, body mass index, age and size of practice. There was also no significant association of the errors with pay-for-performance initiatives; however, those patients not on disease register had worse glycaemic control.Conclusions A high prevalence of diabetic diagnostic errors was confirmed using medication, biochemical and demographic data. Larger studies are needed to more accurately assess the scale of this problem. Automation of these processes might be possible, which would allow searches to be even more user friendly.