Women's College Hospital Institute for Health Systems Solutions and Virtual Care, Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Can J Diabetes. 2020 Jun;44(4):350-355. doi: 10.1016/j.jcjd.2019.10.011. Epub 2019 Nov 9.
Electronic health records (EHRs) allow standardized data capture for robust quality and performance assessments, but data quality issues may exist. This study compared extracted structured EHR data with chart review from endocrinologists' health-care records at a large, academic ambulatory hospital in Toronto, Ontario.
Consecutive chart review for the first 10 patients with either type 1 or type 2 diabetes seen between January 1, 2015 and March 1, 2016 was sampled for each of the 10 endocrinologists within the diabetes program, and electronic data extraction was also completed for a core set of structured diabetes care elements. Fisher exact test was used to determine differences in data availability between extracted EHR data and chart review.
Out of 100 charts, there was significant under-representation of care elements using EHR extraction compared with chart review for glycated hemoglobin (45% vs 100%; p<0.0001), low-density lipoprotein (36% vs 89%; p<0.0001), urine albumin to creatinine ratio (38% vs 86%; p<0.0001), insulin use (20% vs 72%; p<0.0001), glomerular filtration rate (42% vs 87%, p<0.0001) and use of oral hypoglycemic agent (14% vs 64%; p<0.0001), respectively. EHR data extraction could not be obtained for driving status, hypoglycemia and driving counselling, smoking cessation counselling, family planning counselling and eye examination documentation.
There is poor accuracy of using EHR data extraction during the early adoption phase to understand diabetes care, limiting EHR-based quality assessments.
电子健康记录 (EHR) 允许对稳健的质量和绩效评估进行标准化数据采集，但可能存在数据质量问题。本研究比较了安大略省多伦多市一家大型学术门诊医院的内分泌医生的电子病历中的提取结构化 EHR 数据与病历记录。
对 2015 年 1 月 1 日至 2016 年 3 月 1 日期间就诊的每位内分泌医生的第 1 型或第 2 型糖尿病的前 10 名连续患者进行了病历回顾，并且还对核心组结构化糖尿病护理元素进行了电子数据提取。Fisher 精确检验用于确定提取的 EHR 数据与病历记录之间数据可用性的差异。
在 100 份图表中，与病历记录相比，EHR 提取在糖化血红蛋白（45%对 100%；p<0.0001）、低密度脂蛋白（36%对 89%；p<0.0001）、尿白蛋白与肌酐比值（38%对 86%；p<0.0001）、胰岛素使用（20%对 72%；p<0.0001）、肾小球滤过率（42%对 87%，p<0.0001）和口服降糖药使用（14%对 64%；p<0.0001）方面存在明显的护理元素代表性不足。无法从 EHR 数据提取中获取驾驶状态、低血糖和驾驶咨询、戒烟咨询、计划生育咨询和眼部检查记录。
在早期采用阶段，使用 EHR 数据提取来了解糖尿病护理的准确性较差，限制了基于 EHR 的质量评估。
