1 The University of Sydney, Australia.
2 University of Wollongong, Australia.
Health Inf Manag. 2019 Jan;48(1):3-11. doi: 10.1177/1833358318798123. Epub 2018 Oct 3.
: Electronic medical records are increasingly used for research with limited external validation of their data.
: This study investigates the validity of electronic medical data (EMD) for estimating diabetes prevalence in general practitioner (GP) patients by comparing EMD with national Bettering the Evaluation and Care of Health (BEACH) data.
: A "decision tree" was created using inclusion/exclusion of pre-agreed variables to determine the probability of diabetes in absence of diagnostic label, including diagnoses (coded/free-text diabetes, polycystic ovarian syndrome, impaired glucose tolerance, impaired fasting glucose), diabetic annual cycle of care (DACC), glycated haemoglobin (HbA1c) > 6.5%, and prescription (metformin, other diabetes medications). Via SQL query, cases were identified in EMD of five Illawarra and Southern Practice Network practices (30,007 active patients; from 2 years to January 2015). Patient-based Supplementary Analysis of Nominated Data (SAND) sub-studies from BEACH investigating diabetes prevalence (1172 GPs; 35,162 patients; November 2012 to February 2015) were comparison data. SAND results were adjusted for number of GP encounters per year, per patient, and then age-sex standardised to match age-sex distribution of EMD patients. Cluster-adjusted 95% confidence intervals (CIs) were calculated for both datasets.
: EMD diabetes prevalence (T1 and/or T2) was 6.5% (95% CI: 4.1-8.9). Following age-sex standardisation, SAND prevalence, not significantly different, was 6.7% (95% CI: 6.3-7.1). Extracting only coded diagnosis missed 13.0% of probable cases, subsequently identified through the presence of metformin/other diabetes medications (without other indicator variables) (6.1%), free-text diabetes label (3.8%), HbA1c result (1.6%), DACC* (1.3%), and diabetes medications* (0.2%).
: While complex, proxy variables can improve usefulness of EMD for research. Without their consideration, EMD results should be interpreted with caution.
: Enforceable, transparent data linkages in EMRs would resolve many problems with identification of diagnoses. Ongoing data quality improvement remains essential.
电子病历越来越多地用于研究，但对其数据的外部验证有限。
本研究通过比较电子病历（EMD）与全国改善医疗保健评估（BEACH）数据，调查 EMD 用于估计全科医生（GP）患者糖尿病患病率的有效性。
创建了一个“决策树”，通过纳入/排除预先商定的变量来确定诊断标签缺失时的糖尿病概率，包括诊断（编码/自由文本糖尿病、多囊卵巢综合征、糖耐量受损、空腹血糖受损）、糖尿病年度护理周期（DACC）、糖化血红蛋白（HbA1c）>6.5%和处方（二甲双胍、其他糖尿病药物）。通过 SQL 查询，在 Illawarra 和 Southern Practice Network 五家诊所的 EMD 中确定了病例（30007 名活跃患者；从 2 年到 2015 年 1 月）。来自 BEACH 的调查糖尿病患病率的患者基础提名数据（SAND）子研究是比较数据（1172 名全科医生；35162 名患者；2012 年 11 月至 2015 年 2 月）。调整 SAND 结果以匹配 EMD 患者的年 GP 就诊次数、每位患者的就诊次数，然后按年龄和性别标准化。为两个数据集计算了经过聚类调整的 95%置信区间（CI）。
EMD 糖尿病患病率（T1 和/或 T2）为 6.5%（95%CI：4.1-8.9）。经过年龄和性别标准化后，SAND 患病率没有显著差异，为 6.7%（95%CI：6.3-7.1）。仅提取编码诊断会遗漏 13.0%的可能病例，随后通过存在二甲双胍/其他糖尿病药物（无其他指示变量）（6.1%）、自由文本糖尿病标签（3.8%）、HbA1c 结果（1.6%）、DACC*（1.3%）和糖尿病药物*（0.2%）来识别。
虽然复杂，但代理变量可以提高 EMD 在研究中的实用性。如果不考虑这些因素，应该谨慎解释 EMD 结果。
在 EMR 中实施可执行、透明的数据链接将解决诊断识别的许多问题。持续的数据质量改进仍然至关重要。
