Institute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public Health, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada.
Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, G1 06, Toronto, ON, M4N 3M5, Canada.
NPJ Prim Care Respir Med. 2017 May 15;27(1):34. doi: 10.1038/s41533-017-0035-9.
Little is known about using electronic medical records to identify patients with chronic obstructive pulmonary disease to improve quality of care. Our objective was to develop electronic medical record algorithms that can accurately identify patients with obstructive pulmonary disease. A retrospective chart abstraction study was conducted on data from the Electronic Medical Record Administrative data Linked Database (EMRALD) housed at the Institute for Clinical Evaluative Sciences. Abstracted charts provided the reference standard based on available physician-diagnoses, chronic obstructive pulmonary disease-specific medications, smoking history and pulmonary function testing. Chronic obstructive pulmonary disease electronic medical record algorithms using combinations of terminology in the cumulative patient profile (CPP; problem list/past medical history), physician billing codes (chronic bronchitis/emphysema/other chronic obstructive pulmonary disease), and prescriptions, were tested against the reference standard. Sensitivity, specificity, and positive/negative predictive values (PPV/NPV) were calculated. There were 364 patients with chronic obstructive pulmonary disease identified in a 5889 randomly sampled cohort aged ≥ 35 years (prevalence = 6.2%). The electronic medical record algorithm consisting of ≥ 3 physician billing codes for chronic obstructive pulmonary disease per year; documentation in the CPP; tiotropium prescription; or ipratropium (or its formulations) prescription and a chronic obstructive pulmonary disease billing code had sensitivity of 76.9% (95% CI:72.2-81.2), specificity of 99.7% (99.5-99.8), PPV of 93.6% (90.3-96.1), and NPV of 98.5% (98.1-98.8). Electronic medical record algorithms can accurately identify patients with chronic obstructive pulmonary disease in primary care records. They can be used to enable further studies in practice patterns and chronic obstructive pulmonary disease management in primary care.
NOVEL ALGORITHM SEARCH TECHNIQUE: Researchers develop an algorithm that can accurately search through electronic health records to find patients with chronic lung disease. Mining population-wide data for information on patients diagnosed and treated with chronic obstructive pulmonary disease (COPD) in primary care could help inform future healthcare and spending practices. Theresa Lee at the University of Toronto, Canada, and colleagues used an algorithm to search electronic medical records and identify patients with COPD from doctors' notes, prescriptions and symptom histories. They carefully adjusted the algorithm to improve sensitivity and predictive value by adding details such as specific medications, physician codes related to COPD, and different combinations of terminology in doctors' notes. The team accurately identified 364 patients with COPD in a randomly-selected cohort of 5889 people. Their results suggest opportunities for broader, informative studies of COPD in wider populations.
利用电子病历识别慢性阻塞性肺疾病患者以改善护理质量的相关研究甚少。本研究旨在开发可准确识别慢性阻塞性肺疾病患者的电子病历算法。在安大略省临床评估科学研究所的电子病历行政数据链接数据库(EMRALD)中进行了回顾性图表提取研究。提取的图表基于现有医生诊断、慢性阻塞性肺疾病特异性药物、吸烟史和肺功能检查提供参考标准。利用累计患者档案(CPP;问题清单/既往病史)中的术语组合、医生计费代码(慢性支气管炎/肺气肿/其他慢性阻塞性肺疾病)和处方,对慢性阻塞性肺疾病电子病历算法进行测试,并与参考标准进行对比。计算了敏感性、特异性、阳性/阴性预测值(PPV/NPV)。在 5889 名随机抽样的年龄≥35 岁的队列中,有 364 名慢性阻塞性肺疾病患者(患病率为 6.2%)。每年至少有 3 个慢性阻塞性肺疾病医生计费代码的电子病历算法;在 CPP 中有记录;噻托溴铵处方;或异丙托溴铵(或其制剂)处方和慢性阻塞性肺疾病计费代码,其敏感性为 76.9%(95%CI:72.2-81.2),特异性为 99.7%(99.5-99.8),阳性预测值为 93.6%(90.3-96.1),阴性预测值为 98.5%(98.1-98.8)。电子病历算法可准确识别初级保健记录中的慢性阻塞性肺疾病患者。它们可用于在初级保健中开展有关慢性阻塞性肺疾病管理和实践模式的进一步研究。
新型算法搜索技术:研究人员开发了一种算法,可在电子健康记录中准确搜索以查找患有慢性肺部疾病的患者。挖掘全民数据中有关在初级保健中诊断和治疗慢性阻塞性肺疾病(COPD)的患者的信息,可以为未来的医疗保健和支出实践提供信息。加拿大多伦多大学的 Theresa Lee 及其同事使用算法从医生的笔记、处方和症状史中搜索电子病历以识别 COPD 患者。他们通过添加详细信息(如特定药物、与 COPD 相关的医生代码和医生笔记中的不同术语组合)仔细调整算法以提高敏感性和预测值。该团队在 5889 名随机选择的人群中准确识别了 364 名 COPD 患者。他们的研究结果为在更广泛的人群中进行更广泛、更有意义的 COPD 研究提供了机会。