Division of Endocrinology & Metabolism, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Office of Lifelong Learning & the Physician Learning Program, Faculty of Medicine and Dentistry, University of Alberta, AB, Edmonton, Canada.
BMC Health Serv Res. 2023 Jan 2;23(1):1. doi: 10.1186/s12913-022-08882-7.
Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure.
We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described.
The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases.
Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged.
电子病历和行政数据的关联具有支持学习型医疗体系和数据驱动的质量改进的潜力。然而，在应用之前，必须首先评估数据的完整性和准确性。我们评估了在艾伯塔省埃德蒙顿的五家专科糖尿病诊所中，将电子病历和行政数据关联起来用于质量改进的过程、可行性和局限性，该省以其强大的健康数据基础设施而闻名。
我们对 2017 年 3 月至 2018 年 12 月期间在诊所就诊的年龄≥18 岁的个体进行了回顾性横断面分析，使用电子病历和行政数据。针对人口统计学、服务使用、糖尿病类型和标准糖尿病基准，生成了描述性统计数据。描述了获得结果的系统和迭代过程。
发现将电子病历与行政数据整合用于质量改进的过程是非线性和迭代的，涉及四个阶段：项目规划、信息生成、局限性分析和行动。在局限性分析之后，将问题分为有信心回答、有局限性回答和无法用现有数据回答的问题。导致数据局限性的因素包括不准确的数据输入、编码、整理、迁移和综合、实验室报告的变化以及现有数据库中未捕获的信息。
电子病历和行政数据库可以成为建立临床实践模式、为区域层面的数据驱动质量改进提供信息以及支持学习型医疗体系的有力工具。然而，在这些资源能够被可靠地利用之前，必须解决大量的数据局限性问题。
