Kamdje Wabo Gaetan, Moorthy Preetha, Siegel Fabian, Seuchter Susanne A, Ganslandt Thomas
Center for Preventive Medicine and Digital Health Baden-Wuerttemberg, Department of Biomedical Informatics, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany.
Department of Urology and Urosurgery, University Medical Center of Mannheim, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany.
JMIR Med Inform. 2024 Aug 19;12:e57153. doi: 10.2196/57153.
Leveraging electronic health record (EHR) data for clinical or research purposes heavily depends on data fitness. However, there is a lack of standardized frameworks to evaluate EHR data suitability, leading to inconsistent quality in data use projects (DUPs). This research focuses on the Medical Informatics for Research and Care in University Medicine (MIRACUM) Data Integration Centers (DICs) and examines empirical practices on assessing and automating the fitness-for-purpose of clinical data in German DIC settings.
The study aims (1) to capture and discuss how MIRACUM DICs evaluate and enhance the fitness-for-purpose of observational health care data and examine the alignment with existing recommendations and (2) to identify the requirements for designing and implementing a computer-assisted solution to evaluate EHR data fitness within MIRACUM DICs.
A qualitative approach was followed using an open-ended survey across DICs of 10 German university hospitals affiliated with MIRACUM. Data were analyzed using thematic analysis following an inductive qualitative method.
All 10 MIRACUM DICs participated, with 17 participants revealing various approaches to assessing data fitness, including the 4-eyes principle and data consistency checks such as cross-system data value comparison. Common practices included a DUP-related feedback loop on data fitness and using self-designed dashboards for monitoring. Most experts had a computer science background and a master's degree, suggesting strong technological proficiency but potentially lacking clinical or statistical expertise. Nine key requirements for a computer-assisted solution were identified, including flexibility, understandability, extendibility, and practicability. Participants used heterogeneous data repositories for evaluating data quality criteria and practical strategies to communicate with research and clinical teams.
The study identifies gaps between current practices in MIRACUM DICs and existing recommendations, offering insights into the complexities of assessing and reporting clinical data fitness. Additionally, a tripartite modular framework for fitness-for-purpose assessment was introduced to streamline the forthcoming implementation. It provides valuable input for developing and integrating an automated solution across multiple locations. This may include statistical comparisons to advanced machine learning algorithms for operationalizing frameworks such as the 3×3 data quality assessment framework. These findings provide foundational evidence for future design and implementation studies to enhance data quality assessments for specific DUPs in observational health care settings.
将电子健康记录（EHR）数据用于临床或研究目的在很大程度上取决于数据的适用性。然而，缺乏评估EHR数据适用性的标准化框架，导致数据使用项目（DUP）的质量参差不齐。本研究聚焦于大学医学研究与护理医学信息学（MIRACUM）数据集成中心（DIC），并考察在德国DIC环境下评估和自动化临床数据适用性的实证做法。
本研究旨在（1）了解并讨论MIRACUM DIC如何评估和提高观察性医疗保健数据的适用性，并检验其与现有建议的一致性；（2）确定在MIRACUM DIC内设计和实施计算机辅助解决方案以评估EHR数据适用性的要求。
采用定性研究方法，对隶属于MIRACUM的10家德国大学医院的DIC进行开放式调查。采用归纳定性方法，通过主题分析对数据进行分析。
所有10个MIRACUM DIC均参与，17名参与者揭示了评估数据适用性的各种方法，包括四眼原则和数据一致性检查，如跨系统数据值比较。常见做法包括建立与DUP相关的数据适用性反馈循环，以及使用自行设计的仪表板进行监测。大多数专家具有计算机科学背景和硕士学位，表明他们具备较强的技术能力，但可能缺乏临床或统计专业知识。确定了计算机辅助解决方案的九个关键要求，包括灵活性、可理解性、可扩展性和实用性。参与者使用异构数据存储库来评估数据质量标准，并采用实际策略与研究和临床团队进行沟通。
该研究确定了MIRACUM DIC当前做法与现有建议之间的差距，深入了解了评估和报告临床数据适用性的复杂性。此外，引入了一个用于适用性评估的三方模块化框架，以简化即将进行的实施过程。它为跨多个地点开发和集成自动化解决方案提供了有价值的输入。这可能包括进行统计比较以及采用先进的机器学习算法来实施诸如3×3数据质量评估框架等框架。这些发现为未来的设计和实施研究提供了基础证据，以加强观察性医疗保健环境中特定DUP的数据质量评估。
