Evaluating and Enhancing the Fitness-for-Purpose of Electronic Health Record Data: Qualitative Study on Current Practices and Pathway to an Automated Approach Within the Medical Informatics for Research and Care in University Medicine Consortium

Abstract

BackgroundLeveraging 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. ObjectiveThe 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. MethodsA 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. ResultsAll 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. ConclusionsThe 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.