ArticleDigital health
Development and application of a healthcare data quality indicator framework from the perspective of data-as-a-product.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Objective: Data-as-a-product (DaaP) treats data as a marketable asset by applying product management principles throughout the data life cycle. Despite the high value of healthcare data, poor data quality hinders the effective implementation of DaaP in the healthcare industry. Robust data quality assessment is necessary to ensure that data products meet stakeholder expectations. This study aims to develop a healthcare data quality indicator framework (HDQIF) combined with the perspective of DaaP, explore the interrelationships among various indicators within HDQIF, and apply the HDQIF in assessing healthcare data quality. Methods: A three-staged hybrid method is adopted. A specialized HDQIF is initially constructed through grounded theory and revised by Delphi consultation. Then, the HDQIF is investigated to identify key indicators and disentangle intricate interrelationships among indicators using a Decision-making Trial and Evaluation Laboratory and Adversarial Interpretive Structure Modeling (DEMATEL-AISM) method. Afterward, the HDQIF is applied to quantitatively assessing healthcare data quality using the analytic network process and fuzzy comprehensive evaluation (ANP-FCE) approach, with a case study demonstrating the practical application of the HDQIF. Results: The HDQIF was established with 16 unique indicators that comprehensively captured both established and new aspects of healthcare data quality. The DEMATEL-AISM analysis revealed that a four-quadrant influential relation map categorized 16 indicators to identify crucial ones and a nine-level topological hierarchical structure model hierarchized 16 indicators to disentangle interrelationships among indicators. The application of the ANP-FCE approach validated the framework's capacity to quantitatively assess healthcare data quality, with a case study confirming the practicability of HDQIF. Conclusions: The HDQIF offers a consolidated framework to support fit-for-use understandings of healthcare data quality within the DaaP context. Our findings provide several insights for healthcare organizations to improve healthcare data quality. Future work exploring context-driven adaptations of the HDQIF to facilitate the assessment of various types of healthcare data products is needed.
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