ArticleDigital health
The open sharing operation mechanism of health data in the digital healthcare era: A study based on grounded theory and interpretative structural modeling method.
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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Objective: In the evolving digital healthcare landscape, the strategic exploitation of open health data has become increasingly pivotal. The open sharing of health data is essential due to its irreplaceable role in digital health care. It is made feasible by the development of emerging technologies and the support of national policies. However, in practice, health data systems continue to face challenges such as data fragmentation, uneven data quality, poor platform connectivity, and a data regulatory vacuum, all of which hinder the effective integration of healthcare resources. This study proposes a health data governance framework to align stakeholder incentives, clarify operational responsibilities, and facilitate the compliant sharing and reuse of health data. Methods: Innovatively employing grounded theory, this study meticulously examines the critical elements that influence health data sharing and provides a comprehensive explanation of its operational mechanisms through the Interpretive Structural Modeling - Cross-Impact Matrix Multiplication Applied to Classification model, delves into the operational mechanisms from three perspectives: the interest-driven and coordinated actions of multiple stakeholders, the open flow and secure sharing of the data ontology, and the internal support and external guarantees provided by the environmental context. Results: This study proposes a structured data governance framework, establishing a health data sharing system built upon four core components: top-level guidance, collaborative governance, technological empowerment, and rights protection. By standardizing data quality assessment, enhancing platform interoperability, unifying data exchange protocols, and addressing critical regulatory gaps, the framework offers a systematic solution to the key challenges associated with open health data sharing. Conclusion: This study advocates an open health data-sharing framework characterized by "top-level guidance, collaborative governance, technological empowerment, and robust rights protection." Such a framework will optimize the economic and social value of open health data, address existing challenges, and harness future opportunities to create a more inclusive and efficient healthcare system.
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