A rapid review on the application of common data models in healthcare: Recommendations for data governance and federated learning in artificial intelligence development

Abstract

<h3>Objective</h3><p>This rapid review was undertaken to summarize contemporary knowledge on the application of common data models (CDMs) for semantic data standardization in the field of healthcare and provide a set of recommendations to guide the development of a CDM.</p><h3>Methods</h3><p>The review adapted the Cochrane methodological recommendations for rapid reviews, namely (1) topic refinement, (2) setting eligibility criteria, (3) searching, (4) study selection, (5) data extraction, and (6) synthesis.</p><h3>Results</h3><p>A total of 69 studies were included in the analysis. The analysis resulted in three interconnected layers covering (1) the federated network, (2) the iterative application process of a CDM, and (3) the data management process of each partner.</p><h3>Conclusion</h3><p>Development and implementation of CDMs is a collaborative and iterative process, highly affected by the boundaries set by the individual federated learning partners, and the nature of their data. Interdisciplinary collaboration in application of CDMs for federated learning and data governance of health data is mandatory, with a call to increase domain expert involvement in data management.</p>