Innovation is key for advancing the science of biomedical and health informatics and for publishing in JAMIA

Abstract

Innovation is key for scientific advancement. One of the most common reasons that well-written manuscripts are rejected from Journal of the American Medical Informatics Association (JAMIA) is lack of innovation from the perspective of biomedical and health informatics. Oxford defines innovation (in something) as “a new idea, way of doing something, etc. that has been introduced or discovered.”1 Innovation in biomedical and health informatics can take multiple forms (eg, conceptual, topical, methodological, or application domains) and is relevant across manuscript types. In this editorial, I highlight 5 articles that illustrate different aspects of innovation. A research study by Kuo et al2 reflects innovation in its development of a framework that combines level-wise model learning, blockchain-based model dissemination, and a hierarchical consensus algorithm to construct generalizable predictive models using cross-institutional approaches. The framework is designed to take advantage of the privacy-preserving characteristics of blockchain ledger technology while considering the topology of large-scale research enterprises, which the authors characterize as a network of networks. As compared to centralized server privacy-preserving approaches, the peer-to-peer blockchain approach has advantages related to provenance as well as immutability and transparency of the models. They created an implementation of the framework called HierarchicalChain (Hierarchical privacy-preserving modeling on blockChain) and evaluated it using 3 healthcare and genomic datasets comparing HierarchicalChain’s predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. The authors found that HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time.

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