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Register a new userA Triple Helix Model of Medical Innovation: Supply, Demand, and Technological Capabilities in terms of Medical Subject Headings
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Added by
Daniele Rotolo
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on triple helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the medical context, where uncertainty poses significant challenges to the governance of innovation. We use the Medical Subject Headings (MeSH) of MEDLINE/PubMed to identify publications classified within the categories Diseases (C), Drugs and Chemicals (D), Analytic, Diagnostic, and Therapeutic Techniques and Equipment (E) and use these as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of medical research areas are used as representative entry perspectives of the medical innovation process. These are: (i) human papilloma virus, (ii) RNA interference, and (iii) magnetic resonance imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that point to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels.
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We analyzed Medical Subject Headings (MeSH) from 21.6 million research articles indexed by PubMed to map this vast space of entities and their relations, providing insights into the origins and future of biomedical convergence. Detailed analysis of MeSH co-occurrence networks identifies three robust knowledge clusters: the vast universe of microscopic biological entities and structures; systems, disease and diagnostics; and emergent biological and social phenomena underlying the complex problems driving the health, behavioral and brain science frontiers. These domains integrated from the 1990s onward by way of technological and informatic capabilities that introduced highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental structure-function-behavior questions. Article-level analysis confirms a positive relationship between team size and topical diversity, and shows convergence to be increasing in prominence but with recent saturation. Together, our results invite additional policy support for cross-disciplinary team assembly to harness transdisciplinary convergence.
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We propose and analyze numerically a simple dynamical model that describes the firm behaviors under uncertainty of demand forecast. Iterating this simple model and varying some parameters values we observe a wide variety of market dynamics such as equilibria, periodic and chaotic behaviors. Interestingly the model is also able to reproduce market collapses.
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