Building Teams Using Principles from High Reliability Organizations and Biological Systems

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Biological systems and high reliability organizations are networks that operate in complex high-hazard environments for extended periods without serious accidents or catastrophic failures. High reliability organizations are comprised of multiple interactions and relationships between personnel within which information and communication flows and are critical to the functioning of many industries including aviation, pharmaceuticals, energy, military, and healthcare sectors. Biological systems, such as metabolic pathways, are one of the oldest self-organizing systems and are comprised of molecular entities that interact at various levels to allow for adaptation in complex environments. The presence of specific network structures, for example, specific geometric features or network motifs in social, biological, and technological networks has been associated with their ability to withstand perturbations. Our central hypothesis is that despite their distinct nature, the abstraction of human-made high reliability organizations and biological systems using network science can reveal general organizing principles that enable resilience across these networks. In this proposal, we aim to understand (1) the organizing principles that make some organizations more reliable than others in the face of perturbations, and (2) whether such principles can be extended across disciplines to build high reliability organizations that are resilient to perturbations. Therefore, we propose to study the organizational patterns of two distinct networks: the national Department of Veterans Affairs healthcare system comprised of clinicians, other patient facing roles, and administrative staff who collaborate to promote high value care that is safe, efficient, effective, timely, and at a manageable cost; and the biological networks of three species (Homo sapiens, Saccharomyces cerevisiae and Escherichia coli). First, we will examine and compare the structural components of these networks to identify similarities and differences in their organizing principles. Second, we will identify the network structures that are protective against perturbations in each system. Finally, using complementary organizing principles that we identify in these networks, we will use network theories drawn from social sciences, biology and physics to determine whether teams constructed using these principles are resilient to random and deliberate attacks on the networks.  Ultimately, this work will increase our understanding of the adaptive network structures and will inform the design of more reliable networks.