A case study approach to high-containment laboratory workflows promoting sustainability, networking and innovation. Academic Article uri icon

abstract

  • Advances in information technologies (ITs) and operational technologies (OTs) offer high-containment laboratories opportunities to evolve scientific and operational approaches, while increasing efficiency. Emerging technologies steadily introduce changes in data generation and management practices. United States (US) government agencies and partners operate high-containment laboratories that rely on ITs/OTs to provide critical scientific functions that support prevention, detection, response and recovery for catastrophic events. These unique operating environments provide an opportunity for implementation of ITs/OTs that can facilitate both efficiency and deeper or parallel study of disease and associated biological phenomena. Operational study by subject matter experts can aid in identification of requirements and challenges pertaining to emerging ITs/OTs, examination of use cases, refinement of technical specifications and optimisation of workflows. The National Bio and Agro-Defense Facility (NBAF) in the United States of America (USA), slated to be fully operational by 2023, will be a state-of-the-art research and diagnostic facility with Biosafety Level 2, 3 and 4 laboratories for the study of high-consequence transboundary animal pathogens and zoonotic diseases impacting public health. The NBAF will support the diagnosis of emerging diseases, development of countermeasures and transboundary animal disease training. Given the rapid emergence of IT/OT solutions, the authors used a case study approach to analyse and assess real-world, high-containment laboratory functions to help maximise efficiency in mission delivery for the NBAF and the broader high-containment laboratory network. The case study approach described here could be widely adapted to diverse situations characterised by a high rate of change to provide accurate, relevant workflow analyses and optimised recommendations.

published proceedings

  • Rev Sci Tech

author list (cited authors)

  • Hunt, C. L., Yu, L., Cochran, M., Liu, J., Mccarl, B., Johnson, C. D., Brun, M., & Berquist, M.

complete list of authors

  • Hunt, CL||Yu, L||Cochran, M||Liu, J-C||Mccarl, B||Johnson, CD||Brun, M||Berquist, M

publication date

  • January 1, 2020 11:11 AM