REA Newsletter Editor: Sheuwen Chuang. Article by: By Sheuwen Chuang, Taipei Medical University (Taiwan)
Resilience Engineering (RE) has been recognized widely and grown rapidly worldwide. The initiatives that highlight how resilience is unique and essential to safety management have advanced the RE theory and methodology. The four cornerstones of resilience, including the ability to monitor, respond, anticipate, and learn, are well-known to sustain operations or ensure system and individual safety. Various studies developed novel instruments, comprehensive frameworks, and analysis tools to assist in measuring, implementing, and improving resilience potentials.
In contrast to reinforcing human adaptations to deal with contextual difficulties, system design, especially incorporating sophisticated data use, provides algorithmic information that can support humans making adequate decisions to cope with situation challenges. The algorithmic or aggregated data can profile or characterize an entity, such as a process (e.g., nursing care), a kind of resource (e.g., critical care beds), or an inpatient to reveal a certain condition that the entity may possess. The profiling information can be used to derive, infer, predict, or evaluate a situation that the entity would face and should be monitored or anticipated to keep the system or individual (patient) safe.
Our team has collaborated with the researchers and practitioners in various disciplines to initiate two projects by fully utilizing big data such as expert interview data, nursing notes (text), regular quantitative data from hospital operations. One study is to redesign the hospital acquired infection control system in a regional hospital to increase monitoring and anticipatory ability toward proactive infection control. The other is to build a smart mass casualties distribution system to enhance effectiveness of distribution response. The two studies are to improve system resilience through in-time understanding the profiling information of critical entities involved in the care delivery or emergency medical care system.
Data is power; however, data is the most challenging part of collecting and computing in the studies. For example, how to define the ability of a hospital’s mobilization and how to define a patient’s risk level in the nursing care processes by data. These difficulties are associated with embedding resilience potential, i.e., the ability to monitor into the system. Nevertheless, we have not achieved our goals. However, the studies have brought rich learning to the team. We welcome researchers who are conducting similar projects to share or discuss with us.