A human-centered approach to the design and implementation of advanced health technologies using Cognitive Work Analysis (CWA)

While there have been several literature reviews on the performance of digital sepsis prediction technologies and clinical decision-support algorithms for adults, there remains a knowledge gap in examining the development of automated technologies for sepsis
prediction in children. Pediatric sepsis is a major cause of mortality of children worldwide. However, there is still a lack of easy-to-use predictive tools that can accurately diagnose sepsis in children. This research aimed to develop an optimal algorithm for supporting early sepsis prediction in children. The results may inform research on identifying relevant predictive indicators best suited for the design of digital technologies in specific use contexts and environments, improvements towards model development for sepsis prediction and factors supporting the optimal workflow integration of digital prediction systems by clinicians.

Faculty Supervisor:

Catherine Burns

Student:

Partner:

National Technical University of Ukraine

Discipline:

Computer science

Sector:

Technology; Health and Related Sciences & Technology; Artificial Intelligence

University:

University of Waterloo

Program: