This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic
The current conversation around automation and artificial intelligence technologies creates a future vision where humans may not possibly compete against intelligent machines, and that everything that can be automated through deep learning, machine learning, and other AI technologies will be automated. In this article, we focus on general practitioner documentation of the patients’ clinical encounter, and explore how these work practices lend themselves to automation by AI.
Automation of jobs is discussed as a threat to many job occupations, but in the UK healthcare sector many view technology and automation as a way to save a threatened system. However, existing quantitative models that rely on occupation-level measures of the likelihood of automation suggest that few healthcare occupations are susceptible to automation. In order to improve these quantitative models, we focus on the potential impacts of task-level automation on health work, using qualitative ethnographic research to understand the mundane information work in general practices. By understanding the detailed tasks and variations of information work, we are building a more complete and accurate understanding of how healthcare staff work and interact with technology and with each other, often mediated by technology.