JMIR Publications

ABSTRACT

Background:

The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and utilization of health big data, reflecting the empirical characteristics of users who have performed health big data analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services.

Objective:

This study was conducted to integrate and analyze users’ experiences along the health big data analysis journey using the human-centered design methodology and reflect them in the development of artificial intelligence agents that support future health big data analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and utilization of health big data (HBD), which will be urgently needed in the near future.

Methods:

Using human-centred design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analysing and utilizing health big data. We identified users' empirical characteristics, emotions, pain points and needs related to health big data analysis and utilization and created personas and journey maps.

Results:

Three types of personas and journey map—healthcare professionals as a big data analysis beginner, healthcare professionals who have experience in big data analytic, and non-healthcare professionals who are experts of big data analytics—were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through navigation function, a crisis management support system, communication and sharing among users, and expert linkage service.

Conclusions:

The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and utilization. This is expected to further increase the usability of HBD by helping users perform effective utilization of HBD more easily.