CTI-Lab

CogNeat: Artificial Intelligence (AI) to reduce cognitive and information overload in Hospital

Sylvie Grosjean of the Communication Department is collaborating with the Montfort Knowledge Institute (Institut du Savoir Montfort) on a research project (CHRI funding) which investigates how an Artificial Intelligence (AI) technology could help clinicians and nurses working in hospital to manage digital stress. The main purpose of the project is to design a technology (based on a Machine Learning) that allows clinicians to manage the different forms of cognitive and information overload they face on a daily basis. This interdisciplinary project is based on a Participatory Design Approach involving physicians and nurses in the design and development of this AI technology.

Publications:

[1] Grosjean, S., Bonneville, L. & Marrast, P. (2019). Innovation en santé conduite par les médecins et infirmières : l’approche du design participatif à l’hôpital [Health Innovation Driven by Physicians and Nurses: A Participatory Design Approach in Hospital] Innovations, 60(3), 69-92.

Abstract: The objective of this article is to explore how healthcare professionals contribute to the design of health technology and to identify the elements that underline the relevance of a participatory design approach in this context. To this end, we present a health technology design project involving physicians and nurses to help them manage information, communication and cognitive overload at the hospital. We propose in this article a reflexive discussion on this participatory design approach in healthcare setting. To do this, we will examine the engagement of healthcare professionals in the analysis of their clinical activity and their informational practices, and their contribution to the design of health technology supported by a Machine Learning.

[2] Grosjean, S. (In press). L’interopérabilité sociale de l’IA en santé : Un enjeu pour le design d’algorithmes situés dans des pratiques [Social Interoperability of AI in Health: An Issue for the Design of Algorithms], Revue Française des Sciences de l’Information et de la Communication (RFSIC).

Abstract: In this article, our objective is to explore an emerging concept in the field of Artificial Intelligence (AI) in health, that of social interoperability. In other words, we would like to understand the relationship between users (healthcare professionals, patients, caregivers) and data generated by algorithms. To do this, we will rely on two research projects, conducted in healthcare organizations, and based on a participatory design approach. This approach allows future users of technologies to be involved from the beginning of the design process and helps to reveal anticipated uses, expectations and gradually to understand the relationship that users attribute to the data generated by algorithms. Through these two cases, we will show how it is possible to think about social interoperability through a “practice-based design” of machine learning technologies.

Note: Project funded by Canadian Institutes of Health Research (CIRH).

Contact: sylvie.grosjean@uottawa.ca

 

Keywords Digital Stress, Hospital, Information Overload, Participatory Design, Machine Learning, eHealth Technology