Strategies for conducting situated studies of technology use in hospitals

Ethnographic methods are widely used for understanding situated practices with technology. When authors present their data gathering methods, they almost invariably focus on the bare essentials. These enable the reader to comprehend what was done, but leave the impression that setting up and conducting the study was straightforward. Text books present generic advice, but rarely focus on specific study contexts. In this paper, we focus on lessons learnt by non-clinical researchers studying technology use in hospitals: gaining access; developing good relations with clinicians and patients; being outsiders in healthcare settings; and managing the cultural divide between technology human factors and clinical practice. Drawing on case studies across various hospital settings, we present a repertoire of ways of working with people and technologies in these settings. These include engaging clinicians and patients effectively, taking an iterative approach to data gathering and being responsive to the demands and opportunities provided by the situation. The main contribution of this paper is to make visible many of the lessons we have learnt in conducting technology studies in healthcare, using these lessons to present strategies that other researchers can take up

Computer-Mediated Trust in Self-interested Expert Recommendations

International audienceImportant decisions are often based on a distributed process of information processing , from a knowledge base that is itself distributed among agents . The simplest such situation is that where a decision-maker seeks the recommendations of experts. Because experts may have vested interests in the consequences of their recommendations, decision-makers usually seek the advice of experts they trust . Trust, however, is a commodity that is usually built through repeated face time and social interaction , and thus cannot easily be built in a global world where we have immediate internet access to a vast pool of experts. In this article, we integrate findings from experimental psychology and formal tools from Artificial Intelligence to offer a preliminary roadmap for solving the problem of trust in this computer-mediated environment. We conclude the article by considering a diverse array of extended applications of such a solution