Translating AI innovation into healthcare practice: The role of professionals’ sensemaking



Translating AI innovation into healthcare practice: The role of professionals’ sensemaking  

Division: Organization and Management

Corresponding author: Yaru Chen, Research Fellow, Centre for Healthcare Innovation Research, Cass Business School, City, University of London

Email: yaru.chen@city.ac.uk

Radhika Narasinkan, Public Health England

Email: radhika.narasinkan@gmail.com 

Harry Scarbrough, Professor of Information Systems and Management, Cass Business School, City, University of London

Email: harry.scarbrough.1@city.ac.uk


Compared to the other forms of digital technology, the advent of AI in the workplace is seen as having particular implications for the work practices, status and identity of professional groups, since their expertise is, for the first time, vulnerable to forms of automation and de-skilling previously confined to lower-skilled workers (Susskind & Susskind, 2015). Previous research on technological innovation, however, suggests that the implementation and use of AI will be driven not only by its potential but also by users’ initial sensemaking of the technology (Griffiths, 1999). How professional groups make sense of  new technologies and the reworking of professional roles to enable their use play a significant part in the successful ‘translation’ of innovations (Reay et al., 2013). ‘Translation’ is defined here as involving the re-interpretation of innovative ideas or technologies and their material implementation in practice (Czarniawska-Joerges and Sevón, 1996). It is preferred to terms such as innovation adoption or implementation because it emphasizes the work involved in fitting the innovation to a particular context (Ansari et al., 2010).


The sensemaking lens has been increasingly utilised to examine the interaction between human actors and technologies (Griffiths, 1999; Jensen and Aanestad, 2007). The sensemaking of individuals involves the search for a continued self in an ambiguous environment, via identity construction and re-construction (Brown et al., 2008). It becomes more salient as individuals encounter a new phenomenon or experience an interruption, prompting actors to ask questions of ‘who am I’ and ‘how do I fit in the story’ (Weick et al., 2005). We argue here that the translation of AI technology into front-line healthcare practice constitutes such an interruption, since it threatens changes in professionals’ work practices and professional identity (Brown et al., 2008; Chen and Reay, forthcoming). Thus, we are likely to see the emergence of decision-making assemblages where ‘radiologists are mandatory as component human authorities’ and which are being labelled ‘radiologist-in-the-loop’ systems (Liew, 2018). Such assemblages are not given either by the technology or by the human actors involved, but emerge from the process of translation (Barley, 1986; Nielsen, Mathiassen and Newell, 2014).  


In this paper, we aim to contribute to greater understanding of the translation of AI into practice by focussing on professionals’ sensemaking of the new AI technology emerging in the field of radiology. This is an important arena for the study of this topic, because professionals wield great power over the determination of practices and services in healthcare and are therefore critical to either enabling or obstructing translation efforts (Ferlie, Fitzgerald, Wood and Hawkins, 2005), Radiology is a specialism in which the application of AI shows the greatest promise of widespread use and patient benefits (Loder and Nicholas, 2018). We ask the following questions:  


What influences radiology professionals’ sensemaking of the AI technology emerging in their field?




What implications does this have for the future translation of AI technology into front-line practice in radiology? 


To address this question, we draw on an initial empirical study of radiology professionals involved in breast cancer screening in the UK. The sense-making of this initial group is prospective in nature (Stigliani and Ravasi, 2012), since AI applications have not yet been widely adopted but are widely anticipated as emerging from their early stage development (a later phase of work will address sites where AI applications have been introduced into professional works). Our study includes the analysis of the responses by professional groups (via professional journals, and other media). In addition, through interviews with individual radiology professionals we related sense-making around AI to established work practices and professional identities. Further. we sought to understand the expectations placed upon their roles and identities by their organizational context and professional affiliation.


Initial findings


We find that, despite some concerns about the potential job losses resulting from AI use, the professional bodies’ interpretation is broadly supportive of AI acting as a tool under the control of radiologists. Articles in professional journals, for example, suggest that radiologists outperform AI innovations regarding clinical judgement, which is developed through years of education, training, and practice (Liew, 2018).  With respect, to individual professionals’ sense-making around AI, we found that this reflected their status and capability concerns, and that these were differentiated across professional groups according to their current practices. Radiographers, which are a less qualified, lower status group, had concerns about losing a particular aspect of their work – film reading. Higher status radiologists, meanwhile, emphasized the positive benefits of AI, seeing this technology as enabling them to focus on more clinical work. We also found that stakeholders’ influence on radiologists’ professional identity and their enactment of an augmented radiologist role had implications for their sense-making around AI (Chen and Reay, forthcoming). For example, external targets and standards such as recall rates and waiting times directly affected how these professionals viewed the prospect of the introduction of AI applications at their workplaces. 


These findings highlight the complex character of professionals’ prospective sense-making around AI technology. They suggest that professionals’ involvement in the translation of AI into frontline healthcare practices will be influenced not only by their fears around its impact on their jobs – a major theme of existing work (Susskind & Susskind, 2015). – but also by their sense-making efforts. This is not reducible to a simple positive or negative perception of AI,  but reflects concerns, firstly, to buttress professional identity by appropriating higher status work, and, secondly, to ensure that their work practices continue to meet the stakeholder expectations underpinning their role .




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