Concerted adoption as an emerging strategy for digital transformation of healthcare - lessons from Australia, Canada, and England

Objectives: With an increasing focus on the digitalization of health and care settings, there is significant scope to learn from international approaches to promote concerted adoption of electronic health records. Materials and methods: We review three large-scale initiatives from Australia, Canada, and England, and extract common lessons for future health and social care transformation strategy. Results: In doing so, we discuss how, despite differences in contexts, concerted adoption enables sharing of experience and learning to streamline the digital transformation of health and care. Discussion and conclusion: Concerted adoption can be accelerated through building communities of expertise and partnerships promoting knowledge transfer and circulation of expertise, commonalities in geographical and cultural contexts, and commonalities in technological systems

How Can Social Media Lead to Co-Production (Co-Delivery) of New Services for the Elderly Population? A Qualitative Study

Background:The future of health care services in the European Union faces the triple challenges of aging, fiscal restriction, and inclusion. Co-production offers ways to manage informal care resources to help them cater for the growing needs of elderly people. Social media (SM) is seen as a critical enabler for co-production.Objective:The objective of this study was to investigate how SM—private Facebook groups, forums, Twitter, and blogging—acts as an enabler of co-production in health and care by facilitating its four underlying principles: equality, diversity, accessibility, and reciprocity.Methods:We used normalization process theory as our theoretical framework to design this study. We conducted a qualitative study and collected data through 20 semistructured interviews and observation of the activities of 10 online groups and individuals. We then used thematic analysis and drew on principles of co-production (equality, diversity, accessibility, and reciprocity) as a deductive coding framework to analyze our findings.Results:Our findings point to distinct patterns of feature use by different people involved in care of elderly people. This diversity makes possible the principles of co-production by offering equality among users, enabling diversity of use, making experiences accessible, and encouraging reciprocity in the sharing of knowledge and mutual support. We also identified that explication of common resources may lead to new forms of competition and conflicts. These conflicts require better management to enhance the coordination of the common pool of resources.Conclusions:SM uses afford new forms of organizing and collective engagement between patients, carers, and professionals, which leads to change in health and care communication and coordination

Domesticating AI in medical diagnosis

We consider the anticipated adoption of Artificial Intelligence (AI) in medical diagnosis. We examine how seemingly compelling claims are tested as AI tools move into real-world settings and discuss how analysts can develop effective understandings in novel and rapidly changing settings.Four case studies highlight the challenges of utilising diagnostic AI tools at differing stages in their innovation journey. Two ‘upstream’ cases seeking to demonstrate the practical applicability of AI and two ‘downstream’ cases focusing on the roll out and scaling of more established applications.We observed an unfolding uncoordinated process of social learning capturing two key moments: i) experiments to create and establish the clinical potential of AI tools; and, ii) attempts to verify their dependability in clinical settings while extending their scale and scope. Health professionals critically appraise tool performance, relying on them selectively where their results can be demonstrably trusted, in a de facto model of responsible use. We note a shift from procuring stand-alone solutions to deploying suites of AI tools through platforms to facilitate adoption and reduce the costs of procurement, implementation and evaluation which impede the viability of stand-alone solutions.New conceptual frameworks and methodological strategies are needed to address the rapid evolution of AI tools as they move from research settings and are deployed in real-world care across multiple settings. We observe how, in this process of deployment, AI tools become ‘domesticated’. We propose longitudinal and multisite `biographical’ investigations of medical AI rather than snapshot studies of emerging technologies that fail to capture change and variation in performance across contexts