Three key challenges to a whole-system approach to process improvement in health systems are the complexity of socio-technical activity, the capacity to change purposefully, and the consequent capacity to proactively manage and govern the system. The literature on healthcare improvement demonstrates the persistence of these problems. In this project, the Access-Risk-Knowledge (ARK) Platform, which supports the implementation of improvement projects, was deployed across three healthcare organisations to address risk management for the prevention and control of healthcare-associated infections (HCAIs). In each organisation, quality and safety experts initiated an ARK project and participated in a follow-up survey and focus group. The platform was then evaluated against a set of fifteen needs related to complex system transformation. While the results highlighted concerns about the platform’s usability, feedback was generally positive regarding its effectiveness and potential value in supporting HCAI risk management. The ARK Platform addresses the majority of identified needs for system transformation; other needs were validated in the trial or are undergoing development. This trial provided a starting point for a knowledge-based solution to enhance organisational governance and develop shared knowledge through a Community of Practice that will contribute to sustaining and generalising that change.

Linked data (LD) have the capability to open up and share materials, held in libraries, archives and museums (LAMs), in ways that are restricted by many existing metadata standards. Specifically, LD interlinking can be used to enrich data and to improve data discoverability on the Web through interlinking related resources across datasets and institutions. However, there is currently a notable lack of interlinking across leading LD projects in LAMs, impacting upon the discoverability of their materials. This research describes the Novel Authoritative Interlinking for Semantic Web Cataloguing in Libraries (NAISC-L) interlinking framework. Unlike existing interlinking frameworks, NAISC-L was designed specifically with the requirements of the LAM domain in mind. The framework was evaluated by Information Professionals (IPs), including librarians, archivists and metadata cataloguers, via three user-experiments including a think-aloud test, an online interlink creation test and a field test in a music archive. Across all experiments, participants achieved a high level of interlink accuracy, and usability measures indicated that IPs found NAISC-L to be useful and user-friendly. Overall, NAISC-L was shown to be an effective framework for engaging IPs in the process of LD interlinking, and for facilitating the creation of richer and more authoritative interlinks between LAM resources. NAISC-L supports the linking of related resource across datasets and institutions, thereby enabling richer and more varied search queries, and can thus be used to improve the discoverability of materials held in LAMs.

Organisational trust enhances effectiveness, but its evaluation is often overlooked in real-world AI deployments. The relationship between trustworthy AI (TAI) and organisational trust is largely unexplored. This scoping review applies socio-technical systems analysis (STSA) to examine TAI mechanisms and their ability to build organisational trust, marking the first use of STSA in this context. A search of academic databases yielded 803 articles, with 54 included in the review. The findings show a lack of focus on the context, longitudinal aspects and organisational maturity in TAI implementation. While TAI guidelines aim to foster trust in specific technologies, successful implementation requires broader organisational maturity and active efforts to build, maintain and repair trust both within organisations and with the public.