John Guilfoyle’s scientific contributions

Innovative approaches are needed for managing risk and system change in healthcare. This paper presents a case study of a project that took place over two years, taking a systems approach to managing the risk of healthcare acquired infection in an acute hospital setting, supported by an Access Risk Knowledge Platform which brings together Human Factors Ergonomics, Data Science, Data Governance and AI expertise. Evidence for change including meeting notes and use of the platform were studied. The work on the project focused on first systematically building a rich picture of the current situation from a transdisciplinary perspective. This allowed for understanding risk in context and developing a better capability to support enterprise risk management and accountability. From there a linking of operational and risk data took place which led to mapping of the risk pattern in the hospital.

Effective governance necessitates going beyond compliance with rules, regulations and procedures; particularly as adverse events are generally the result of a combination of human, organisational, technological, and economic factors. This study explores the use of socio-technical systems analysis (STSA) in an Artificial Intelligence (AI) platform called Access-Risk-Knowledge (ARK) to go beyond established accountability frameworks by linking evidence, outcomes, and accountability. The aim of the ARK-Virus project was to use the ARK Platform to support mindful risk governance of infection prevention and control (IPC) for healthcare organisations during the COVID-19 pandemic. ARK was deployed across three healthcare organisations: a fire and ambulance service, an outpatient dialysis unit, and a large acute hospital. Each organisation conducted an IPC case study, the three of which were then compiled into a synthesis project. A set of guidance principles for a pandemic preparedness strategy were proposed using the synthesis project findings. A Community of Practice (CoP) enabled the successful deployment of ARK, including intense interdisciplinary collaboration and was facilitated by practitioner-researchers in the implementing organisations. Data governance methods and tools supported a whole organisation and multi-organisation approach to risk. This first full implementation trial of the ARK platform deployed dedicated STSA within a semantically structured AI framework, demonstrating accountable risk management that addresses the complex antecedents of risk, links to evidence, and has the potential for managing the full cycle of risk mitigation and improvement.

This paper proposes a framework for developing a trustworthy artificial intelligence (AI) supported knowledge management system (KMS) by integrating existing approaches to trustworthy AI, trust in data, and trust in organisations. We argue that improvement in three core dimensions (data governance, validation of evidence, and reciprocal obligation to act) will lead to the development of trust in the three domains of the data, the AI technology, and the organisation. The framework was informed by a case study implementing the Access-Risk-Knowledge (ARK) platform for mindful risk governance across three collaborating healthcare organisations. Subsequently, the framework was applied within each organisation with the aim of measuring trust to this point and generating objectives for future ARK platform development. The resulting discussion of ARK and the framework has implications for the development of KMSs, the development of trustworthy AI, and the management of risk and change in complex socio-technical systems.

This paper proposes a framework for developing a trustworthy artificial intelligence (AI) supported knowledge management system (KMS) by integrating existing approaches to trustworthy AI, trust in data, and trust in organisations. We argue that improvement in three core dimensions (data governance, validation of evidence, and reciprocal obligation to act) will lead to the development of trust in the three domains of the data, the AI technology, and the organisation. The framework was informed by a case study implementing the Access-Risk-Knowledge (ARK) platform for mindful risk governance across three collaborating healthcare organisations. Subsequently, the framework was applied within each organisation with the aim of measuring trust to this point and generating objectives for future ARK platform development. The resulting discussion of ARK and the framework has implications for the development of KMSs, the development of trustworthy AI, and the management of risk and change in complex socio-technical systems.

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.