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Over the last three decades, health information technology (HIT) has been an inherent part of modern healthcare, improving workflow and services to be more effective and efficient. However, HIT system deployment, adoption, and implementation can create significant disruptions, such as interoperability and interfaces between various other systems. Despite many benefits and strengths, HIT has made healthcare a truly complex socio-technical system. This layer of complexity may pose substantial risks to patient safety and organisational efficiency that may compromise intended benefits. Thus, HIT can cause patient harm, patient inconvenience, or delays in care delivery, both individually and collectively, often in unexpected and unforeseen ways (1,2).
Therefore, there is a need to identify the types of patient safety incidents, their contributing factors, the consequences of those incidents, and actions taken to manage those incidents. This would help minimise the risk to patient safety and devise a set of strategies to prevent them. The project will also determine the role of Machine Learning Algorithms (MLA) and Artificial Intelligence (AI) in identifying HIT-related incidents, improving decision-making, and thus minimising the risk to patient safety (3).
The project will comprise three phases.
NB: Patient and Public Involvement (PPI) representatives will be integral to all phases 1-3, recruited by the UoB or the researchers, ensuring a collaborative and inclusive approach to the research.
Given the cross-national nature of the project, we will seek ethics approval/guidance from multiple ethics committees, i.e., the Ethics Committee of the UoB, as well as the Ethical Advisory Board of Southeast Sweden, to ensure the highest ethical standards are maintained throughout the research.
Formal applications can be made via the University of Bradford web site.
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