Effective decision-making is crucial to control outbreaks of infectious diseases. Poor decision-making around infection prevention and control costs the NHS an estimated £2.7 billion annually in the UK. This is primarily due to insufficient time and resources for teams to properly analyse outbreak situations and determine the most effective actions to take. These inefficiencies lead to prolonged lengths of stays, unnecessary medical procedures, and severe consequences for patients. Infection prevention and control is a complex field, and artificial intelligence (AI) has the potential to revolutionise how infectious diseases are managed by supporting optimal decision-making. However, the black-box nature of these AI systems and the inability to communicate effectively has hindered their adoption at scale. As a result, we are applying for funding to develop trust, transparency, and communication into a novel AI recommendation tool for infection prevention and control teams. By providing this level of transparency and communication, we hope to increase trust in AI tools and promote their adoption in infection prevention and control. The potential impacts of this system are far-reaching, not only in the primary marketplace of hospital infection prevention and control but also in providing transparency and communication of algorithm recommendation systems, enabling them to be adopted in various use cases. With the development of this system, we hope to decrease the costs associated with poor decision-making in infection prevention and control and ultimately improve patient outcomes. Improving the efficiency of decision-making in this field can help reduce the spread of infectious diseases and prevent future outbreaks. Investing in developing such systems is critical to provide better care and reducing healthcare costs. We believe that our research at NEX.Q will lead to the development of a valuable tool that can be adopted in various healthcare settings and ultimately improve patients' lives.