Lay summary
Machine learning (ML) is well established; however, its presence within healthcare is less extensive. Clinical decision-support algorithms are ubiquitous and informed by standard statistical tests. Although ML is enhanced statistical-analysis, clinicians are mistrustful of the approach. Finding ways to engage clinicians to lead the development of ML assisted algorithms is critical to the future of healthcare. Abtin Ijadi Maghsoodi has led a partnership between Te Whatu Ora Waikato and Waikato/Auckland universities to develop algorithms to streamline acute patient hospital flow. As the lead Te Manawa Taki data scientist, he has identified the need to enhance current algorithms with ML and through clinical engagement. The proposed project focuses on combining ML with clinical insights to develop a transparent, interpretable and effective predictive tool for patient prioritisation. By implementing a user-friendly interface across Te Manawa Taki, a trusted, data-driven framework can be implemented that enhances patient outcomes while reducing overall costs.