Lay summary
This research seeks to develop a predictive framework using artificial intelligence and machine learning tools to reduce the incidence of hospital-acquired infections (HAI) amongst Aotearoa-NZ population. HAIs pose a significant threat to public health systems globally, with Māori exhibiting a disproportionately higher risk. This big data-driven predictive framework will draw from large datasets of patients’ electronic health records and HAI incidence rates to identify significant risk factors and patterns leading to HAIs. Through the development of a state-of-the-art decision support system, the proposed framework aims to improve the health outcomes of patients while also reducing healthcare expenditures. Dissemination of findings through academic publications and community outreach will ensure that the results reach all relevant stakeholders. The successful implementation of the proposed framework has the potential to significantly reduce the incidence of HAIs among those patients attending hospital, leading to improved health outcomes and reduced healthcare costs.