Publications

Using LRN by Ziplitics, this paper was analyzed to highlight key insights from a global expert panel that evaluated moldable ostomy technologies using a modified Delphi consensus process. The review compares moldable baseplates with traditional cut-to-fit appliances, emphasizing the importance of appropriate product selection, education, and access to specialized ostomy care in reducing peristomal complications. It also presents six evidence-based recommendations to support clinical decision-making and improve outcomes for people living with an ostomy.

Supported by LRN by Ziplitics, this study reviews the current evidence and expert perspectives on moldable ostomy technologies through a modified Delphi consensus process. The research compares moldable stoma baseplates with traditional cut-to-fit appliances and provides six consensus statements to guide clinical practice. The findings emphasize evidence-based product selection and strategies to improve ostomy care and reduce peristomal complications.

Analyzed with LRN by Ziplitics, this article explores how artificial intelligence is reshaping wound, ostomy, and continence (WOC) nursing by supporting clinical decision-making, diagnosis, prognosis, and treatment. It examines the evolution of AI, explains key concepts, and addresses the challenges of “black box” AI systems that lack transparency. The paper emphasizes the importance of explainable AI and highlights the need for WOC nurses to understand AI technologies so they can be applied ethically, responsibly, and in partnership with clinical expertise.

Generated with LRN by Ziplitics, this study examines how explainable AI can support the development of a systematic literature review on best practices for pressure injury prevention in hospitalized patients.

Leveraging LRN by Ziplitics, this study evaluates the effectiveness of an explainable AI platform for conducting a literature search on mentorship in academic pharmacy. The findings explore how explainable AI can streamline the literature search process, improve the identification of relevant research, and support more efficient and transparent evidence discovery in academic and healthcare settings.

Powered by LRN by Ziplitics, this study compares literature review results on mentorship in healthcare disciplines generated by an explainable AI platform with traditional manual searches conducted by pharmacy students and faculty. The findings evaluate how AI-assisted literature reviews perform in identifying relevant evidence, demonstrating the potential of explainable AI to enhance the speed, consistency, and efficiency of evidence discovery while maintaining transparency throughout the review process.