This presentation explains a complex-prediction modelling evaluation that can track and measure the usability and impact of an Artificial Intelligence (AI) as a clinical-decision-support (CDS): Rapidx AI. The evaluation considers the perspective of those embedded within the workflows of a randomised controlled trial (RCT) within 12 participating South Australian emergency departments.
RAPIDx AI aims to improve patients' clinical management and outcomes for those presenting to hospitals with suspected cardiac chest pain (e.g., possible heart attack). The proposed evaluation looks at the RCT, so it does not test the clinical outcomes of RAPIDx AI but rather the end-user feedback concerning the algorithm uptake within the hospital workflows from the perspectives of a variety of stakeholders involved in this technically enhanced decision-making process. n= ~ 3,600 (~300 people per hospital).
RAPIDx AI aims to improve patients' clinical management and outcomes for those presenting to hospitals with suspected cardiac chest pain (e.g., possible heart attack). The proposed evaluation looks at the RCT, so it does not test the clinical outcomes of RAPIDx AI but rather the end-user feedback concerning the algorithm uptake within the hospital workflows from the perspectives of a variety of stakeholders involved in this technically enhanced decision-making process. n= ~ 3,600 (~300 people per hospital).