Enhancing palliative care triage: decision support system for patient prioritisation

Abstract

ABSTRACT This study presents an innovative approach to patient triaging in palliative care through a convolutional neural network (CNN). CNN, trained on a dataset annotated by medical experts, is adept at identifying levels of patient urgency from complex clinical data and is tailored to decipher intricate patterns enabling the accurate prioritisation of care needs. Our evaluation involved experimental simulations to test the model’s precision and reliability in triaging. CNN demonstrated exceptional performance, with all major metrics – recall, F1-Score, precision, and accuracy – exceeding 98%. This represents a substantial improvement over current triaging methods and underscores the potential of AI to lead healthcare innovation. The model’s precision in identifying urgent patient cases, coupled with its low misclassification rate, is particularly pertinent to the high-stakes environment of palliative care.These outcomes suggest that AI has the potential to transform palliative care triaging, ensuring that patients receive timely and appropriate interventions is promising in terms of enhancing the responsiveness of care delivery, marking a pivotal step towards a more patient-focused approach.