A Holistic Approach to Integrated Healthcare Decision Support Using CDF-IHDSS With HCRNN And IRPO Optimization

Good clinical decision-making is constitutionally intricate because it changes in different elements of a person’s health experience and involves multiple levels of decision-making, while at the same time conditioning subsequent clinical decisions through diagnosis, treatment planning, and palliative care. In response to this inherent complexity, we present CDF-IHDSS-healthcare as a new Connected Decision Framework for an Integrated Healthcare Decision Support System (IHDSS). Unlike conventional and typical single-stage systems, our framework takes a systems perspective that permits adaptive and ongoing decision-making by drawing from contextual information from multiple levels of care. This model employs a hybrid model consisting of a Hybrid Convolutional Recurrent Neural Network (HCRNN) that recognizes spatial-temporal patterns from temporal medical data, and Improved Red Panda Optimization (IRPO) to tune decision parameters that are most influential for the decision. A multi-agent system design is applied to enable real-time coordination and further responsiveness at all stages of treatment. Experimental evaluations on clinician-annotated diabetes care test datasets established that CDF-IHDSS-healthcare model meaningfully improves the accuracy and quality of predictions and decisions, as compared with current systems. These results provide evidence for the potential of CDF-IHDSS-healthcare to help improve clinical care quality outcomes in practice and healthcare workflows.