Strategic Healthcare Technology Management Using Quantum Computing AND Deep Learning FOR Clinical Decision Support Systems

Abstract

The surge in healthcare data and intelligent medical technologies has augmented the need to have advanced Clinical Decision Support Systems (CDSS) that can enhance the diagnostic accuracy, prediction of patient outcome and management of healthcare resources. The traditional healthcare analytics models have been faced with constraints to handle massive heterogeneous clinical data due to the complexity of computations, un-optimized feature optimization, and decreased predictive scalability. This paper introduces a healthcare technology management model that considers quantum computing and deep learning to be combined to create intelligent clinical decision support applications. The proposed framework is the product of a hybrid Convolutional Neural NetworkLong Short-Term Memory (CNN-LSTM) framework along with quantum-inspired optimization methods aimed at increasing the clinical prediction efficiency as well as the computational efficiency. The framework integrates the preprocessing of clinical data, automated feature mining, intelligent modeling of prediction, and decision support through optimization in a coherent healthcare analytics landscape. The publicly available healthcare datasets of demographic data, physiological data, laboratory data, and clinical outcome variables were used as experimental data. The proposed framework performed better than traditional machine learning and standalone deep learning models, with an accuracy of 97.4%, precision of 96.9, recall of 96.5, F1-score of 96.7 and ROC-AUC of 0.98. Cross-validation 10-fold validation proved to be a very robust and had a high ability to generalize with significant p-value of less than 0.05. The quantum-inspired optimization also led to better performance of feature selection and minimized redundancy in computation in tasks of clinical prediction. The suggested framework will be effective in supporting smart healthcare management, risk prediction of disease and high-level clinical decision support applications in the contemporary digital healthcare systems.