An Intelligent Healthcare Administration Model For Clinical Quality Benchmarking Using Medical Image Processing And Artificial Intelligence Techniques

Abstract

The progressive use of artificial intelligence (AI) and medical image processing technology has revolutionized healthcare administration through support of clinical decisions and quality benchmarking systems through an intelligent system. In contemporary healthcare organizations, clinical quality benchmarking is vital in assessing diagnostic stability, healthcare effectiveness, and optimizing patient outcomes. This paper suggests a smart healthcare administration framework, combining the medical image processing and AI-based analytics to support automated clinical quality benchmarking. The given framework unites image preprocessing, feature extraction with the help of a convolutional neural network (CNN), feature classification with the help of deep learning, and healthcare performance assessment in one analytic framework. Experimental analysis was done with publicly available datasets of medical images such as chest X-ray, diagnostic radiographic images etc. Image normalization, noise reduction, contrast enhancement and resizing to enhance performance in extracting features was used in the preprocessing stage. The deep learning model based on CNN was used to detect clinically relevant imaging patterns in relation to healthcare quality indicators. Experimental outcomes showed high benchmarking performance with an accuracy of 96.4, precision in 95.8, recall of 95.2, F1-score of 95.5, sensitivity of 95.7, specificity of 96.1 and ROC-AUC of 97.1. The robustness, stability, and generalization ability of the proposed framework was statistically validated by 10-fold cross-validation. The created intelligent healthcare administration model can greatly assist healthcare institutions in enhancing the quality of assessment of clinical quality, diagnostic reliability and evidence-based healthcare administration practices.