CEECP: CT-based enhanced e-clinical pathways in terms of processing time to enable big data analytics in healthcare along with cloud computing

Abstract

Clinical pathway is generally used as a tool for the implementation of evidence-based medicine and clinical guidelines. The e-clinical pathway is helpful in clinical cases covering both diagnostic and therapeutic fields. Healthcare facility automation is a challenging task to streamline a high informatics-intensive sector. With the help of datasets which were collected on LOS, CP, and EHR for hearth patient is taken from Kaggle data sets were collected, a better solution to this existing problem is proposed in this paper in the form of automation of CP, with the help of SNOMED-CT which not only helps to study the correct health situation of the patient and also helpful to estimate the correct length of stay in hospitals and to minimize the expenditures. This paper has carried out an extensive highlighted review for the current trending mechanism and approaches for smartly tackling privacy and security issues along with suggesting short-term stay in hospitals, thereby a cost reduction. In our proposed method, a new cost dynamic approach is suggested, which is a unique method for the exact analysis of data. The proposed CEECP method achieved less average processing time of 1116.6 µs and achieved accuracy of 99.01%, which is higher than existing methods. The proposed method achieved higher values for other factors such as accuracy, precision, sensitivity, and specificity.