Predicting and reducing “hospital‐acquired infections” using a knowledge‐based e‐surveillance system

The use of automated computer methods when detecting hospital-acquired infections (HAIs) enhances the validity of the surveillance in an effective manner. This is because manual infection control systems used by hospitals are time consuming and are often restricted to intensive care units. This paper proposes a new knowledge-based electronic surveillance system to predict and reduce HAIs. The system can gather patient-associated data from hospital databanks to automatically predict patient injury based on the standard central line-associated bloodstream infection algorithm for HAI detection rules. The application of the proposed electronic system reduces the number of central lines associated with infection of the bloodstream and reduces the length of stay for patient treatment and thus reduces costs. The proposed system has several advantages: (a) It is a web-based system that collects actual data from patients from several IT sources, which will help collect patient data safely and quickly, thereby predicting HAIs effectively. (b) It has an integrated simulator to generate patient records, providing the ability to train medical personnel and nurses to enhance their skills. (c) It is a multimedia-based system to improve patient health reporting. (d) It assists policymakers in reviewing and approving control plans and policies to reduce and prevent hospital injuries. (e) The investigational results of the system showed an enhancement value equal to 87%.