Knowledge Based e-Health Surveillance System for Predicting Hospital Acquired Infections

Manual surveillance systems for hospital acquired infections is time-consuming and often limited to intensive care units. Computer-automated methods of hospital acquired infections detection can improve the validity of surveillance. A new knowledge based e-Health surveillance system for predicting hospital acquired infections is proposed. The system has the capability to collect patient-related data from hospital databases to predict the patient infection automatically based on knowledge discovery rules, and hospital acquired infections decision standard algorithms. Applying the proposed system, both central line associated bloodstream infections rates and patient treatment costs can be reduced significantly. The system has many benefits, among which are: (1) It is a web-based system that can collect real patients' data from many IT resources such as iPhone, laptops, internet, and hospital databases. These facilities help to collect patients' actual data quickly and safely in electronic format and hence predict hospital acquired infections efficiently. (2) The system has a built-in simulator for generating patients' data records, when needs; offering the capability to train nurses and medical staff for enhancing their qualifications. (3) The system is multimedia-based; it uses text, colors, and graphics to enhance patient healthcare report generation and charts. (4) It helps healthcare decision makers to review and approve policies and surveillance plans to reduce and prevent hospital acquired infections. (5) System experimental results achieved improvement value equal to 87%.