Application of a Novel PM Model to Assess the Risk of Infections in Medical Facilities as a Tool for Improving the Quality of Health Services and the Safety of Patients.

Infections with multi-drug resistant microorganisms associated with the provision of health services have become an acute problem worldwide. These infections cause increased morbidity as well as mortality and are a financial burden for the healthcare system. Effective risk management can reduce the spread of infections and thus minimize their number in hospitalized patients. We have developed a new approach to the analysis of hazards and of exposure to the risk of adverse events by linking the patient's health record system to the entire infrastructure of the hospital unit. In this study, using the developed model, we focused on infections caused by the bacterium, as they constitute a significant number of nosocomial infections in Poland and worldwide. The study was conducted in a medical facility located in the central part of Poland which provides tertiary care. In the proposed PM model, a risk analysis of hospital acquired infections at the Intensive Care and Anesthesiology Unit combined with the hospital's technical facilities and organizational factors was conducted. The obtained results indicate the most critical events which may have an impact on potential hazards or risks which may result from the patient's stay at the specific ward. Our method can be combined with an anti-problem approach, which minimizes the critical level of infection in order to determine the optimal functioning of the entire hospital unit. Research has shown that in most situations the spread dynamics of nosocomial infections can be controlled and their elimination may be attempted. In order to meet these conditions, the persons responsible for the daily operation of the medical facility and its individual wards have to indicate potential events and factors which present a risk to the hospitalized patients. On the basis of a created spreadsheet directions for improvement may be finally established for all potential events, their frequency may be minimized, and information may be obtained on actions which should be undertaken in a crisis situation caused by the occurrence of a given phenomenon. We believe that the proposed method is effective in terms of risk reduction, which is important for preventing the transmission of multi-drug resistant microorganisms in the hospital environment.