Evaluation and optimization of CES performances: application of Pareto principle to KPIs Global Clinical Engineering Journal

In recent times the approach to health care has been mostly influenced by the growing quantity of biomedical equipment used in hospitals, which needs the support of the clinical engineering service (CES). This work aims to suggest a methodology to improve the performance of a CES through the application of Pareto principle to the leading Key Performance Indicators (KPIs). The methodology is applied by focusing on using KPIs that represent a quantifiable measure of achieving goals set by an organization. In this study, five KPIs are considered: Uptime, MTTR (mean time to repair), PPM (percentage preventive maintenance completion), MTBF (mean time between failures), and the COSR (cost of service ratio). The first three indicators express the measure of CES efficiency in ensuring regular maintenance. The first step consists of retrieving data related to work orders for 2015-2016 on 6000 installed devices, carried out by manage - ment software. The second step is to get the results by using an environment for numerical calculation and statistical analysis. To identify the main critical issues that may be present, three indicators (Uptime, MTTR and MTBF) are analyzed by applying the Pareto principle (i.e., 20% of the causes produce 80% of the effects). Considering the totality of work orders, it is possible to concentrate on only 20% of them to focus on a small group to understand the correlations between them. Identifying these characteristics means identifying the main critical issues that are present, on which action must be taken, and which affect 80% of the overall behavior. Instead, the COSR and PPM indicators suggest distribution models that focus on the most critical devices. In conclusion, the way to analyze the results is obtained, when possible, by applying Pareto principle. Therefore, a CES will be able to focus on a few causes of poor performance. The achievement of these results could allow the standardization of the method used, enabling it to be applied to any healthcare system