Objective:To construct a risk identification and early warning management model,and to explore its value in the risk control and management of active medical devices maintenance and repair. Methods:The risk identification and early warning knowledge base of active medical equipment maintenance and repair was constructed from three aspects:basic data,core data and auxiliary data. The risk evaluation index system was designed in combination with the equipment operating status,and the weight was assigned by coefficient of variation and the extension cloud algorithm was used to evaluate the risk level,so as to form a hierarchical early warning trigger path and a three-dimensional early warning intervention scheme of personnel,system and process. A total of 287 active medical devices in clinical use in the Second Hospital of Shanxi Medical University from 2022 to 2023 were selected,and 261 devices used in the period from January to December 2022 were managed by conventional management methods,270 active medical devices (including 244 in use under conventional management method) used from January to December 2023 were managed by active medical equipment maintenance and repair risk identification and early warning model (referred to as risk identification model management). The equipment maintenance and repair management effects of the two management methods were compared from the aspects of safety level assessment and risk hazard statistics,and business capability of personnel involved in equipment management were assessed and evaluated. Results:The risk rate of active medical equipment managed by risk identification model was 7.8％ (21/270),which was lower than that of conventional management method,and the difference was statistically significant (x2=8.773,P<0.05). Among the 2839 maintenance and repair activities carried out by the risk identification model management method,safety risk hazards of large medical equipment,ECG monitoring equipment,life support emergency equipment and medical testing equipment occurred 75,19,82 and 11 times,respectively,with the hidden danger rates of 2.6％,0.7％,2.9％ and 0.4％,which were all lower than those of the conventional management method,and the difference was statistically significant (x2=27.989,24.580,46.654,12.604,P<0.05). The pass rates of 92 medical equipment managers participating in the risk identification model management method in maintenance management,quality monitoring,fault handling and risk response were 95.7％ (88/92),98.9％ (91/92),92.4％ (85/92) and 97.8％ (90/92),respectively,which were higher than those of the conventional management method,the difference was statistically significant (x2=4.901,4.016,6.368,5.176,P<0.05). Conclusion:The risk identification and early warning model based on coefficient of variation weighting and extension cloud algorithm can reduce the risk level of active medical devices maintenance and repair,control the occurrence probability of potential safety hazards,and improve the support level of maintenance and repair management.
Maintenance and repairRisk identificationEarly warning interventionCoefficient of variationExtension cloudActive medical devices
维普
万方数据
