Objective:To explore the effect of continuous improvement model with Definition,Measure,Analyze,Improve,and Control(DMAIC)on the management for equipment of diagnosing and treating chronic respiratory diseases.Method:To address the management issues of diagnosis and treatment equipment for chronic respiratory diseases,multiple management strategies were formulated on the basis of definition,measurement,analysis,improvement and control stages of the DMAIC continuous improvement model.Selected 80 diagnostic and therapeutic equipment used at the department of respiratory in People's Hospital of Xinjiang Uygur Autonomous Region from August 2021 to July 2023.The conventional management method was used to manage them during August 2021 to July 2022,and the management method of DMAIC continuous improvement management model(continuous improvement management method)for chronic respiratory diseases was adopted to manage them during August 2022 to July 2023.The ratio of failure equipment,score of risk assessment,qualified rate of examination,and satisfaction score of users who operated equipment by using the two kinds of management methods were compared.Result:The average failure ratios of shutdown due to failure,injury that caused external environment,abnormal self-examination at power on,unqualified quality inspection,and poorly operational quality in treatment in equipment of diagnosing and treating respiratory disease by using continuous improvement management method were respectively(1.58±0.51)%,(0.34±0.10)%,(0.65±0.20)%,(2.08±0.53)%and(1.61±0.52)%,which were all lower than those by using conventional management method,and the differences of them were significant(t=14.512,11.205,24.354,17.169,17.663,P<0.05).The degree of detectability,severity,and probability scores of occurrence of the equipment by using continuous improvement management method were lower than those of conventional management method,and the differences were statistically significant(t=10.478,6.930,9.407,P<0.05).The qualified rate of equipment inspection of continuous improvement management method was higher than that of conventional management method,and the difference was statistically significant(x2=9.642,P<0.05).The performance,cleanliness of equipment,and timeliness scores of operators,who used equipment,for diagnosis and treatment equipment that were managed by using continuous improvement management method were all higher than those by using conventional management method,and the differences were statistically significant(t=2.204,2.268,2.604,P<0.05).Conclusion:The DMAIC continuous improvement management model method for equipment of diagnosing and treating chronic respiratory disease can significantly improve management level for equipment,and reduce failure risk of equipment,and increase the utilization efficiency of equipment and the users'satisfaction.

Objective:To explore the effect of continuous improvement model with Definition,Measure,Analyze,Improve,and Control(DMAIC)on the management for equipment of diagnosing and treating chronic respiratory diseases.Method:To address the management issues of diagnosis and treatment equipment for chronic respiratory diseases,multiple management strategies were formulated on the basis of definition,measurement,analysis,improvement and control stages of the DMAIC continuous improvement model.Selected 80 diagnostic and therapeutic equipment used at the department of respiratory in People's Hospital of Xinjiang Uygur Autonomous Region from August 2021 to July 2023.The conventional management method was used to manage them during August 2021 to July 2022,and the management method of DMAIC continuous improvement management model(continuous improvement management method)for chronic respiratory diseases was adopted to manage them during August 2022 to July 2023.The ratio of failure equipment,score of risk assessment,qualified rate of examination,and satisfaction score of users who operated equipment by using the two kinds of management methods were compared.Result:The average failure ratios of shutdown due to failure,injury that caused external environment,abnormal self-examination at power on,unqualified quality inspection,and poorly operational quality in treatment in equipment of diagnosing and treating respiratory disease by using continuous improvement management method were respectively(1.58±0.51)%,(0.34±0.10)%,(0.65±0.20)%,(2.08±0.53)%and(1.61±0.52)%,which were all lower than those by using conventional management method,and the differences of them were significant(t=14.512,11.205,24.354,17.169,17.663,P<0.05).The degree of detectability,severity,and probability scores of occurrence of the equipment by using continuous improvement management method were lower than those of conventional management method,and the differences were statistically significant(t=10.478,6.930,9.407,P<0.05).The qualified rate of equipment inspection of continuous improvement management method was higher than that of conventional management method,and the difference was statistically significant(x2=9.642,P<0.05).The performance,cleanliness of equipment,and timeliness scores of operators,who used equipment,for diagnosis and treatment equipment that were managed by using continuous improvement management method were all higher than those by using conventional management method,and the differences were statistically significant(t=2.204,2.268,2.604,P<0.05).Conclusion:The DMAIC continuous improvement management model method for equipment of diagnosing and treating chronic respiratory disease can significantly improve management level for equipment,and reduce failure risk of equipment,and increase the utilization efficiency of equipment and the users'satisfaction.

Objective:To construct a prediction model for the operation quality of medical equipment based on mutual information particle swarm optimization(PSO)-long short-term memory(LSTM)neural network to assist the intelligent management of diagnosis and treatment equipment for chronic respiratory diseases.Methods:The basic data,usage data,maintenance data and performance data of equipment were collected for denoising and standardized processing,and a PSO-LSTM prediction model was constructed,and intelligent management plans for equipment use,maintenance,repair and scrapping were formulated.A total of 139 medical equipment in clinical use in the Respiratory Department of the People's Hospital of Xinjiang Uygur Autonomous Region from August 2019 to July 2023 was selected.67 devices from August 2019 to July 2021 adopted the experience management mode,and 72 devices from August 2021 to July 2023 adopted the intelligent management mode.The prediction accuracy of traditional recurrent neural network(RNN),LSTM neural network model training and test set,and PSO-LSTM neural network model were calculated.The equipment management quality of the two management modes and the satisfaction of equipment operators,technical support personnel,patients and their families with the two management modes were compared.Results:The mean absolute percentage error(MAPE)and root mean square error(RMSE)values of the prediction accuracy of the PSO-LSTM model training set are 0.014 and 0.008,respectively,and the test set is 0.032 and 0.018,respectively,both lower than RNN and RMSE.The failure rate,start-up rate,management cost increase,maintenance implementation rate and scrap compliance rate of the intelligent management mode were(0.99±0.85)times/year,(95.74±2.16)%,(1.72±1.28)%,(96.49±1.97)%and(97.59±1.49%),respectively,and the increase of fault frequency and management cost were lower than those of the experience management mode,while the start-up rate,maintenance implementation rate and scrap compliance rate were than those of the experience management mode,the difference was statistically significant(t=3.297,3.469,2.394,4.187,3.503,P<0.05).The satisfaction scores of equipment operators and technical support personnel and patients and their families on the performance,operating quality,management method,management cost and diagnosis and treatment effect of the equipment of the intelligent management mode were(94.73±1.85),(93.38±3.15),(93.48±2.02),(94.35±2.34)and(95.14±2.07),respectively,which were all higher than those of the experience management,the difference was statistically significant(t=4.131,3.827,5.716,3.430,3.173,P<0.05).Conclusion:PSO-LSTM neural network prediction model can more accurately evaluate the operating status of medical equipment,improve the clinical operation quality of medical equipment and improve clinical service satisfaction.