Objective:To build a big data analysis model of comprehensive performance of medical equipment,so as to improve the management efficiency and fine management level for medical equipment of hospital.Methods:The big data analysis model of comprehensive performances of medical equipment was developed through building a data integration platform,and integrating multi-business system data of hospital,and including quantified social benefit data and relative data of the process of procurement and maintenance,which established index system of performance analysis with multi-dimensions and multi-layers.It realized the presentation of evaluation results of performance of medical equipment in the form of visual data reports.Results:The big data analysis model for the comprehensive performance of medical equipment has set a system with more than 10 indicators,which included social-benefit indicators,evaluation indicators of comprehensive performance,income indicators,and the indicator of recovery period of investment,and visual report with 35 pages.Each layer of the performance of equipment was analyzed from 35 angles,which realized a comprehensive,multi-dimensional and refined evaluation for the performance of medical equipment of hospital.Its'agile,easy-to-use,efficient response and automation features can significantly improve work efficiency of business.The model realized automatic data collection through interfaces and other means,and reduced manual input errors,which data accuracy rate was＞99%.The results of testing the performance data of equipment for three consecutive years by using this model indicated that the consuming time of single calculation of business system of hospital was shortened from＞20 min to 5-8 s.Conclusion:The big data analysis model of comprehensive performance of medical equipment can help hospital to identify,prevent and control risks in advance,and improve the efficiency of internal control,and enhance management efficiency and fine management level for hospital,and provide support for management decision for medical equipment of hospital.

Objective:To build a big data analysis model of comprehensive performance of medical equipment,so as to improve the management efficiency and fine management level for medical equipment of hospital.Methods:The big data analysis model of comprehensive performances of medical equipment was developed through building a data integration platform,and integrating multi-business system data of hospital,and including quantified social benefit data and relative data of the process of procurement and maintenance,which established index system of performance analysis with multi-dimensions and multi-layers.It realized the presentation of evaluation results of performance of medical equipment in the form of visual data reports.Results:The big data analysis model for the comprehensive performance of medical equipment has set a system with more than 10 indicators,which included social-benefit indicators,evaluation indicators of comprehensive performance,income indicators,and the indicator of recovery period of investment,and visual report with 35 pages.Each layer of the performance of equipment was analyzed from 35 angles,which realized a comprehensive,multi-dimensional and refined evaluation for the performance of medical equipment of hospital.Its'agile,easy-to-use,efficient response and automation features can significantly improve work efficiency of business.The model realized automatic data collection through interfaces and other means,and reduced manual input errors,which data accuracy rate was＞99%.The results of testing the performance data of equipment for three consecutive years by using this model indicated that the consuming time of single calculation of business system of hospital was shortened from＞20 min to 5-8 s.Conclusion:The big data analysis model of comprehensive performance of medical equipment can help hospital to identify,prevent and control risks in advance,and improve the efficiency of internal control,and enhance management efficiency and fine management level for hospital,and provide support for management decision for medical equipment of hospital.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

Objective To observe any effect when eye acupuncture is combined with computer-assisted cognition training to ameliorate cognitive dysfunction after stroke.Methods Stroke patients with cognitive impairment were randomly divided into an eye acupuncture group (n=30),a computer group (n=30) and a combination group (n=30).The eye acupuncture group was given eye acupuncture,the computer group was given rehabilitative cognition training with specialized equipment and the combination group was given both eye acupuncture and the computeraided training.The treatment lasted 2 months.The Loewenstein occupational therapy cognitive asessment (LOTCA)and the modified Barthel index were used to evaluate the patients' cognitive function and daily life ability before and after treatment.Results Before training there was no significant difference in average LOTCA and MBI results among the three groups.After 2 months of training,all three groups had significantly higher scores in both evaluations.Moreover,the combination group's average score on organization ability,perceptual ability,thinking operation ability and concentration,and also their average total score and MBI score were significantly better than those of the other two groups.Conclusions Eye acupuncture combines synergistically with cognitive rehabilitation training to ameliorate cognitive dysfunction after stroke.Together they promote cognition,ADL ability and an early return to normal family and social life better than either alone.

Objective:To evaluate the effectiveness of psychological intervention on improving the survival quality of patients with endometrial cancer during the operation. Methods:Sixty-two patients included in the eligible subjects were randomly divided into experimental group (n=31) and control group (n=31). The control group was given routine nursing intervention and the experimental group was given psychological intervention. Admission, discharge and discharge after one year follow-up measurement of quality of life were recorded. Results:Two patients discharged from the SF-36 scale score difference were statistically significant (t=11.3152, P<0.05), statistically significant long-term effect one year after the score difference (t=9.2025, P<0.05), the test group was better than control group. Conclusion:Psychological intervention is effective to improve the quality of life of patients with endometrial cancer during operation period and long-term.