摘要:目的:优化药品单剂量调剂,加强信息化管理,优化操作流程。 方法:采用东华软件:住院药房管理系统(DTCISIP)和住院药品调剂系统(DTCISID) 实施。结果:东华软件成功实现了我院4300病床的药品单剂量调剂及各部门管理联网,优化了操作系统及流程,且系统运行稳定。结论:东华软件进行药品单剂量调剂,加强了药品的出入管理,优化了药品单剂量调剂的操作流程。Abstract: Objective: To improve united dose dispension, enhance the utilization of information technology in management of united dose dispension and optimize clinical human resource. Methods: DONG HUA software, which included DTCISIP system(system for management of medicine for inpatients) and DTCISID system(system for dispension of medicine for in-patients), was used to carry out united dose dispension. Results: United dose dispension of 4300 beds were easy to achieve by using DONG HUA software. The system worked smoothly and received lots of praise. Conclusion: The management of medicine is enhanced and clinical human resource is optimized by using DONG HUA software to carry out united dose dispension
Consultation is an important form for the diagnosis and treatment of severe diseases, and consultation management is an important content of medical management work, which directly affects the medical quality and treatment efficiency of the hospital. With the help of information network platform, our hospital has realized electronic consultation system online through scientific development, training enhancement, and safeguard mechanism improvement. The system can optimize consultation work process effectively, improve the consultation work, save manpower cost and help construction of hospital informatization.
ObjectiveTo explore the effect of continuous improvement of quality control system on the emergency treatment efficiency for patients with acute ST segment elevation myocardial infarction (STEMI) after the establishment of Chest Pain Center. MethodsWe retrospectively analyzed the differences of theory examination scores acquired by the Chest Pain Center staff one month before and after they got the system training. Moreover, we designated the STEMI patients treated between May and August 2015 after the establishment of Chest Pain Center but before optimization of process to group A (n=70), and patients treated from September to December 2015 after optimization of process to group B (n=55). Then we analyzed the differences between these two groups in terms of the time from patients' arriving to registration, the time from arriving to first order, the length of stay in Emergency Department, and even the time from door to balloon (D2B). ResultsThe scores acquired by Chest Pain Center staff before and after system training were 69.89±6.34 and 87.09±4.39 respectively, with a significant difference (P＜0.05). All the time indicators of both group A and group B were shown as median and quartile. The time from patients' arriving to registration of group A and group B was 6.0 (0.0, 11.0) minutes and 1.0 (0.0, 3.0) minutes (P＜0.05); the time from arriving to first order was 12.8 (9.0, 18.0) minutes and 5.0 (3.0, 9.0) minutes (P＜0.05); the length of stay in Emergency Department was 54.0 (44.0,77.0) minutes and 33.0 (20.0, 61.0) minutes (P＜0.05); and the time of D2B was 107.5 (89.0, 130.0) minutes and 79.0 (63.0, 108.0) minutes (P＜0.05). ConclusionAfter taking measures such as drawing lessons from the past, training staff and optimizing process continuously, we have significantly shortened the acute STEMI patients' length of stay in the Emergency Department, which has saved more time for the following rescue of STEMI patients.
In this paper, a method for dose calculation with pencil beam kernels constructed by point kernel superposition was proposed to accelerate the dose calculation during intensity optimization iteration. With this method, the direct aperture optimization method can be integrated in the planning system based on point kernel convolution/superposition model. The dose calculation time was also reduced during the iteration. From the result of the phantom and clinical patient data test, it was concluded that this method could be used for the intensity optimization of iteration dose calculation as the satisfied precision due to the optimization result coherence obtained. By implementing the method in the planning system product based on point kernel convolution/superposition model, a lot of additional research and development works for the pencil beam dose calculation model as well as the product maintenance cost can be avoided.
Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.
Mammography imaging is one of the most demanding imaging modalities from the point of view of the balance between image quality (the visibility of small size and/or low contrast structures) and dose (screening of many asymptomatic people). Therefore, since the introduction of the first dedicated mammographic units, many efforts have been directed to seek the best possible image quality while minimizing patient dose. The performance of automatic exposure control (AEC) is the manifestation of this demand. The theory of AEC includes exposure detection and optimization and also involves some accomplished methodology. This review presents the development and present situation of spectrum optimization, detector evolution, and the way how to accomplish and evaluate AEC methods.
Sudden cardiac arrest is one of the critical clinical syndromes in emergency situations. A cardiopulmonary resuscitation (CPR) is a necessary curing means for those patients with sudden cardiac arrest. In order to simulate effectively the hemodynamic effects of human under AEI-CPR, which is active compression-decompression CPR coupled with enhanced external counter-pulsation and inspiratory impedance threshold valve, and research physiological parameters of each part of lower limbs in more detail, a CPR simulation model established by Babbs was refined. The part of lower limbs was divided into iliac, thigh and calf, which had 15 physiological parameters. Then, these 15 physiological parameters based on genetic algorithm were optimized, and ideal simulation results were obtained finally.
The present study was aimed at the optimal solution of the main muscular force distribution in the lower extremity during standing balance of human. The movement musculoskeletal system of lower extremity was simplified to a physical model with 3 joints and 9 muscles. Then on the basis of this model, an optimum mathematical model was built up to solve the problem of redundant muscle forces. Particle swarm optimization (PSO) algorithm is used to calculate the single objective and multi-objective problem respectively. The numerical results indicated that the multi-objective optimization could be more reasonable to obtain the distribution and variation of the 9 muscular forces. Finally, the coordination of each muscle group during maintaining standing balance under the passive movement was qualitatively analyzed using the simulation results obtained.
In the present study, packaging system composed of pAAV-CMV-GFP, pAAV-RC and pHelper were transfected into human embryonic kidney 293 cells (HEK293 cells) mediated by polyethyleneimine (PEI) to explore an optimal transfection condition. Different total plasmid DNA dosages (1, 2, 3, 4, 5, 6μg) and different PEI/Plasmid ratios (1:1, 3:1, 5:1, 7:1) were tested with detection of green fluorescence protein (GFP) with ImagePro Plus6.0 Software. Then transfection efficiency of the optimized transfection system was further observed for different time periods(12, 24, 36, 48, 60, 72 h). The results showed that total plasmid dosage of 4μg/well with PEI/plasmid ratio of 3:1~5:1 was an efficient transfection condition. Transfection efficiency-time curve was an S-shaped curve. Transfection efficiency reached a plateau at 60 h after transfection. The optimized conditions for PEI-mediated transfection at the optimal time result in enhanced transfection efficiency of triple plasmid into HEK293 cells.