Analysis of ergonomics problems contact-centers

The analysis of the ergonomic problems of the contact center. It allocates main factors of influence on man- operator

Improvement of the reduction in catastrophic health expenditure in China’s public health insurance

<div><p>This study aimed to locate the contributing factors of Catastrophic Health Expenditure (CHE), evaluate their impacts, and try to propose strategies for reducing the possibilities of CHE in the context of China’s current public health insurance system. The financial data of all hospitalization cases from a sample hospital in 2013 were gathered and used to determine the pattern of household medical costs. A simulation model was constructed based on China’s current public health insurance system to evaluate the financial burden for medical service on Chinese patients, as well as to calculate the possibilities of CHE. Then, by adjusting several parameters, suggestions were made for China’s health insurance system in order to reduce CHE. It’s found with China’s current public health insurance system, the financial aid that a patient may receive depends on whether he is from an urban or rural area and whether he is employed. Due to the different insurance policies and the wide income gap between urban and rural areas, rural residents are much more financially vulnerable during health crisis. The possibility of CHE can be more than 50% for low-income rural families. The CHE ratio can be dramatically lowered by applying different policies for different household income groups. It’s concluded the financial burden for medical services of Chinese patients is quite large currently, especially for those from rural areas. By referencing different healthcare policies in the world, applying different health insurance policies for different income groups can dramatically reduce the possibility of CHE in China.</p></div

Hospitalization expenses by department in the sample hospital in 2013.

<p>Hospitalization expenses by department in the sample hospital in 2013.</p

Regions with significant volume percentage increment (A, red color) in gray matter, and volume decrement in gray matter (A, blue color) as well as white matter volume reduction (B, blue color) in autism compared to controls (P<0.01).

<p>Significant cortical thickness increment in percentage change in autism compared to controls is shown in C with red color (P<0.01). Both medial and lateral views on sagittal surface in the left and right hemispheres of an individual control subject are shown for gray matter volume (A), white matter volume (B) and cortical thickness (C) comparison between autistic children and controls. The percentage change of volume in autism was calculated as the mean difference between volumes of two groups normalized with individual supratentorial volume and scaled by the mean normalized volume of control group.</p

Frequency of hospitalization expenses after logarithm in the sample hospital in 2013.

<p>Frequency of hospitalization expenses after logarithm in the sample hospital in 2013.</p

Fitting household income by lognormal distribution.

<p>Fitting household income by lognormal distribution.</p

Significant correlations between each single MRI quantitative metric and phenotypic tests in children with autism spectrum disorder (P<0.05), adjusted with number of available patients for each test.

<p>Note: N is the total number of voxels and Z is the average correlational z-value computed from the functional connectivity map (fcMRI) of each seed using a threshold of cluster corrected P<0.05.</p><p>*Indicates a significant difference with corrected P<0.05 after Bonferroni adjustment, with multiplication factors determined by both the number of category tests applied (x5 in this study) and the number of sub-domain tests (e.g., x10 for ADOS, x3 for IQ, and x15 for VABS) of each category.</p

Significant regional volume differences (P<0.01) comparing children with autism spectrum disorder (ASD) to age-matched typically developing (TD) children after supratentorial volume normalization.

<p>Note-Data (V1, V2) are mean brain volumes after normalization to the supratentorial volume with a scale factor of 1000, no unit.</p><p>**Calculated with two-sample t test to obtain original p-value (shown with P<0.01) between two groups, and with Bonferroni multiple region correction (x8 factor given 4 brain lobes in two hemispheres).</p><p>*Indicates a significant difference with corrected P<0.05 after Bonferroni adjustment.</p><p>Δ1 Indicates percentage change between volume of ASD children (V2) and volume of TD children (V1), calculated as Δ1 =  (V2-V1)/V1*100%.</p

Margin curves showing the cumulative probability of difference between true positive (sensitivity) and false positive (1-specificity) as a function of instance number (i.e. subject number) for 4-feature classification (A) and 6-feature classification (B) based on full dataset.

<p>As expected, the 6-feature classifier performed slightly better than 4-feature classifier with faster convergence and more margins maintained.</p

Figure 4

<p>A: Co-variance matrix of 22 quantitative imaging features showed highly-correlated structural metrics (i.e. volumetry) of 9 regions, and strong association between average Z-value and voxel number N-value of fcMRI for each region (or global metric). B: principal component analysis decomposition of the covariance matrix showing most (>95%) data variation (information) was contained with 4 primary components (pink color) and 99% data information was contained in 6 primary components (red color). C: The four imaging features selected via mRMR criteria were right caudate volume (1), the fcMRI average Z values seeded from bilateral caudate (2), bilateral IFG pars opercularis (3) and IFG pars triangularis (4). Together with the four imaging features selected, the DMN average Z (5) and the VMHC average Z (6) were the other two imaging features selected for 99% criteria.</p