25 June 2015, Volume 22 Issue 3

    

• Select all
  |

Orignal Article
• Select
  Study on equity of allocation of medical resources in Gansu

  Li Xiping, Liu Weiwei

  journal1. 2015, 22(3): 161-164. https://doi.org/10.3969/j.issn.1006-5253.2015.03.001

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To analyze the equity of health resource allocation in different cities of Gansu province. Methods The health resource allocation data of 14 cities of Gansu Province were selected and the equity of health resource allocation by using the Lorenz curve, Gini coefficient and Theil index. Results Through calculation, the Gini coefficient of the allocation in fiscal subsidies, medical bed allocation and health personnel was lower than 0.2, showing that there was equity in the policy for different regions, and the health department of Gansu Province didn′t adopt different policies for different geographical position. Comprehensive analysis of Theil index showed that Theil index of financial assistance and medical institutions in the health resources of Gansu Province was 0.111 7, which was the highest, but the allocation of beds was the most unfair, with the Theil index 0.093 3. On the whole, compared with the whole country, the allocation of financial subsidies, the number of beds and number of health personnel in Gansu Province medical institutions was fair, which was consistent with the research conclusion of the Gini coefficient. Conclusion We should combine the market regulation with government regulation, strengthen the layout and construction of medical institutions in cities, and make clear the nature and function of basic medical services.
• Select
  Investigation and influence factor analysis of satisfaction of inpatients in tertiary teaching hospitals

  Li Ruibo, Zhang Qiao, Wu Cong, Zheng Ce, Xiang Jing

  journal1. 2015, 22(3): 165-166. https://doi.org/10.3969/j.issn.1006-5253.2015.03.002

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To investigate the satisfaction of inpatients in tertiary teaching hospitals and analyze the influence factors of satisfaction. Methods The survey was done with a stratified sampling method to randomly select the samples. The questionnaires were used to collect the data and the statistical methods were simple and multivariate linear regression analysis. Results The satisfaction of inpatients in tertiary teaching hospitals was high and the average score was 92.94±11.81. The satisfaction of rural inpatients was lower than urban inpatients. The more the household average month income, the higher the satisfaction. The satisfaction of patients who were firstly admitted in hospital was higher than that of the others. Conclusion The coverage of hospital indicating signs should be increased. The hospital guide service and the medical charge management should be improved. The hospitals should increase the publicity of booking register, in order to improve the satisfaction of rural and lower income inpatients.
• Select
  Meta-analysis of the risk factors on hospital infection in intensive care unit

  Lin Jianchao, Wang Linghong, Zhou Xinping

  journal1. 2015, 22(3): 170-172. https://doi.org/10.3969/j.issn.1006-5253.2015.03.004

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To provide evidence for decision-making on hospital infection prevention through studying the main risk factors related to the incidence of hospital infection. Methods Meta-analysis was used to comprehensively and quantitatively evaluate the 11 case-control studies on risk factors of hospital infection. With Review Manager 5.2 we carried out consistency check, pooled OR and calculated 95% confidence interval. Results The pooled odds ratio values were 1.73(95%CI:1.29~2.31)for patients aged 60 and above, 7.43(95%CI:2.02~27.34)for those with 7 hospitalization days and above, 4.37(95%CI:1.74~10.96)for those with tracheostomy/intubation, 3.83(95%CI:1.89~7.79)for those with catheterization, 3.58(95%CI:1.99~6.42)for those with deep venous catheter, 2.51(95%CI:1.45~4.36)for those with 3 types of antibiotic use and above, 3.87(95%CI:2.06~7.26)for those in coma, and 2.42(95%CI:1.39~4.23)for those with immune agents. Conclusion The major risk factors influencing the incidence of hospital infection in China were aging, days in hospital, tracheostomy/intubation, catheterization, deep venous catheter, types of antibiotic use, coma, and immune agents, and the results could provide scientific basis for hospital infection intervention.
• Select
  Comparison of BP neural network with logistic regression in influencing factor analysis of lung cancer hospitalization costs

  Sun Lin, Qi Aiqin, Xu Tianhe, Gao Yong

  journal1. 2015, 22(3): 173-175. https://doi.org/10.3969/j.issn.1006-5253.2015.03.005

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To analyze the costs of hospitalization of patients with lung cancer to understand the influencing factors of lung cancer hospitalization costs, and to provide a relatively consummate evaluation method for lung cancer cost studies. Methods By Mathlab 7.1, we took age group, occupation and other factors as the input neurons, hospitalization costs the output neuron to simulate the analog neural network model, while sensitivity of the derived variables to the neural network model was used to evaluate the factors of hospitalization costs effect. At the same time, we used SAS 8.2 to build the logistic regression model, and made a comparison between two model results. Results The two model results showed that the top three factors of the hospital cost impact were number of days of hospitalization, drugs, and discharge outcome of the situation. Conclusion BP neural network model and logistic regression can be well applied to the analysis of influencing factors of hospitalization costs, but because of the BP neural network model has a wider range of conditions of application, more in-depth information mining and full depth analysis of the influencing factors, and thus has a better development and utilization prospects.
• Select
  Curative effect evaluation study on prognosis of acute stroke patients carrying out early comprehensive rehabilitation training

  Zhu Xueli

  journal1. 2015, 22(3): 176-178. https://doi.org/10.3969/j.issn.1006-5253.2015.03.006

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To explore the effect of prognosis of acute stroke patients carrying out early comprehensive rehabilitation training, and the risk factors of prognosis outcome of stroke. Methods One hundred and twenty patients with acute stroke were randomly divided into control group (n=60) and treatment group (n=60). The control group was carried out normal treatment, and the intervention group was given early comprehensive rehabilitation training measures on the basis of the control group. The indexes of total effective rate were compared between the two groups, and risk factors of not good outcome of prognosis were screened with multiple factors analysis method. Results After 3 months rehabilitation treatment, the total effective rate in the treatment group was 91.7%, much higher than the control group (76.7%, P<0.05). Multiple factors analysis result showed that age (OR=1.745), hypertension (OR=3.931), cerebral arteriostenosis (OR=5.302), late visit (OR=2.366), without carrying out early rehabilitation training (OR=8.207) and NIHSS score (OR=2863) were the risk factors of unfavourable prognosis of stroke. Conclusion Carrying out early comprehensive rehabilitation training measures for acute stroke patients might obviously raise clinical curative effects. There are many independent risk factors affecting the prognosis of stroke patients, so we should formulate effective preventive measures to aim directly at these risk factors.
• Select
  Comprehensive evaluation of the hospital medical quality based on principal component analysis

  Zeng Suqin

  journal1. 2015, 22(3): 179-182. https://doi.org/10.3969/j.issn.1006-5253.2015.03.007

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective In order to understand the medical quality of Jieyang secondary comperhensive hospitals, to sort nine hospitals based on comprehensive levels for hospital managers to identify weak links, and to to provide basis for raising the level of integrated management. Methods We selected representative 15 indexes to build the comprehensive evaluation index system of medical treatment quality, processed the original data with standards, using excel and spss 17.0 software principal component analysis, and calculated the medical quality comprehensive evaluation value of 9 hospitals and sorted them. Results From 15 indicators, three common factors were extracted and their cumulative variance contribution rate was 86.943%. According to the comprehensive evaluation value of principal component analysis, we ranked in general the hospitals, with I hospital the best, H hospital the second, and B hospital the worst. The ranking was consistent with the change direction of the original data. Conclusion Principal component analysis can objectivelyand comprehensively reflect changes of the medical indexes. The evaluation method can accurately reflect the change trend of hospital medical work quality, and has great application value in the comprehensive evaluation.
• Select
  Construction and application of an ARIMA model for predicting the number of outpatient visits in a Xinjiang tertiary general hospital

  Li Jing, Chen Yingying, Huo Yongsheng, Peng Qiaojun

  journal1. 2015, 22(3): 183-185. https://doi.org/10.3969/j.issn.1006-5253.2015.03.008

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To study change rules of outpatient visits, to forecast the change tendency,and thus to provide a basis for hospital outpatient management decisions. Methods Combined with sequence stability with long-term trend and seasonal effect, by using SPSS 17.0 to establish ARIMA model. Results After the screening we got the optimal model of ARIMA (1,1,0)×(0,1,1)₁₂, and made autocorrelation diagram of the residual series. The Results showed that the selected model was proper. The predicted number of outpatient visits in 2013 was 2.2674 million, while the actual number was 2.3099 million, with the relative error 4.4%. Conclusion There was a seasonal change and a growing trend for the number of outpatient visits, suitable for using ARIMA model, and the model prediction effect was good. It could provide basis for operation management and decision-making for the hospital leadership, and effectively guide the work plan and arrangement. The model application has effectiveness and generalizability in the outpatient management.
• Select
  Analysis and evaluation of the hospitalization appropriateness of a tertiary hospital based on N-AEP

  Li Ying, Chen Li

  journal1. 2015, 22(3): 186-189. https://doi.org/10.3969/j.issn.1006-5253.2015.03.009

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To analyze and evaluate the hospitalization appropriateness of a tertiary hospital between 2011 and 2013 through appropriateness evaluation N-protocol (N-AEP) in order to evaluate the inappropriate hospitalization days proportion in diagnosis and treatment process, and to predict the average hospitalization days that should be shorten. Methods According to the data reviewed standard of the tertiary hospital, we selected acute myocardial infarction, chronic obstructive pulmonary disease, digestive tract hemorrhage (complications), and renal failure in eighteen hospitalized key diseases of circulatory system, respiratory system, digestive system, genitourinary system during 2011 to 2013. We took 2 926 hospitalized days from 240 medical records as samples, used N-AEP to carry on the retrospective evaluation and research, and used a delay tool of hospitalization evaluation to carry on analysis of attribution. Results Altogether 337 hospitalization days were judged inappropriateness, with the inappropriate hospitalization rate 11.52%. Conclusion The inappropriate hospitalization rate is in a middle level compared with the related records inland China. The average hospitalization days can be respectively shortened to 9.98 days, 11.78 days, 10.78 days, and 10.60 days. Better hospitalization appropriateness will be achieved if comprehensive measures are carried out in the hospital.
• Select
  Investigation on job burnout among staff of a military hospital

  Wen Meilin, Ding Tianpeng, Shi Genlin, Chen Weiwei

  journal1. 2015, 22(3): 190-193. https://doi.org/10.3969/j.issn.1006-5253.2015.03.010

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To understand the burnout situation of a military hospital medical personnel, analyze the main factors of burnout, and provide a basis for the development of effective prevention and intervention strategies to improve the efficiency of medical personnel. Methods By using the method of comprehensive survey, a questionnaire survey was conducted on a military hospital of 526 medical personnel, and the collected data were input to Excel, using SPSS statistical software to analyze the status of occupation burnout. Results There were (45.70 ± 13.936) points for the military hospital medical personnel burnout, (11.39 ± 7.621) points for emotional exhaustion, (7.69 ± 6.297) points for deindividuation and (26.65 ± 8.808) points for occupation efficacy. There were 288 people (59.1%) with low burnout, 193 people (39.6%) with severe burnout, and 6 people (1.2%) with more severe burnout. Conclusion The military hospital medical personnel is given priority to moderate occupation burnout, while for the comprehensive occupation burnout, age and working time are main factors. In the dimension of emotional exhaustion, age, occupation and working time are main factors. In the dimension of deindividuation, age, active duty, occupation, education level and working time are main factors.
• Select
  Statistical analysis of blood routine test error and influencing factors

  Yang Yibo

  journal1. 2015, 22(3): 194-196. https://doi.org/10.3969/j.issn.1006-5253.2015.03.011

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To investigate clinical blood test error and influencing factors. Methods Selected from 2014 July to 2015 January in our hospital treatment of 304 cases of patients blood routine examination samples, and detected blood routine samples in the different storage temperature, storage time, anticoagulant concentration conditions. Results When the storage temperature conditions for the normal temperature(6～25℃), white blood cell content was (4.42±0.63) ×10⁹/L, significantly higher than that of 4℃ and platelet content was (210.41±7.93)×10⁹/L, significantly lower than that of 4℃(P<0.05). The differences of red blood cell and hemoglobin in the conventional temperature and 4℃ was no statistical significance(P>0.05). After placing 4h white blood cell content was (2.92±0.77)×10⁹/L, significantly lower than that of real-time and 2h detection value, and platelet content was (303.22±21.04)×10⁹/L, significantly higher than that of real-time and 2h detection value (P<0.05). Real-time detection of red blood cell and hemoglobin content were (4.45±0.52)×10⁹/L and (119.02±23.06)g/L, significantly lower than that of 2h and 4h(P<0.05). When the anticoagulants concentration was 2.4mg/ml, MCV and Hct were (89.32±7.05) fl and (0.441±0.012), significantly higher than that of the concentration of 1.6mg/ml and 2.0mg/ml. Conclusion Blood samples storage temperature, determination time and the anti coagulant concentration will affect the blood samples detection value, so the medical staff should test strictly in accordance with the relevant provisions.
• Select
  Analysis of the seasonal variation of outpatient visits of a tertiary general hospital in Guangxi from 2004 to 2013

  Zhang Fan

  journal1. 2015, 22(3): 197-200. https://doi.org/10.3969/j.issn.1006-5253.2015.03.012

  Abstract ( ) Download PDF ( )   Knowledge map   Save

  Objective To investigate the seasonal variation of outpatient visits of a tertiary general hospital in Guangxi from 2004 to 2013. Methods Using statistical data of a hospital clinic visits in various months from 2004 to 2013 to make statistical tables. Simple average method was used for seasonal variation analysis to calculate the seasonal index of each month (season rate). The forecasting model of ARIMA (1,0,1)×(0,1,1)₁₂ was established using residual error analysis and least squares method according to the sequence stability, and testing for linear trend equation, and finally we had the interval forecasting to the 95% confidence level. Results The outpatient visits of the average monthly were 123 339 patients in 10 years, with the most 217 065 patients in July 2013, the least 54 001 patients in January 2004, The minimum and maximum range of fluctuation range (range) was equal to 163 064 in 10 years. The outpatient amount each year in a hospital was that the highest month was 7 times in July and 2 times in August in 10 years and the lowest month was 6 times in February, and 4 times in January. The forecasting model AIC was 21.16, the SBC was 21.24, and the relative predictive error of predicting the outpatient visits was 6.9%. Conclusion The distribution of a hospital outpatient amount shows a strong seasonal variation. Using this analysis as a basis for clinic appointment, we can arrange outpatient service medical personnel reasonably, strengthen the out-patient power in peak, and shorten the treatment time of patients. That would be one of ways to coordinate reasonably the treatment time and the arrangement between doctors and patients.