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Comprehensive Evaluation And Analysis Of Influence Factors On The Traditional Chinese Medical Treatment Service Efficiency Of General Hospital

Posted on:2013-03-18Degree:MasterType:Thesis
Country:ChinaCandidate:W J FuFull Text:PDF
GTID:2234330374494162Subject:Pharmacy
Abstract/Summary:PDF Full Text Request
Objective:since the reform and opening up, China’s health had great development, the number of national comprehensive hospitals and health personnel number, number of sickbeds, equipment, building area greatly increased, basic solved the patients "difficulties in getting the difficult, surgery, be in hospital" difficult issues, but at the same time, the hospital medical service in low efficiency, health resources waste serious problem. How to improve the comprehensive hospital service efficiency, make full use of hospital health resources and achieve the best social benefit and economic benefit, has become a health policy makers and hospital managers facing major issue. Improving hospital service efficiency, the essential problem is how to the efficiency of the hospital scientific and accurate evaluation, according to the evaluation results of affecting the service efficiency?Research methods:this is mainly used Exce12003description of national comprehensive hospital service status, spss17.0application by principal component analysis and factor analysis to the national level3general hospital service efficiency of traditional Chinese medicine, a comprehensive assessment will be comprehensive evaluation score as the dependent variable, general hospital in general health resources as the independent variable, using multiple regression analysis method, and find out the influence factors. The results:(1) general hospital of traditional Chinese medical treatment service efficiency comprehensive evaluationHome to370of the general hospital13item3efficiency index (years medical this people and medical experts this year visits, years medical discharges, TCM doctors per capita for passengers, daily diagnosis and treatment of traditional Chinese medicine physicians annual average discharges, TCM doctors hospital bed for daily per capita average day in hospital, in general, and in general average open beds number, general actual total bed day, open the actual total take general bed day, general ChuYuanZhe takes up the total number of the bed, bed, bed utilization in general general cycle times) principal component analysis and factor analysis, extraction four principal component, after factor rotation, and find out the four male factor, the comprehensive factor, quantity factor, the hospital bed(2) comprehensive hospital of traditional Chinese medical treatment service efficiency analysis of influence factorsTraditional Chinese medicine outpatient department opened several (x1), Chinese medicine physicians (x2), Chinese medicine for nurse (x3), Chinese medicine specialized technical personnel (x4), general business housing area (x5), currently square meters (x6), Chinese medicine diagnosis and treatment equipment characteristics of the Numbers (x7), Chinese characteristics (yuan) worth equipment (x8), general ChuangWeiShu (x9)9into sex index for the independent variable and score for the dependent variable (y), the multiple linear regression method, and concludes that the final "optimal" equationNamely effect factors of traditional Chinese medicine outpatient department opened several (xi), Chinese medicine physicians (x2), TCM number of professional and technical personnel (x4), general ChuangWeiShu (x9). According to the contribution of sorting, order ChuangWeiShu0for regions Conclusion:Improve the general hospital of traditional Chinese medical treatment service efficiency, the need to improve the medical service level from the start, to shorten the day in hospital as the breakthrough point, improve the turnover bed, indirect increase number of beds, reduce the funds, and reduce the burden of patients, and reduce the burden for more patients provide medical opportunity.
Keywords/Search Tags:efficiency, Comprehensive evaluation, Principal component analysis, Factor analysis, Multiple linear regression
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