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AN EVALUATION OF THE EFFICIENCY OF R...

HUANG, YUEH-GUEY LAURA.

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AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS./
作者:	HUANG, YUEH-GUEY LAURA.
面頁冊數:	276 p.
附註:	Source: Dissertation Abstracts International, Volume: 48-02, Section: B, page: 0404.
Contained By:	Dissertation Abstracts International48-02B.
標題:	Health Sciences, Public Health. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=8711183

AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS.
HUANG, YUEH-GUEY LAURA.

AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS. - 276 p.

Source: Dissertation Abstracts International, Volume: 48-02, Section: B, page: 0404.

Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 1986.

Data Envelopment Analysis (DEA) is a technique which uses non-parametric deterministic mathematical programming to optimize the relative efficiency ratio in each decision-making unit (DMU) that utilizes similar inputs to produce similar outputs. It produces a summary scalar efficiency ratio for each DMU and identifies the amount of inefficiency for each variable in each inefficient DMU.Subjects--Topical Terms:

1017659
Health Sciences, Public Health.

AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS.
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245 1 3 $a AN EVALUATION OF THE EFFICIENCY OF RURAL PRIMARY CARE PROGRAMS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS.
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500 $a Source: Dissertation Abstracts International, Volume: 48-02, Section: B, page: 0404.
502 $a Thesis (Dr.P.H.)--The University of North Carolina at Chapel Hill, 1986.
520 $a Data Envelopment Analysis (DEA) is a technique which uses non-parametric deterministic mathematical programming to optimize the relative efficiency ratio in each decision-making unit (DMU) that utilizes similar inputs to produce similar outputs. It produces a summary scalar efficiency ratio for each DMU and identifies the amount of inefficiency for each variable in each inefficient DMU.
520 $a This study examined how DEA could be applied to a heterogeneous set of rural primary health care programs. Data came from 193 observations in a nationwide evaluation survey of rural primary health care programs in 1980, of which 77 observations could be used in this study. The outputs used in this study model were the number of medical encounters provided by physicians, new health practitioners (NHPs), and nurses separately. The number of total encounters, which included medical, dental and other health encounters, was used as an uncontrollable output to account for program capacity.
520 $a The input variables used were chosen from previous studies, and after correlation analysis among inputs and outputs, and stepwise regression analysis for each chosen output. The inputs of program resources were measured by the number of FTEs for physicians, NHPs, and nurses separately. Indirect (exogenous) inputs were measured by the service area population size, the percentage of users under 4 years old, and program age. NHP productivity was another uncontrollable input in the reduced model for physician service.
520 $a Four empirical models, including one multiple output model and three reduced models for physician, NHP, and nurse services, were then formulated. Disregarding the 35 outliers generated from DEA, the utility of DEA was shown through the comparisons of these four models, ratio and simple linear regressions. The efficiency classification was relatively consistent in all measures by different methods. The t-test, chi-square, and logistic regression were used to examine the relationships between program efficiency and certain other variables. Those programs located in small population areas, or operated by organized group practices were found more efficient than others.
520 $a Therefore, DEA is useful in assessing the inefficiency in each relatively homogeneous unit, and yields more informative assessments than do other methods. However, DEA was deficient in assessing outliers in this heterogeneous population of DMUs.
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