↓ Skip to main content

Dynamic network data envelopment analysis for university hospitals evaluation

Overview of attention for article published in Revista de Saúde Pública, May 2016
Altmetric Badge

Mentioned by

facebook
2 Facebook pages

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
94 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Dynamic network data envelopment analysis for university hospitals evaluation
Published in
Revista de Saúde Pública, May 2016
DOI 10.1590/s1518-8787.2016050006022
Pubmed ID
Authors

Maria Stella de Castro Lobo, Henrique de Castro Rodrigues, Edgard Caires Gazzola André, Jônatas Almeida de Azeredo, Marcos Pereira Estellita Lins

Abstract

OBJECTIVE To develop an assessment tool to evaluate the efficiency of federal university general hospitals. METHODS Data envelopment analysis, a linear programming technique, creates a best practice frontier by comparing observed production given the amount of resources used. The model is output-oriented and considers variable returns to scale. Network data envelopment analysis considers link variables belonging to more than one dimension (in the model, medical residents, adjusted admissions, and research projects). Dynamic network data envelopment analysis uses carry-over variables (in the model, financing budget) to analyze frontier shift in subsequent years. Data were gathered from the information system of the Brazilian Ministry of Education (MEC), 2010-2013. RESULTS The mean scores for health care, teaching and research over the period were 58.0%, 86.0%, and 61.0%, respectively. In 2012, the best performance year, for all units to reach the frontier it would be necessary to have a mean increase of 65.0% in outpatient visits; 34.0% in admissions; 12.0% in undergraduate students; 13.0% in multi-professional residents; 48.0% in graduate students; 7.0% in research projects; besides a decrease of 9.0% in medical residents. In the same year, an increase of 0.9% in financing budget would be necessary to improve the care output frontier. In the dynamic evaluation, there was progress in teaching efficiency, oscillation in medical care and no variation in research. CONCLUSIONS The proposed model generates public health planning and programming parameters by estimating efficiency scores and making projections to reach the best practice frontier.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 1%
Unknown 93 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 12%
Student > Doctoral Student 11 12%
Student > Ph. D. Student 10 11%
Student > Bachelor 8 9%
Researcher 5 5%
Other 18 19%
Unknown 31 33%
Readers by discipline Count As %
Business, Management and Accounting 15 16%
Economics, Econometrics and Finance 10 11%
Medicine and Dentistry 10 11%
Social Sciences 7 7%
Engineering 7 7%
Other 14 15%
Unknown 31 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2016.
All research outputs
#20,655,488
of 25,373,627 outputs
Outputs from Revista de Saúde Pública
#897
of 1,139 outputs
Outputs of similar age
#245,817
of 327,276 outputs
Outputs of similar age from Revista de Saúde Pública
#13
of 19 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,276 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.