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Article Contents

Introduction, declaration, data availability, acknowledgements, reference list, data envelopment analysis applications in primary health care: a systematic review.

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Izabela Zakowska, Maciek Godycki-Cwirko, Data envelopment analysis applications in primary health care: a systematic review, Family Practice , Volume 37, Issue 2, April 2020, Pages 147–153, https://doi.org/10.1093/fampra/cmz057

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Strategic management of primary health care centres is necessary for creating an efficient global health care system that delivers good care.

To perform a systematic literature review of the use of data envelopment analysis in estimating the relative technical efficiency of primary health care centres, and to identify the inputs, outputs and models used.

PubMed, MEDLINE Complete, Embase and Web of Science were searched for papers published before the 25 March 2019.

Of a total of 4231 search results, 54 studies met the inclusion criteria. The identified inputs included personnel costs, gross expenditures, referrals and days of hospitalization, as well as prescriptions and investigations. Outputs included consultations or visits, registered patients, procedures, treatments and services, prescriptions and investigations. A variety of data envelopment analysis models used was identified, with no standard approach.

Data envelopment analysis extends the scope of tools used to analyse primary care functioning. It can support health economic analyses when assessing primary care efficiency. The main issues are setting outputs and inputs and selecting a model best suited for the range of products and services in the primary health care sector. This article serves as a step forward in the standardization of data envelopment analysis, but further research is needed.

We identify various approaches to effective assessment of primary care.

Data envelopment analysis is widely used in primary care.

A variety of analysis models exist, with no single standard.

Many countries have a growing demand for health care services and this is accompanied by growing expenditure ( 1–3 ). Measuring the relative efficiency of health care systems, including primary health care (PHC) centres, creates a baseline for evaluating the management of their resources and for benchmarking their productiveness against others. Moreover, better use of health care resources could lead to the provision of better health services at the basic level. Recent years have seen a growth in interest in the use of quantitative methods for comparing the efficiency of health care systems ( 2 , 4 , 5 ), and many approaches for estimating the efficiency of various levels of health care organizations are under intensive investigation ( 6 ).

Data envelopment analysis (DEA) ( 7 ) is a non-parametric, deterministic alternative to several efficiency measurement techniques, such as the cost-effectiveness ratio, corrected ordinary least squares or stochastic frontier analysis. DEA ( 7 ) uses a linear programming technique that gives a single measure of efficiency. It is based on the principle that an organization can be considered efficient when it is able to obtain the greatest output, in terms of goods, products or feasible services, by using a certain combination of used resources as input or, alternatively, when it produces a certain level of output using the least possible input ( 8 , 9 ).

Since its inception 40 years ago, DEA has been extensively investigated and applied as a tool ( 10 ). Despite its uncertainty ( 11 ), it is a promising way to estimate the efficiency of units in many areas, including health care systems ( 12 , 13 ), reform ( 14 , 15 ), PHC centres (see 54 publications in Supplementary Table S1 ), regions ( 16–20 ), dental units ( 21 ), emergency departments ( 22 ) and hospitals ( 10 , 23–25 ). Systematic reviews have surveyed the literature associated with DEA and identified the most influential journals in the fields of DEA and its applications in the period 1978–2019 ( 26–30 ). DEA is a well-developed method used in health care efficiency assessment, and its use is still being refined ( 31 ). It can be used to evaluate operating organizations, establish criteria to improve their functioning and measure their progress.

Despite the considerable body of literature surrounding DEA published over the last 20 years ( 32 ), including applications in Poland ( 33 ), a search of PROSPERO revealed only four protocols for the systematic review of assessments of the efficiency of health services using DEA ( 34 , 35 ). The aim of the present article is to systematically review empirical studies of DEA applications in the field of PHC to identify the most commonly employed group inputs, outputs and models.

Our findings will facilitate the development of a standard set of criteria for the design and execution of DEA in PHC and may prove valuable for the standardization of DEA outcomes. This study serves as a step towards the standardization of DEA as the most widely used tool for improving the efficiency of PHC organizations and contributes to the refinement of DEA as a methodology.

Our review employs the approach described by the Institute of Health Science in Oxford, adopted for General Practice by Department of General Practice, University of Glasgow, as part of their Critical Appraisal Skills Programme ( 36 ).

This review was reported according to the Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) approach ( 37 ). The review corpus comprises studies of health care technical efficiency based in the primary care setting. The list of included studies was restricted to those concerning decision-making units (DMUs) such as PHC centres, physicians, family physicians, primary care physicians, GPs, general practices and health maintenance organizations (HMOs).

Search strategy

A systematic electronic search was performed according to PRISMA ( 37 ) between March 2017 and March 2019, which covered the studies published before 25 March 2019. The literature search was performed by two independent researchers (IZ and MGC) in four electronic databases: PubMed, MEDLINE Complete (Medical Literature Analysis and Retrieval System Online), Embase (Excerpta Medica Database) and Web of Science.

The search terms and filters listed in Box 1 were used. The identified papers were limited to full-text original and review articles published in English.

– Efficiency [MeSH Major Topic]

– Benchmarking [MeSH Major Topic]

– Benchmarking [Title/Abstract]

– ‘Data Envelopment Analysis’ [Title/Abstract]

– DEA[Title/Abstract]

– ‘Technical Efficiency’ [Title/Abstract]

– #1 OR #2 OR #3 OR #4 OR #5 OR #6

– ‘Primary Health Care’ [MeSH Major Topic]

– ‘Physicians, Primary Care’ [MeSH Major Topic]

– ‘Primary Health Care’ [Title/Abstract]

– ‘General Practice*’ [Title/Abstract]

– ‘Physicians*’ [Title/Abstract]

– ‘HMO*’ [Title/Abstract]

– #8 OR #9 OR #10 OR #11 OR #12 OR #13

– #7 AND #14

Filters: (Journal Article OR Review) AND Full text AND English

Study selection

The retrieved papers were imported into EndNote X4. Duplicates were identified and removed. The two researchers independently manually screened the titles and Abstracts to select relevant papers. Any disagreements were resolved by discussions with an external expert. When the articles had insufficient information in the title and Abstract to support this screening, a full-text reading was conducted. Following this, all potentially eligible papers were added in full-text form.

A manual search was then used to retrieve papers for full-text review. These papers were examined by two authors using a checklist designed for this study, with inclusion and exclusion criteria given in Box 2 . The list of included studies was restricted to those concerning DMUs such as PHC centres, physicians, family physicians, primary care physicians, GPs, general practices and HMOs.

Studies that met the following criteria were included inputs and outputs used to evaluate the technical efficiency of PHC centres or physicians, using the DEA method. The studies concentrated on the efficiency of PHC centres as the organization as a whole, including the physicians’ efficiency working in these centres.

Studies that included the following DMU levels: PHC centres , physicians , family physicians , primary care physicians , GPs , general practices , and health maintenance organizations (HMO).

Studies written as a full-text journal article, in the English language .

Studies that were not based on the DEA method: no inputs, no outputs, no models, or no technical efficiency calculations, and not refer to primary health care. In addition, those that did refer to technical efficiency of quality/satisfaction, disease, treatment/drug/therapy, e-health/computer, reform/system, programmes, statistics, or education/training.

Studies that were based on the following DMU levels: nurses, specialists, emergency, paediatric, mental/psychology units, systems, hospitals, psychiatric hospitals, nursing homes, veterans integrated service networks, acute care nursing units, ambulatory surgery centres, specialized inpatient cancer centres, dialysis, dialysis centres, dental providers, organ procurement organizations, skilled nursing facilities, community-based youth services, mental health cases, regions and area agencies on ageing.

Studies that were not a journal article, book, review or editorial; studies that were not written in the English language; and studies with no full text.

All papers that passed the inclusion criteria were subjected to full-text reading. Papers from the reference lists and bibliographies of the retrieved studies were also included. Finally, 54 papers were selected for analysis.

Data collection process

The key findings and conclusions of the eligible studies were identified by one author. An evidence table was used to extract information relevant to the study aim. As shown in online resource, Supplementary Table S1 , this extracted information included the authors and year of publication, link, title, name and number of centres, country of study and key findings of the DEA model used (orientation and type); input and output categories were analysed systematically to ensure consistency between the eligible studies regarding the extracted data characteristics. Further consistency with the primary studies was ensured by sharing data between the authors.

Synthesis of results

A thematic analysis was performed of the results, tables and graphs of summary data of the studies; this analysis allowed a comparison of the key findings, conclusions and impact of study quality on results, and to identify the potential for publication bias. The inputs, outputs, categories and models were summarized and calculated, as were the descriptive statistics for categories (minimum, maximum, mode and median).

The inputs and outputs from eligible papers were classified into categories. The PHC dimensions developed by Kringos et al . for primary care systems ( 31 , 38 ) were included as compound variables ( Tables 1 and 2 ).

Input categories

Output categories

The details of the included studies of the basic bibliographical information and all inputs and outputs used are presented in online resource ( Supplementary Table S1 ).

Risk of bias across studies

The overall quality of the studies was assessed using the Quantitative Study Assessment Checklist developed at the Department of Computer Science, University of Auckland ( 39 ). All studies described the DEA method in detail; however, some did not include substantive information on the variables used to minimize selection bias. None of the eligible studies reported the theoretical or philosophical bases for methodological choice, which limited the ability to situate and assess methodological relevance.

The risk of bias and the quality of individual selected studies were assessed by two members of the team working directly on the review, who independently evaluated each included paper. Doubts were adjudicated by a third, external reviewer. The criteria for assessing research quality were based on the Critical Appraisal Checklist for a Systematic Review adapted by the Department of General Practice, University of Glasgow, from the Critical Appraisal Skills Programme of the Institute of Health Science in Oxford ( 36 ).

A total of 4231 papers were identified (639 in PubMed, 849 in MEDLINE Complete, 103 in Embase and 2640 in the Web of Science) for title and Abstract screening and manual selection. Eighty-one papers were retrieved from the screenings, with an additional 25 selected from their bibliographies, for a total of 108 papers selected for full-text review. After the full-text review based on a checklist, 54 papers were selected and analysed for inputs, outputs and models. The following numbers of papers were excluded for the following reasons: no full text available (25), no DEA (no input and no output) (17), no primary care (6), review paper (5) and editorial article (1). Figure 1 presents a flowchart of the search strategy results of the DEA method applications in PHC.

Flow diagram of the search strategy results of the data envelopment analysis method applications in primary health care.

Study characteristics

Most of the eligible studies were performed in Europe (24 studies), followed by North America (15), Africa (6), South America (6), Asia (2), and Australia and New Zeeland (1). The most eligible publications came from the USA (13), followed by Spain (6), the UK (6), Greece (4), Brazil (3) and Italy (3). Two studies per country were identified in Portugal, Sierra Leone and Burkina Faso, while only one each was identified from the Netherlands, Austria, Canada, Finland, Guatemala, Pakistan, Colombia, Chile, Mexico, New Zealand, South African, Ethiopia and Saudi Arabia.

Inputs and outputs

Inputs and outputs from the analysed papers were assigned to 13 and 12 thematic categories, respectively. Details of the inputs and outputs included in each category are presented in online resource Supplementary Table S1 .

The number of inputs used in a single study ranged from 1 (minimum) to 24 (maximum), while the outputs ranged from 1 (minimum) to 21 (maximum), with a modal value of three for both. The most frequently used input categories were personnel (associated with 98 variables), PHC centres ( 33 ), consultations or visits ( 25 ), referrals or hospitalizations ( 24 ), and pharmaceuticals or prescriptions ( 23 ) ( Table 1 ).

The most frequently used output categories were health care consultations or visits (83 variables from studies), patients (69), procedures, treatment, and services (45), quality (43), personnel (31), preventive interventions (including vaccinations) (18), and PHC centres (11) ( Table 2 ).

Eleven categories were represented in both the input and output groups.

The efficiency of PHC centres was evaluated using various DEA models. The most commonly used single model was the Variable Returns to Scale (VRS) DEA, which was applied in 16 studies. In 13 publications, the efficiency was calculated using both the Constant Returns to Scale (CRS) and VRS DEA models. Fourteen publications used the CRS model. The most widely used DEA model was input orientation, which was applied in 22 papers.

The characteristics of the eligible studies are presented in Supplementary Table S1 .

Summary of evidence

This systematic review showed a number of approaches to quantitative evaluation to PHC activities with a scope of inputs and outputs used. These can be divided into thematic categories, with the variety of models which have been used. There is still room for improvement of the model in PHC applications.

A total of 54 studies on DEA applications in PHC were identified with selections of inputs, outputs and models related to patients. This is a potential additional value of the DEA method: it offers researchers a wide selection of potential research questions associated with an adequate choice of the model and analysis parameters. Quantitative DEA-based studies can examine the effectiveness of a wide scope of processes in primary care, such as costs of provided care, medication, patient waiting time or chronic care delivery. We hope that future studies will confirm our expectations.

Greater standardization of DEA is needed in further research considering PHC applications. Eleven categories were represented in both the input and output groups: e.g. for category as consultations or visits used as input (e.g. outpatient visits, the annual number of patient consultations with their physicians) and as the most frequently output (e.g. number of visits carried out by the community health workers; annual number of patient visits to each Primary Care Centre) ( Table 2 and Supplementary Table S1 ).

The choice of CRS or VRS and model orientation depends on the context.

The number of publications related to DEA in all databases has increased over the last 5 years. Various DEA methods were used to estimate the efficiency of organizations in the health sector, with a variety of models being applied. DEA methods do not require any knowledge of the linkage between inputs and outputs to calculate efficiency. DEA use varies geographically, with most studies performed in the USA and the UK.

In 1999, Garcia ( 8 ) found the efficiency of PHCs to be affected by intermediate outputs, which needed to be improved. These results confirmed that efficiency depends on the number of outputs and inputs and the choice of outputs for a specific unit of measurement ( 8 ). According to Pelone et al ., primary care outcomes can be determined by general practice discretionary inputs ( 40 ).

Input and output categories

The main input and output categories can be seen from two perspectives. The first concerns PHC centres and patients, which addresses the patients, number of staff (GPs, nurses and administrators), costs, areas, procedures, prescriptions and referrals. The second concerns public health, which looks at health care systems and the optimal organizational achievement of primary care service delivery; their inputs include primary care governance, workforce development and economic conditions, and their outputs include comprehensiveness, access, coordination and service delivery indicators of access continuity and comprehensiveness of care.

Ferreira et al . used another approach including staff expenditure as the most common input, with the different kinds of consultations related to the PHC being the most commonly used outputs ( 41 ).

All of the included DEA applications were focussed on technical efficiency. Various DEA models were used to evaluate the efficiency of PHC units (e.g. primary care practices, district health authorities, physician practices, PHC centres).

In 13 publications, the efficiency was calculated using both the CRS and VRS DEA models. The choice of a CRS or a VRS should depend on the context and the level of analysis.

The CRS model assumes a linear, proportional change in outputs associated with changes in inputs, e.g. Chilingerian and Sherman employed the CRS DEA model, with the DMU as the individual primary care physician ( 42 ).

The VRS model is appropriate when input or output variables are defined using ratios ( 43 ). While 16 studies used VRS, 14 used CRS (see Supplementary Table S1 ).

Model orientations

The most widely used DEA model was input orientation, which was applied in 22 papers. When choosing a DEA model, it is necessary to define initially if the input or output-oriented method will be used. In an input-oriented model, the goal is to minimize the use of inputs to maintain a constant level of outputs (input-oriented model producing a given output with minimum inputs). In the health care industry, outputs are less controllable than inputs. The choice of the input model is justified on the fact that managers in health care services tend to have greater control over inputs rather than outputs.

From the point of view of the health care executives, it is easier to control inputs than health results, which is why reserchers choosing an input-oriented model ( 44 ).

Cordero Ferrera et al . used an input orientation in primary care centre efficiency measurements because managers can determine only those resources attributed to each primary care centres and that the demand for health services cannot be controlled ( 45 ).

However, regarding the reduction of expenditure in PHC services, Stefko et al . conclude that the health care sector is specific and that health care services should concentrate on increasing outputs rather than reducing inputs and costs ( 20 ). Oikonomou et al . chose an output-oriented model because the demand for PHC services has a tendency to expand rather than decrease. In an output-oriented DEA model, whose aim is to maximize the outputs with the given level of inputs, it is assumed that greater output is associated with technical efficiency ( 46 ).

Methods for measuring the efficiency of health care sectors and national innovations most commonly were based on the input- or output-oriented DEA CRS model ( 47 ), although the super-efficiency DEA model, the DEA specification for bilateral comparison of two clusters of DMUs, and grey relational analysis with DEA models ( 48 ) were also used. Pelone et al . studied PHC efficiency using the DEA method and showed that scale efficiency scores depended on the DEA model orientation, the input–output variables used, and the restrictions incorporated into the DEA model ( 49 ).

Our analysis revealed a gradual increase in the number of scientific publications related to the use of DEA methods. DEA appears to be the most commonly used tool used for analysing the efficiency of PHC organizations. Nevertheless, there is still room for improvement; further research is needed on DEA analysis, particularly the choice of inputs and outputs, as these affect the efficiency of the organizations examined.

An interesting idea concerns the introduction of exogenous variables, which in addition to the allocation efficiency score for all units, also provide information about potential additional reductions in inputs or potential increases in outputs. These can be detected in specific cases by incorporating non-radial inefficiency or slacks to the DEA dual model ( 43 ).

Finally, it has to be stressed that DEA scores depend on the choice of input and output variables, models and weighting. The efficiency score is relative. Although each organization can be compared with the reference organization, i.e. the best one, within a study, it is not possible to compare scores between separate efficiency studies. Moreover, the DEA technique ignores the noise in the data, and the efficiency measures are very sensitive to the sample size and outliers ( 50 ). In addition, there are no diagnostic tests to determine the validity of the model or to improve the model specification ( 51 ).

Despite rising health care costs and the growing need for the financial sustainability of health care systems, the tools for analysing their efficiency, including DEA models, remain inadequate, and further studies on PHC organizational efficiency are needed.

Limitations

This review presents a quantitative tool for the assessment of the public domains of PHC, which despite their importance, are costly and prone to risk of shortage. However, this review has limitations. As it was limited to studies published in English in peer-reviewed journals, it is possible that other relevant published or unpublished studies and insights were missed. Some publications could be missed due to lack of access. Moreover, the screening process for some of the eligible studies was conducted by a single author, which may have affected the accuracy, reliability and transparency of the process.

This article, a review of state-of-the-art research describing the most commonly used groups of outputs and inputs, serves as a step towards the standardization of DEA. It was found that the most widely used model for efficiency orientation was input orientation. Although the number of studies based on DEA methods is gradually increasing and DEA is the most frequently used tool in the efficiency analysis of PHC organizations, there is still room for improvement. Further research is required to identify appropriate input and output variables and a suitable DEA model for assessing PHC.

The standardization of DEA could extend the scope of research tools for the analysis of functioning the primary care. This would support health economic analyses of measurements of primary care efficiency.

Funding: Narodowe Centrum Nauki (National Science Centre Poland) (2016/21/B/NZ7/02052).

Ethical approval: none.

Conflict of interest: none.

The authors declare that the data supporting the study findings are available within the article.

The authors thank Katarzyna Kosiek, PhD, for reviewing the manuscript and Edward Lowczowski for English language assistance.

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A review on inputs and outputs in determining the efficiency of universities of medical sciences by data envelopment analysis method

Mohammad m. mojahedian.

1 Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran

Aeen Mohammadi

2 Department of eLearning in Medical Education, Virtual School, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Abdollahi

3 Department of Toxicology and Pharmacology, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran

Abbas Kebriaeezadeh

4 Department of Toxicology and Pharmacology, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran

Mohammad Sharifzadeh

5 Department of Pharmacology and Toxicology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran

Shadi Asadzandi

6 Research Department, Virtual School, Tehran University of Medical Sciences, Tehran, Iran

Shekoufeh Nikfar

Background: Increasing the number of students in universities, simultaneously limiting allocation of funds to them, and maintaining the highest efficiency level in education and research are of paramount importance. There are several methods to assess the efficiency of universities, and one of the most widely used of which is data envelopment analysis (DEA). The aim of this study was to determine the input and output criteria to evaluate the efficiency of universities of medical sciences through review-related articles using the DEA method.

Methods: The time limit for retrieving articles was considered from the beginning of the publication of the first paper in this field until the end of 2017. The data were retrieved from Web of Science, Scopus, Ovid, ProQuest, Science Direct, and PubMed using advanced searches. Inclusion criteria were as follow: relevancy of the articles to the purpose of the research, availability of the articles’ full-text, articles published to the end of 2017, and articles published in English.

Results: The most inputs used in the literature to determine university efficiency were number of academic staffs, budget and costs, number of students, number of nonacademic staffs, spaces, and equipment and student's entrance scores. Also, the most outputs used in the literature to determine university efficiency were number of graduates, publications, incomes, number of students, and student's scores.

Conclusion: This study showed that a large number of researchers have focused on measuring and comparing the efficiency of universities to improve efficiency, reduce costs, and manage the resources. Efficiency analysis by DEA allows the policymakers to define and develop policies and guidelines to improve their performances.

↑ What is “already known” in this topic:

By increasing the number of university students and limited funds allocated to universities, maintaining the efficiency of universities resources is necessary. Data envelopment analysis (DEA) method is one of the most widely used methods to evaluate efficiency of universities.

→ What this article adds:

To evaluate efficiency via DEA method, selecting outputs and inputs plays an important role. Results of this study determine most inputs and outputs that can be used to evaluate the efficiency of universities of medical sciences.

Introduction

Education is an important factor in human life and evidence suggests that it is directly related to the economic growth of a country ( 1 , 2 ). Because of the social benefits of education, one of the ways to finance education cost has always been the public funding of countries. However, due to the high demand for public funding, optimum use of the public resources allocated to education is highly important. By increasing the number of university students and limited funds allocated to universities, maintaining the highest level of performance and using universities’ resources efficiently is of high importance. Therefore, understanding the efficiency of universities plays an important role in allocating funds to academic units. Also, one of the most important issues related to university management is determining the relative efficiency of units ( 3 ).

Generally, efficiency means using minimum resources to produce the highest output and product. More broadly, efficiency can be defined by doing the right things. Organizational efficiency should be continuously measured by making plans for improvement, providing information about organizational performances, and guiding the university toward its goals. Therefore, in recent years, a large number of researchers have focused on measuring and comparing the efficiency of decision-making units to improve efficiency, reduce costs, and manage resources and their subunits. Efficiency analysis allows universities’ policymakers to define policies and guidelines to improve their performances ( 4 ).

There are several methods to evaluate the efficiency of decision-making units, including universities. The data envelopment analysis (DEA), introduced by Charnes et al in 1978, is one of the most widely used methods, which uses multiple inputs and outputs. DEA is a linear and nonparametric method and is used to evaluate the relative efficiency of decision-making units (DMUs). The function of DEA is to evaluate the efficiency by multiple inputs and outputs data from the decision-making units and to evaluate the advantages as much as possible by assigning variable weights to each element of input and output ( 5 ). In this method, the best performance is given to a unit that produces the highest output with the lowest input and this unit is then used as the reference to determine the inefficiency of other units ( 6 , 7 ).

In DEA method, the efficiency of each unit depends on the selected inputs and outputs, which are highly important, as inputs and outputs must reflect appropriate university resources and its specific activities. Also, DEA depends on the study objectives and does not provide guidance for selecting output and input ( 8 , 9 ).

The aim of this study was to determine the inputs and outputs by reviewing the existing articles that have evaluated the efficiency of universities using the DEA method to evaluate the efficiency of universities of medical sciences. In Iran, universities of medical sciences are supervised by the Ministry of Health, while nonmedical universities are supervised by the Ministry of Science. However, in most countries, medical and nonuniversities of medical sciences are integrated. Therefore, in this study, all articles that evaluated the efficiency of universities were examined. Finally, the most frequently used inputs and outputs to determine the efficiency of universities was reported.

Data were retrieved from Web of Science (WOS), Scopus, Ovid, ProQuest, Science Direct, and PubMed databases using advanced search to extract and compile all articles related to the performance evaluation of universities using DEA method.

The time limit for retrieving articles was considered from the beginning of the publication of the first paper in this field until the end of 2017. The search process was conducted in November 2018 for 1 month. The keywords were obtained using MeSH terms and limited by expert opinions. The keywords used to retrieve the articles were compiled as follow: efficiency, efficacy, data envelopment analysis, DEA, university(s), college(s), faculty(s), school(s), and academic unit(s).

An example of strategies used in science direct database is as follows:

(“efficiency” OR “efficacy”) AND (“data envelopment analysis” OR “DEA”) AND (“university” OR “universities” OR “college” OR “colleges” OR “faculty” OR “faculties” OR “school” OR “schools” OR “academic unit” OR “academic units”).

The criteria for including articles to the study were as follow: relevancy of the articles to the purpose of the research, availability of the articles’ full-text, articles published up to the end of 2017, and the articles published in English.

Exclusion criteria were the lack of access to the text of the articles, unrelated subject area, other formats of the article, non-English articles, and review articles. Endnotes were used to remove similar articles, and if similar studies were found during reviewing the articles, they were removed by the researchers. The elimination and review process, including studying abstracts, and full-text of the articles were performed by 2 individuals separately; then, the results and contradictory items were determined. In cases where there was disagreements, a third person reviewed the article. After the articles were collected, data entered into a table containing the title of the article, inputs, and outputs. It was also found that the university's efficiency with respect to education and research was examined separately.

The results of the reviewing process and selecting articles in terms of entry and exit criteria are summarized in Flowchart 1 .

The results of the process of reviewing and selecting articles in terms of entry and exit criteria

Table 1 presents the list of the most inputs used in the literature to determine efficiency of universities.

Also, Table 2 shows the list of the most outputs used in the literature to determine efficiency of universities.

The list of most inputs and outputs used in the literature to determine efficiency of universities is presented in Table 3 .

This study showed that in some studies, the efficiency of universities in the fields of education and research has been studied separately ( 36 , 41 , 42 , 48 , 53 , 69 , 76 , 87 , 88 , 101 , 104 , 106 , 118 , 126 ).

In the extraction section for inputs for the efficiency section of universities, in 79% of the reviewed papers, the number of academic staffs were considered as one of the inputs, which included some titles such as an academic staff member, academic staffs, faculties, teaching staff, etc. In some of these articles, academic staffs were also classified according to academic rank ( 43 , 66 , 102 ).

Also, in 75% of the articles, the budget and costs were used as one of the inputs with headings like funds, income, financial resources, revenue, budget, expenditure, costs, grants, and spending.

In 35% of the articles, other inputs used to analyze the efficiency were number of students with different titles: total number of students, total enrolment, number of undergraduates, and postgraduates.

The number of nonacademic staffs, with titles as number of nonacademic staff, number of administrative staffs, other personnel, support staff etc., were seen in 29% of the articles; physical space, with titles as floor area, land space, number of room and etc., were observed in 16% of the articles; also, physical amenities, with titles as assets, equipment, physical capital, physical resources and etc., were used as an input in 12% of the articles.

Another input related to analyzing efficiency of universities used in 10% of the articles by the researchers was students’ entrance score with titles such as average exam score, minimum entrance score, mean entry score, etc.

The followings were also observed to be used as inputs: diversity of teaching materials ( 148 ), socioeconomic level of students ( 148 ), quality of teaching materials ( 148 ), number of research centers ( 76 ), human resources ( 24 , 145 - 147 ), ranking of the university in the previous year ( 91 ), average students’ qualifications ( 36 ), discipline level ( 99 ), richness of course contents ( 152 ), diversity of accessed multiple teaching channels ( 152 ), average students and staffs qualification ( 48 ), student contact hour ( 112 ), teaching experience of members ( 147 , 151 ), number of study programs ( 56 ), number of collages ( 59 ), number of electives ( 95 ), provision of work placement ( 147 ), and personnel characteristics ( 150 ).

Furthermore, in some articles, input factors were expressed as ratios: the ratio between full-time academic staffs, expenditures, number of nonacademic staffs, and total space to full-time students ( 90 ); student to faculty ratio ( 80 , 134 ); educational and general expenditures per student ( 93 ); total funding per student ( 97 ); academic staffs and total expenditures per student ( 97 ); number of professors and computers per enrolled student ( 114 ); budget per student ( 116 ); faculty to student ratio ( 95 ); and average total spending per student ( 96 ).

In the section of output variable selection for analyzing efficiency of universities, in 52% of the articles, the number of graduates, and in 48% of the studies, publication, with titles of number of publication, number of articles, number of research papers, citation index and etc., were considered as outputs.

Income, with titles of income, grants, funds, revenue etc., in 38% of all articles, number of students in 35% of the articles, and student’s score, with titles such as academic results, the mean score of exam etc., in 8% of the articles were used as one of the outputs.

Some titles, including completion rate ( 15 ), volume of contracts ( 27 , 61 ), patents ( 41 , 56 , 68 , 70 , 98 ), number of projects ( 41 , 69 , 76 , 136 ), students’ level of satisfaction ( 24 , 95 , 121 , 148 ), number of dissertations ( 41 , 81 , 96 , 137 ), students who found jobs ( 36 , 45 , 99 , 128 , 134 ), papers presented in national and international conferences ( 76 ), retention and progress rate ( 35 , 90 ), number of certificates ( 71 ), international collaborations ( 32 , 98 , 140 ), rank of university ( 32 , 80 ), median starting salaries ( 45 , 99 , 134 ), freshman retention rate ( 93 ), employer preference for hiring alumni ( 34 ), number of awards ( 36 ), percentage of international students ( 34 , 45 , 140 ), number of intellectual awards ( 36 , 48 , 64 ), number of seminars ( 136 ), technology transfer ( 139 ), average number of classes taught per department ( 83 ), students learning performance ( 152 ), number of accredited educational, national program ( 108 ), number of infringement and turning over to the committee of peculiar cases ( 121 ), employer satisfaction with training of the student ( 112 ), employer satisfaction with graduate ability ( 95 , 106 ), competencies ( 147 ), and achievements of students in competitions ( 106 ) were outputs used to assess efficiency of universities.

In addition, in some articles, outputs were expressed as ratios, including number of indexed publications per academic staffs ( 31 , 94 ), average enrolment per class ( 83 ), ratio of income to the number of students who paid for education ( 143 ), number of graduate/ postgraduate/ doctorate students per academic year ( 113 ), and the number of students per number of school leavers ( 135 ).

In the reviewed articles, the number of academic staffs, costs, and number of students were the most important inputs; also, number of graduates, publications, and income were the most important outputs used to determine the efficiency of universities via DEA method. Moreover, we suggest the use of these inputs and outputs to evaluate medical universities and the efficiency of their faculties in future studies.

In this study, most of the universities included the faculties of medical sciences. Therefore, these data can also be used to measure the efficiency of universities of medical sciences using DEA method.

Acknowledgments

This study was part of the PhD thesis of Mohammad M. Mojahedian.

The authors declare that they have no competing interests.

Cite this article as: : Mojahedian MM, Mohammadi A, Abdollahi M, Kebriaeezadeh A, SharifzadehM, Asadzandi Sh, Nikfar Sh. A review on inputs and outputs in determining the efficiency of universities of medical sciences by data envelopment analysis method. Med J Islam Repub Iran . 2020 (2 May);34:42. https://doi.org/10.34171/mjiri.34.42

Data Envelopment Analysis

• First Online: 30 April 2023

Cite this chapter

• Farhad Hosseinzadeh Lotfi 6 ,
• Tofigh Allahviranloo 7 ,
• Morteza Shafiee 8 &
• Hilda Saleh 9  

Part of the book series: Studies in Big Data ((SBD,volume 122))

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Throughout history, considering the limitations, humanity has tried to make the most of the available facilities and resources. In this regard, performance evaluation is considered one of the managers' most vital issues. In fact, for a manager, knowing the performance of supervised units is the most critical task in making a decision and adopting a suitable strategy. The complexity of information, a lot of data, and the influence of various other factors make managers unable to learn about the performance of the units under their supervision without a scientific approach. One of the essential concepts in performance evaluation is calculating the efficiency of the units under the assessment. Therefore, more scientific methods are needed to calculate efficiency than in the past. One of the appropriate and efficient tools in the field of efficiency measurement is data envelopment analysis (DEA), which is used as a non-parametric method to calculate the efficiency of decision-making units. DEA models, in addition to determining the relative efficiency, the weak points of the organization in various indicators, also the resources affecting the inefficiency of organizations, are selected by DEA models, and finally, presenting an efficient projection defines the organization's policy toward improving efficiency and productivity. These reasons have caused this technique to grow increasingly from the theoretical and practical aspects and become one of the essential branches in the science of operations research. In recent years, many theoretical and practical developments have happened in DEA models, making it indispensable to know its various aspects for a more precise application of DEA models for the performance evaluation of a supply chain. Thus, in the rest of this chapter, we will explain the DEA definitions and models needed in the following chapters. Thus, in the rest of this chapter, we will explain the DEA definitions and models required for the following chapters.

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Farhad Hosseinzadeh Lotfi

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Hosseinzadeh Lotfi, F., Allahviranloo, T., Shafiee, M., Saleh, H. (2023). Data Envelopment Analysis. In: Supply Chain Performance Evaluation. Studies in Big Data, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-031-28247-8_6

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Radomski, Błażej. "Fusionen deutscher Sparkassen : eine Anwendung der Data-envelopment-Analysis (DEA) /." Hamburg : Kovač, 2008. http://d-nb.info/988938782/04.

Ashoor, Khalil Layla Ali. "Performance analysis integrating data envelopment analysis and multiple objective linear programming." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/performance-analysis-integrating-data-envelopment-analysis-and-multiple-objective-linear-programming(65485f28-f6c5-4eff-b422-6dd05f1b46fe).html.

CARMO, Cinthya Melo do. "Avaliação da Eficiência Técnica das Empresas de Saneamento Brasileiras Utilizando a Metodologia DEA." Universidade Federal de Pernambuco, 2003. https://repositorio.ufpe.br/handle/123456789/5836.

Diagne, Djily. "La performance des écoles de maturité suisses romandes : une évaluation par la méthode DEA /." [S.l.] : [s.n.], 2003. http://aleph.unisg.ch/hsgscan/hm00074577.pdf.

Jardim, João Pedro Fernandes. "Airports efficiency evaluation based on MCDA and DEA multidimensional tools." Master's thesis, Universidade da Beira Interior, 2012. http://hdl.handle.net/10400.6/2011.

Wilkinson, Robert H. "The measurement of university performance using concepts derived from data envelopment analysis (DEA)." Thesis, University of Stirling, 1991. http://hdl.handle.net/1893/2113.

LeBel, Luc. "Performance and efficiency evaluation of logging contractors using data envelopment analysis." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-152114/.

Ozpeynirci, Nail Ozgur. "New Approaches For Performance Evaluation Using Data Envelopment Analysis." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12604977/index.pdf.

Hjalmarsson, Victoria. "Värdeflödesanalys och Data Envelopment Analysis : En fallstudie för att värdera effektiviteten hos ett logistikföretag." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-28011.

Kabnurkar, Amit. "Mathematical Modeling for Data Envelopment Analysis with Fuzzy Restrictions on Weights." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/31992.

Mienie, Barend Jacobus, and Warren Brettenny. "Assessing the productivity of selective container terminals in Africa using Data Envelopment Analysis (DEA)." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/12054.

Dourado, Afonso. "Aplicação da Data Envelopment Analysis na determinação da eficiência empresarial em ambientes colaborativos." Master's thesis, Faculdade de Ciências e Tecnologia, 2009. http://hdl.handle.net/10362/10020.

CANEL, Lautemyr Xavier Cavalcanti. "Análise de projetos de desenvolvimento na Re- gião Nordeste do Brasil : a experiência do Sistema FINOR nos anos de 1962 a 2001." Universidade Federal de Pernambuco, 2005. https://repositorio.ufpe.br/handle/123456789/5202.

Theunissen, Marli. "An application of Data Envelopment Analysis to benchmark CEO remuneration / Marli Theunissen." Thesis, North-West University, 2012. http://hdl.handle.net/10394/9845.

Abel, Lecir. "Avaliação cruzada da produtividade dos departamentos acadêmicos da UFSC utilizando DEA (Data Envelopment Analysis)." Florianópolis, SC, 2000. http://repositorio.ufsc.br/xmlui/handle/123456789/79190.

AYACH, ALI. "Bootstrapped Data Envelopment Analysis DEA: the impact of multicultural workgroup on health care efficiency." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2012. http://hdl.handle.net/2108/214165.

Lee, Myunghyun. "Measuring and Ranking Efficiency of Major Airports in the United States Using Data Envelopment Analysis." Master's thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/46532.

Pereira, Djalma Siqueira. "Technical efficiency of production of graduate courses through the ufc data envelopment analysis." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7430.

Fouad, Geoffrey George. "Assessing the performance of water bodies in Hillsborough County, Florida using Data Envelopment Analysis (DEA)." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002824.

Santos, Jorge Manuel Azevedo. "Issues of universal feasibiliy and multplier adjustment in data envelopment analysis (DEA) with an application." Doctoral thesis, Universidade de Évora, 2008. http://hdl.handle.net/10174/11147.

Minato, Evandro. "Avaliação de produtividade de uma indústria na linha do tempo utilizando DEA (data envelopment analysis)." Florianópolis, SC, 2006. http://repositorio.ufsc.br/xmlui/handle/123456789/89222.

SOUZA, Luciano. "Aplicação de Data Envelopment Analysis - DEA para obtenção de mapas de exclusão e inclusão social." Universidade Federal Rural de Pernambuco, 2007. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5162.

Geymüller, Philipp von. "The efficiency of European transmission system operators. An application of dynamic DEA." Forschungsinstitut für Regulierungsökonomie, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/1070/1/document.pdf.

Mills, Joseph J. "Efficiency evaluation and improvement guidelines for community colleges of Connecticut : a data envelopment analysis (DEA) approach." Thesis, Durham University, 2004. http://etheses.dur.ac.uk/3122/.

Tollin, Michela <1989&gt. "La metodologia data envelopment analysis (DEA) per la misurazione dell'efficienza di organizzazioni culturali: esperienze a confronto." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8417.

Tavares, Necésio José Faria. "Modelo quantitativo para avaliação de desempenho empresarial baseado em análise envoltória de dados com múltiplos fatores." Universidade Presbiteriana Mackenzie, 2008. http://tede.mackenzie.br/jspui/handle/tede/2556.

Tuncer, Ceren. "A Dea-based Approach To Ranking Multi-criteria Alternatives." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607476/index.pdf.

Erturk, Mehmet. "Efficiency Analysis Of Turkish Natural Gas Distribution Companies By Using Dea Method." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610796/index.pdf.

Wooton, Sharyl Stasser. "DATA ENVELOPMENT ANALYSIS: A TOOL FOR SECONDARY EDUCATION RANKING AND BENCHMARKING." Oxford, Ohio : Miami University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1050604854.

Clemente, Moquillaza Luis Alfredo Manuel. "Optimización de la eficiencia operativa de las oficinas de un banco comercial utilizando DEA (Data Envelopment Analysis)." Master's thesis, Universidad Nacional Mayor de San Marcos, 2019. https://hdl.handle.net/20.500.12672/10525.

Tello, Miranda Marco Antonio. "Evaluación de la eficiencia técnica de universidades públicas del Perú utilizando la metodología Data Envelopment Analysis (DEA)." Master's thesis, Universidad Nacional Mayor de San Marcos, 2021. https://hdl.handle.net/20.500.12672/17118.

Heyde, Brandy. "EVALUATING THE PERFORMANCE OF ANIMAL SHELTERS: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3028.

Saraiya, Devang. "The Impact of Environmental Variables in Efficiency Analysis: A fuzzy clustering-DEA Approach." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34637.

Murinová, Michaela. "Porovnání systémů pro hodnocení efektivnosti pomocí DEA modelů." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150228.

Eriksson, Martin, and Niclas Pettersson. "Vi har mätt - gör Polisen saker på rätt sätt? : En Data Envelopment Analysis (DEA) baserad studie för produktivitetsmätning." Thesis, Stockholm University, School of Business, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6311.

I denna uppsats har vi mätt produktionseffektiviteten vid Sveriges 21 polismyndigheter i syfte att identifiera vilka polismyndigheter som är relativt mest produktiva. Vidare har vi beräknat hur stor förbättringspotentialen är för de polismyndigheter som är improduktiva. Beräkningarna grundar sig på Data Envelopment Analysis (DEA), en metod som används i syfte att beräkna den relativa produktionseffektiviteten. Metoden tillåter multipla variabler vilket möjliggör en analys av flervalsproduktion, d.v.s. en analys av enheter som producerar flera prestationer (outputs) med hjälp av flera insatsfaktorer (inputs). Populationen av prestationer och insatsfaktorer finns att tillgå i den nationella resultatmodell som utvecklats av Rikspolisstyrelsen och polismyndigheterna med avsikt att förbättra styrningen av det polisiära arbetet. Urvalet av vilka prestationer och resurser som har legat till grund för beräkningen är sex-sju till antalet och reflekterar de viktigaste produktionsförutsättningarna för respektive polismyndighets inriktning. Resultatet av studien tyder på en stor variation vad gäller produktionseffektiviteten mellan respektive polismyndighet. Endast fem av samtliga 21 polismyndigheter är relativt produktiva, d.v.s. att de erhållit ett resultat på 100 procent inom samtliga inriktningar, nämligen Stockholm, Kronoberg, Gotland, Blekinge samt Örebro. Dessa är till antal något färre än vad vi hade förväntat oss innan genomförd undersökning. Att det är så pass få produktiva polismyndigheter tyder emellertid på en stor förbättringspotential vid nästan samtliga polismyndigheter.

Sheth, Chintan H. "The Measurement and Evaluation of Urban Transit Systems: The Case of Bus Routes." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/9842.

Kohl, Sebastian [Verfasser], and Jens [Akademischer Betreuer] Brunner. "Advancing Data Envelopment Analysis (DEA) with a Focus on the Evaluation of Hospital Efficiencies / Sebastian Kohl ; Betreuer: Jens Brunner." Augsburg : Universität Augsburg, 2019. http://d-nb.info/1202713416/34.

Paris, Alaércio de. "Overall Equipment Effectiveness - OEE: necessário, mas não suficiente: uma análise integrando o OEE e a Data Envelopment Analysis - DEA." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6036.

Lertworasirikul, Saowanee. "Fuzzy Data Envelopment Analysis (DEA)." 2002. http://www.lib.ncsu.edu/theses/available/etd-05032002-101350/unrestricted/etd.pdf.

chen, Yu-ming, and 陳昱銘. "Performance Evaluation of Biotechnology Industry by Data Envelopment Analysis(DEA)." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/13489374886653832058.

Tsai, Yao-Te, and 蔡耀德. "Performance Evaluation of Navigation Industry by Data Envelopment Analysis (DEA)." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/95572124270495866948.

Lin, Tzu-yi, and 林子懿. "Performance Evaluation of Stell Industry by Data Envelopment Analysis (DEA)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/17989885734110259312.

Ferreira, Rodrigo Miguel Neto. "A Eficiência dos Bancos em Portugal de 2006 a 2015: Aplicação da Metodologia DEA." Master's thesis, 2017. http://hdl.handle.net/10316/82211.

Chen, Ying-hao, and 陳盈豪. "Performance Evaluation of TFT-LCD Industry By data envelopment analysis(DEA)." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/33737948208858544333.

Tsai, Han-Yi, and 蔡瀚儀. "Performance Evaluation of the Steel Industry by Data Envelopment Analysis (DEA)." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/94469274794022620018.

Vincent, Charles, R. Färe, and S. Grosskopf. "A translation invariant pure DEA model." 2015. http://hdl.handle.net/10454/17539.

Huang, Hsu-Chuan, and 黃旭全. "Efficiency Evaluation in Healthcare System — An Application of Data Envelopment Analysis ( DEA )." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/00408130205610485743.

Hung, Chen Hsin, and 陳信宏. "Productive Efficiency Analysis of Basic Iron and Steel Manufacturing Industry - Data Envelopment Analysis (DEA)." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/81814125645988316071.

"A DEA approach to mutual fund performance evaluation." 2010. http://library.cuhk.edu.hk/record=b5894261.

Chen, Yi-Juan, and 陳宜娟. "Operating Efficiency and Optimal Scale of Financial Service Industry--Data Envelopment Analysis(DEA)." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/04669758149133138657.

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