Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

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  1. School of Business Administration, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA, 17057, USA Dinesh R. Pai
  2. College of Business, Eastern Connecticut State University, 83 Windham St, Willimantic, CT, 06226, USA Fatma Pakdil
  3. Rochester Institute of Technology, Kate Gleason College of Engineering, Rochester, NY, 14623, USA Nasibeh Azadeh-Fard
  1. Dinesh R. Pai
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Appendices

Appendix 1

DEA was first proposed by Charnes, Cooper, and Rhodes (CCR) [391], which is based on constant returns to scale (CRS) technology. To overcome the challenges posed by CRS, Banker, Charnes, and Cooper (BCC) [176] extended the CCR model to incorporating variable returns to scale (VRS) technology assumption. The CCR and BCC models have seen significant development since their inception. The technique is based on the empirical estimation of a frontier through the application of a mathematical programming model to the observed data [12]. This frontier identifies the most efficient combinations of inputs and outputs. Cooper et al. [392] provide an introduction to the basic DEA models and theoretical extensions. Hospital efficiency was the first ranking DEA application in healthcare [6]. Nunamaker [393] was the first to apply DEA in healthcare, whereas Sherman [232] was the first to focus on hospital efficiency applications in the literature. Literature shows that DEA is the most frequently used approach to measure the efficiency of healthcare institutions [25]. A vast number of studies in the hospital efficiency literature use an input-oriented DEA approach [9, 394]. Hospitals, especially community hospitals, cannot choose their output level, which depends on the demand for healthcare services. Furthermore, in a majority of countries, hospital administrators and policymakers generally have more control over input resources compared with outputs. The focus is on controlling costs rather than increasing demand for healthcare (O’Neill et al., 2008). Stefko et al. [395] argue that the aim of hospitals is not to reduce inputs and costs but to focus on increasing outputs by improving the quality of services and patient satisfaction. Oikonomou et al. [396] claim input reduction in the provision of health services is undesirable, while an increase in outputs in many health centers is feasible. Several studies have therefore used an output-oriented approach. Yet, many studies have applied both input- and output-oriented approaches to the same dataset. The choice of orientation, however, depends on the objectives of production units under relevant production and management constraints [262].

DEA brings several advantages to researchers. First, DEA functions with multiple inputs and outputs in mathematical programming. Second, using a benchmarking approach, DEA measures the relative efficiency of the DMUs included in the analysis. Third, DEA gives flexibility to researchers regarding the structure and type of the data, including optimization direction, DMUs, inputs, and outputs. Fourth, because DEA has a non-parametric nature and a mathematical programming structure, it does not include the risk of misspecification of the production function [169]. Finally, the efficiency scores obtained through DEA can be used as a dependent variable in second-stage analyses, usually post hoc regression analyses, to determine the impact of environmental factors on hospital efficiency. DEA, however, has several disadvantages. First, the results are potentially sensitive to the selection of inputs and outputs. Furthermore, the number of efficient firms on the frontier tends to increase with the number of input and output variables. This is one of the reasons we do not compare results from manuscripts reviewed in this study. Second, being a nonparametric technique, DEA does not provide statistical tests of the significance of the input or output variables included in the model [397]. To partially overcome these limitations, prior studies have performed DEA on a variety of specifications to check the sensitivity of the results. Finally, the performance of DEA, an extreme point technique, deteriorates in the presence of measurement error and statistical noise [398]. Since DEA was first developed in 1978, a variety of models and extensions, including hybrid DEA models, have been proposed to mitigate some of its drawbacks and enhance its performance. Each model, though, has both advantages and disadvantages. For a thorough discussion on various models, we recommend the readers refer to Cooper et al. [399], Cook and Seiford [400], and Emrouznejad et al. [401]. Additionally, we refer readers to Panwar et al. [402] for an extensive examination of the advantages and disadvantages of DEA models, including hybrid DEA models.

Appendix 2

Table 2 Distribution of the journals publishing reviewed articles

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Pai, D.R., Pakdil, F. & Azadeh-Fard, N. Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022. Health Care Manag Sci 27, 284–312 (2024). https://doi.org/10.1007/s10729-024-09669-4

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