A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process

Yang, Xiaopeng; Zheng, Danheng; Sieminowski, Tammy; Paradi, Joseph C.

doi:10.1016/j.ejor.2014.07.010

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…(2013), which uses a model based on a novel specification of weight restrictions, Dai and Kuosmanen (2014), which combines DEA with clustering methods, Yang et al. (2015), which uses DEA to create a dynamic benchmarking system, Gouveia et al. (2015), which combines DEA and multi criteria decision analysis, Daraio and Simar (2016) and Lozano and Soltani (2020), which deals with benchmarking and directional distances, Gomes Júnior et al.…”

Section: Introductionmentioning

confidence: 99%

“…Data envelopment analysis (DEA) (Charnes et al, 1978) has been widely used as a benchmarking tool for improving performance of decision making units (DMUs). Some recent papers dealing with DEA and benchmarking include Adler et al (2013), which uses network DEA, Zanella et al (2013), which uses a model based on a novel specification of weight restrictions, Dai and Kuosmanen (2014), which combines DEA with clustering methods, Yang et al (2015), which uses DEA to create a dynamic benchmarking system, Gouveia et al (2015), which combines DEA and multi criteria decision analysis, Daraio and Simar (2016) and Lozano and Soltani (2020), which deals with benchmarking and directional distances, Gomes Júnior et al (2016), which uses nonradial efficiencies in a benchmarking analysis based on alternative targets, and Ghahraman and Prior (2016) and Lozano and Calzada-Infante (2018), which propose stepwise benchmarking approaches.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances

Ruiz

Sirvent

2020

Int Trans Operational Res

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Data Envelopment Analysis (DEA) is widely used as a benchmarking tool for improving performance of organizations. For that purpose, DEA analyses provide information on both target setting and peer identification. However, the identification of peers is actually a by-product of DEA. DEA models seek a projection point of the unit under evaluation on the efficient frontier of the production possibility set (PPS), which is used to set targets, while peers are identified simply as the members of the so-called reference sets, which consist of the efficient units that determine the projection point as a combination of them. In practice, the selection of peers is crucial for the benchmarking, because organizations need to identify a peer group in their sector or industry that represent actual performances from which to learn. In this paper, we argue that DEA benchmarking models should incorporate into their objectives criteria for the selection of suitable benchmarks among peers, in addition to considering the setting of appropriate targets (as usual). Specifically, we develop models having two objectives: setting the closest targets and selecting the most similar reference sets. Thus, we seek to establish targets that require the less effort from organizations for their achievement in addition to identifying peer groups with the most similar performances, which are potential benchmarks to emulate and improve.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances

Ruiz

Sirvent

2020

Int Trans Operational Res

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“…Comparisons between actual performances and targets may show the DMUs the way for improvement. Some recent papers dealing with DEA and benchmarking include Adler et al (2013), which uses network DEA, Zanella et al (2013), which uses a model based on a novel specification of weight restrictions, Dai and Kuosmanen (2014), which combines DEA with clustering methods, Yang et al (2015), which uses DEA to create a dynamic benchmarking system, Gouveia et al (2015), which combines DEA and multi criteria decision analysis (MCDA), Daraio and Simar (2016), which deals with benchmarking and directional distances, and Ghahraman and Prior (2016) and Lozano and Calzada-Infante (2018), which propose stepwise benchmarking approaches.…”

Section: Introductionmentioning

confidence: 99%

Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA

Ramon

Ruiz

Sirvent

2020

Omega

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In benchmarking, organizations look outward to examine others' performance in their industry or sector. Often, they can learn from the best practices of some of them and improve. In order to develop this idea within the framework of Data Envelopment Analysis (DEA), this paper extends the common benchmarking framework proposed in Ruiz and Sirvent (2016) to an approach based on the benchmarking of decision making units (DMUs) against several reference sets. We refer to this approach as cross-benchmarking. First, we design a procedure aimed at making a selection of reference sets (as defined in DEA), which establish the common framework for the benchmarking. Next, benchmarking models are formulated which allow us to set the closest targets relative to the reference sets selected. The availability of a wider spectrum of targets may offer managers the possibility of choosing among alternative ways for improvements, taking into account what can be learned from the best practices of different peer groups. Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications.

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Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive

Rodger

2015

Informatics in Medicine Unlocked

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A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process

Cited by 7 publications

References 58 publications

Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances

Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances

Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA

Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive

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