Fuzzy decision making in health systems: a resource allocation model

Ekin, Tahir; Kocadağlı, Ozan; Bastian, Nathaniel D.; Fulton, Lawrence V.; Griffin, Paul M.

doi:10.1007/s40070-015-0049-x

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…The resource changes for inputs F T E and EXP are limited from both sides to γ = 25 % to prevent major adjustments. Note that these values of r and γ were chosen based upon feedback received by the decision-makers at the DHA; these values were also used in Bastian et al [3] and Ekin et al [19]. Also, side constraints are added due to the non-discretionary nature of the input variable ENROLL (δ k=ENROLL,i = 0 ∀k ∈ K, i ∈ N).…”

Section: Multi-objective Auto-optimization Model Resultsmentioning

confidence: 99%

“…Lin et al [41] utilized a multi-objective simulation optimization using DEA and genetic algorithms to determine optimal resource levels in surgical services. An example to a F-DEA application in health system efficiency evaluation is the fuzzy resource allocation model of Ekin et al [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (17) prevent rapid decreases of inputs, while constraints (18) prevent rapid increases of inputs. Constraint (19) ensures that the minimum efficiency score r ∈ [0, 1] for all DMUs. Finally, the bounds for the decision variables are given in constraints (20).…”

Section: Maom Optimization Model Formulationmentioning

confidence: 99%

“…Note that these specified data parameters are typical of government hospitals [48] and are often used as variables in MHS performance measurement and evaluation studies [3,4,19,21,22]. As shown in Table 1, the MHS input resources to be manipulated include EXP , ENROLL, and F T E, whereas the output resources are listed as RV U and QU AL.…”

Section: Computational Experimentsmentioning

confidence: 99%

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Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Bastian

Ekin²,

Kang

et al. 2016

Health Care Manag Sci

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The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

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Section: Multi-objective Auto-optimization Model Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Maom Optimization Model Formulationmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Bastian

Ekin²,

Kang

et al. 2016

Health Care Manag Sci

Self Cite

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“…Since Nunamaker's first application of DEA in healthcare [ 19 ], the method has been broadly employed for efficiency evaluation of various decision-making units (DMUs) in healthcare. Studies have used DEA to evaluate the efficiency of hospitals [ 20 – 26 ]. It has also been used to compare efficiency performance between academic and nonacademic hospitals and “for profit” and “not for profit” hospitals [ 14 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Relationship between Productivity and Quality in Emergency Departments

Kang

Bastian

Riordan

2017

Journal of Healthcare Engineering

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Background. In the United States, emergency departments (EDs) are constantly pressured to improve operational efficiency and quality in order to gain financial benefits and maintain a positive reputation. Objectives. The first objective is to evaluate how efficiently EDs transform their input resources into quality outputs. The second objective is to investigate the relationship between the efficiency and quality performance of EDs and the factors affecting this relationship. Methods. Using two data sources, we develop a data envelopment analysis (DEA) model to evaluate the relative efficiency of EDs. Based on the DEA result, we performed multinomial logistic regression to investigate the relationship between ED efficiency and quality performance. Results. The DEA results indicated that the main source of inefficiencies was working hours of technicians. The multinomial logistic regression result indicated that the number of electrocardiograms and X-ray procedures conducted in the ED and the length of stay were significantly associated with the trade-offs between relative efficiency and quality. Structural ED characteristics did not influence the relationship between efficiency and quality. Conclusions. Depending on the structural and operational characteristics of EDs, different factors can affect the relationship between efficiency and quality.

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A Fuzzy Modeling Approach for Group Decision Making in Social Networks

Akkuzu

Aziz

Adda

2019

Business Information Systems

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Social networks have been commonly used, people use social networks with various purposes, such as, enjoying time, making business, and contacting their friends. All these activities are mainly based on sharing data. In social networks, making decision on data sharing process has become one of the main challenge because it involves people who have different opinions on the same problem. Diversified opinions cause uncertainties in decision making process. Fuzzy logic is used to overcome uncertainties' situations. In this work, we provide a fuzzy logic based decision making framework for SNs. The proposed fuzzy logic based framework uses data sensitivity value and trust value (confidence value) to make the group decision. Users express their opinions on data security features to obtain aggregated decision. Facebook data sharing process is chosen as a case study.

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Fuzzy decision making in health systems: a resource allocation model

Cited by 8 publications

References 33 publications

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Evaluating the Relationship between Productivity and Quality in Emergency Departments

A Fuzzy Modeling Approach for Group Decision Making in Social Networks

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