A hybrid data envelopment analysis and game theory model for performance measurement in healthcare

Zare, Hakimeh; Tavana, Madjid; Mardani, Abbas; Masoudian, Sepideh; Saraji, Mahyar Kamali

doi:10.1007/s10729-018-9456-4

Cited by 42 publications

(23 citation statements)

References 48 publications

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“…Th ey used BWM to calculate the weights of the evaluation indicators and then evaluated the performance of each unit using DEA. Zare et al (2018) used a combination of DEA and Game theory to measure the performance of health centres in Iran. Omrani et al (2018) used DEA based on clustering for DMUs under uncertain condition; they expanded the method to measure the performance of hospitals in diff erent provinces.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel DEA model for hospital performance evaluation based on the measurement of efficiency, effectiveness, and productivity

Ghahremanloo

Hasani

Amiri

et al. 2019

Engineering Management in Production and Services

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Hospitals are the most important and costly component of the healthcare system. Therefore, hospital performance evaluation (HPE) is an important issue for the managers of these centres. This paper presents a new approach for HPE that can be used to calculate the efficiency, effectiveness, and productivity of hospitals simultaneously. Efficiency refers to the ratio of inputs and outputs, effectiveness refers to the extent to which outputs align with predetermined goals, and productivity refers to the sum of both efficiency and effectiveness. To this end, a Data Envelopment Analysis (DEA) model is developed to simultaneously measure the efficiency, effectiveness, and productivity (DEA-EEP) of hospitals. DEA is a linear programming technique that in its traditional form, calculates the performance of similar decision-making units (DMUs) that have both inputs and outputs. In this study, the inputs are the number of health workers, the number of other staff, and the number of patient beds; while the outputs are the bed occupancy rate and the bed turnover rate. A target value is set for each output to measure the effectiveness of hospitals. The advantage of the developed model is the ability to provide a solution for non-productive units so that they can improve their performance by changing their inputs and outputs. In the case study, data of 11 hospitals in Tehran were evaluated for a 3-year period. Based on the results, some hospitals experienced an upward trend in the period, but the efficiency, effectiveness, and productivity scores of most hospitals fluctuated and did not have a growing trend. This indicates that although most hospitals sought to improve the quality of their services, they needed to take more serious steps.

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Section: Literature Reviewmentioning

confidence: 99%

A novel DEA model for hospital performance evaluation based on the measurement of efficiency, effectiveness, and productivity

Ghahremanloo

Hasani

Amiri

et al. 2019

Engineering Management in Production and Services

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“…This instrument, besides offering a relative measure for hospitals' technical efficiency based on the maximum, yet feasible, production configuration, can determine peers of efficiency for benchmarking performance metrics and identifying opportunities for improvement and support resource allocation schemas based on production slacks. Many assessments of health efficiency follow this avenue, for instance, the recommendations of Kounetas and Papathanassopoulos [ 19 ] regarding the efficiency of Greek hospitals and Zare et al [ 20 ] using a hybrid data envelopment analysis and game theory methodology for performance measurement in health service units. This interesting perspective of frontier estimations can provide the optimal number of resources to be (re-) allocated from inefficient units through best practices during epidemic crises.…”

Section: Literaturementioning

confidence: 99%

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak

Nepomuceno

Silva

Nepomuceno

et al. 2020

Journal of Healthcare Engineering

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Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

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“…Haghighi and Torabi [37] proposed a hybrid sustainability-resilience framework for assessment hospital information system (HIS) using the best-worst method for determining importance weight of each assessment indicator and the DEA for performance evaluation in different departments of hospital. Zare et al [38] developed a hybrid DEA-Game theory framework to analyze the total performance of healthcare centers in Iran. Rouyendegh et al, [39] proposed a DEA incorporates an analytic hierarchy process (AHP) to improve the total business performance in healthcare system network.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-efficiency Assessment of Sustainable Dynamic Network Healthcare Service System under Uncertainty: Hybrid Fuzzy DEA-MCDM method

Hasani

Mokhtari

2020

Scientia Iranica

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In this paper, sustainability-related factors driving success of healthcare system management include a group of hospitals are considered. A three-pronged approach is considered based on the internal functions of the hospital, which are affecting the social responsibility as well as functions related to the service recipients from health centers. A novel comprehensive multiperiod evaluation of hospitals' performance is considered by the proposed dynamic network. This hybrid data envelopment analysis-based fuzzy multi-criteria decision-making model incorporates fuzzy DEMATEL and best-worst method provides several useful managerial insights using a realistic case study from Iran for sustainable management of the healthcare system. Obtained results indicate the importance of considering potential interrelated relations between network nodes on comprehensive performance assessment of healthcare service system.

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A hybrid data envelopment analysis and game theory model for performance measurement in healthcare

Cited by 42 publications

References 48 publications

A novel DEA model for hospital performance evaluation based on the measurement of efficiency, effectiveness, and productivity

A novel DEA model for hospital performance evaluation based on the measurement of efficiency, effectiveness, and productivity

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak

Self-efficiency Assessment of Sustainable Dynamic Network Healthcare Service System under Uncertainty: Hybrid Fuzzy DEA-MCDM method

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