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An Integrated Data Mining Framework for Organizational Resilience Assessment and Quality Management Optimization in Trauma Centers

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Abstract

Every second counts for patients with life-threatening injuries, and trauma centers deliver timely emergency care to patients with traumatic injuries. Quality assessment and improvement are some of the most fundamental concerns in trauma centers. In this study, a comprehensive organizational resilience approach is proposed to evaluate performance in trauma centers using the European Foundation for Quality Management as a fundamental and strategic approach. We propose a unique intelligent algorithm composed of parametric and non-parametric statistical methods to determine the type and the extent of influence within the organizational resilience and quality management perspectives. We use structural equation modeling to examine the reliability and validity of the input data. The efficiency of each trauma center is then measured using a machine learning method with genetic programming, support vector regression, and Gaussian process regression. The mean absolute percentage error is used to determine the optimal model, and a fuzzy data envelopment analysis model is used to verify and validate the results obtained from the optimal model. The results show that customer results, human capital results, and key performance results have the highest importance weights and positive influence on quality management. Cognitive resources, roles and responsibilities, and self-organization have the highest importance weights and positive influence on organizational resilience.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Dr. Madjid Tavana is grateful for the partial support he received from the Czech Science Foundation (GAˇCR19-13946S) for this research.

Author information

Authors and Affiliations

  1. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA, 19141, USA

    Madjid Tavana

  2. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 33098, Paderborn, Germany

    Madjid Tavana

  3. Department of Industrial and Systems Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Salman Nazari-Shirkouhi & Amir Mashayekhi

  4. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Saeed Mousakhani

Authors
  1. Madjid Tavana
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  2. Salman Nazari-Shirkouhi
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  3. Amir Mashayekhi
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  4. Saeed Mousakhani
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Corresponding author

Correspondence to Madjid Tavana.

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Conflict of Interest

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Appendices

Appendix 1

Table 15 The data collection questionnaire
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Appendix 2

Table 16 Gaussian process regression (Python code)
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Table 17 Support vector machine (Python code)
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Table 18 Genetic programming (Python code)
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Tavana, M., Nazari-Shirkouhi, S., Mashayekhi, A. et al. An Integrated Data Mining Framework for Organizational Resilience Assessment and Quality Management Optimization in Trauma Centers. Oper. Res. Forum 3, 17 (2022). https://doi.org/10.1007/s43069-022-00132-0

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