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<p>Hospice Care Centers (HCCs) are specialized healthcare systems that focus on enhancing the quality of life for patients with terminal illnesses. The effective operation of these facilities during the COVID-19 pandemic relies on proper management, task execution, staff coordination, and communication to mitigate stress, work pressure, occupational fatigue, and emotional distress caused by the virus. This study examines the impact of Macro-Ergonomics indicators and Cognitive Factors on HCCs’ performance. The data were collected by distributing a questionnaire among the employees of HCCs of Tehran (THCCs). This study evaluates the efficiency of each Decision-Making Unit using Artificial Neural Networks alongside statistical techniques. Additionally, several sensitivity analyses were conducted on THCCs. The results of sensitivity analysis and statistical tests demonstrated that THCCs had the best performance in terms of Job Pressure indicator, while having the poorest performance in terms of motivation. Using the Strengths-Weaknesses-Opportunities-Threats matrix, the authors also presented appropriate strategies for improving the performance of the HCC 1. The findings of this study provide managers with valuable insights into the strengths and weaknesses of their services in relation to these factors.</p> Graphical abstract <p></p>

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An intelligent framework to evaluate and improve the performance of hospice care centers based on macro-ergonomics and cognitive factors

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Maral Senobari,
  • Behnaz Salimi,
  • Mahdi Hamid,
  • Masoud Rabbani

摘要

Hospice Care Centers (HCCs) are specialized healthcare systems that focus on enhancing the quality of life for patients with terminal illnesses. The effective operation of these facilities during the COVID-19 pandemic relies on proper management, task execution, staff coordination, and communication to mitigate stress, work pressure, occupational fatigue, and emotional distress caused by the virus. This study examines the impact of Macro-Ergonomics indicators and Cognitive Factors on HCCs’ performance. The data were collected by distributing a questionnaire among the employees of HCCs of Tehran (THCCs). This study evaluates the efficiency of each Decision-Making Unit using Artificial Neural Networks alongside statistical techniques. Additionally, several sensitivity analyses were conducted on THCCs. The results of sensitivity analysis and statistical tests demonstrated that THCCs had the best performance in terms of Job Pressure indicator, while having the poorest performance in terms of motivation. Using the Strengths-Weaknesses-Opportunities-Threats matrix, the authors also presented appropriate strategies for improving the performance of the HCC 1. The findings of this study provide managers with valuable insights into the strengths and weaknesses of their services in relation to these factors.

Graphical abstract