Performance optimization of COVID-19 laboratories with safety participation and psychosocial safety climate: artificial neural network- gray wolf optimization method

Laboratories are essential to healthcare systems, making it crucial to enhance their performance and service quality while addressing safety concerns. This study has two main objectives: first, to evaluate the performance of six COVID-19 laboratories in an Iranian city based on safety climate participation, psychosocial safety climate, and job satisfaction; and second, to provide strategies for improving their performance. Data were collected from laboratory staff using a standardized questionnaire. A hybrid artificial neural network combined with grey wolf optimization method, was utilized to assess the effectiveness of the decision-making units. The performance was further analyzed through statistical tests and sensitivity analysis for each indicator. The approach was validated using data envelopment analysis, and improvement strategies were developed using a SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrix. These findings offer managers valuable insights for enhancing laboratory performance and addressing safety and quality concerns.