An intelligent framework to assess and improve operating room performance considering ergonomics

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC).