Nursing Errors in Intensive Care Unit by Human Error Identification in Systems Tool: A Case Study

Abstract

Background: Although health services are designed and implemented to improve human health, the errors in health services are a very common phenomenon and even sometimes fatal in this field. Medical errors and their cost are global issues with serious consequences for the patients’ community that are preventable and require serious attention. Objectives: The current study aimed to identify possible nursing errors applying human error identification in systems tool (HEIST) in the intensive care units (ICUs) of hospitals. Patients and Methods: This descriptive research was conducted in the intensive care unit of a hospital in Khuzestan province in 2013. Data were collected through observation and interview by nine nurses in this section in a period of four months. Human error classification was based on Rose and Rose and Swain and Guttmann models. According to HEIST work sheets the guide questions were answered and error causes were identified after the determination of the type of errors. Results: In total 527 errors were detected. The performing operation on the wrong path had the highest frequency which was 150, and the second rate with a frequency of 136 was doing the tasks later than the deadline. Management causes with a frequency of 451 were the first rank among identified errors. Errors mostly occurred in the system observation stage and among the performance shaping factors (PSFs), time was the most influencing factor in occurrence of human errors. Conclusions: Finally, in order to prevent the occurrence and reduce the consequences of identified errors the following suggestions were proposed : appropriate training courses, applying work guidelines and monitoring their implementation, increasing the number of work shifts, hiring professional workforce, equipping work space with appropriate facilities and equipment.