Datamining techniques - decision tree: new view on nurses' intention to leave

Abstract

Aim: The aim of the survey is to identify factors of the work environment which are important for general nurses when they are considering whether or not to leave their current employer. Design: The research consists of an observational and a cross-sectional study. Methods: Based on a modified interpretation of Herzberg's theory, we created a structured interview to investigate environmental factors. Interviewers carried out 1,992 interviews with hospital nurses working in the Czech Republic, between 2011 and 2012. The data gathered were analyzed with data mining tools - a decision tree and non-parametric tests. Results: If a good opportunity arose, 34.7% of nurses would leave their current employer. The analysis of the decision tree identified the factor "Patient care", i.e. a factor concerning the nature of the work itself, as the most important. Data mining offers a new view of the data and can reveal valuable information existing within the primary data. Conclusion: Data mining has great potential in nursing. In this research, the decision tree shows that the essence of the nursing profession is the nursing work itself and it is also the most significant stabilizing factor. The management of healthcare providers should create and maintain a work environment which will ensure nursing work can be performed without impediment, thus minimizing staff turnover.

Keywords

• nurse, hospital
• turnover
• intention to leave
• work satisfaction
• Herzberg's theory
• data mining
• decision tree