title: A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan.
abstract: This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs' fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.
authors.affiliation: Cheng Ching General Hospital-Chung Kang Branch, Taichung City, Taiwan.|Chung Shan Medical University, Taichung City, Taiwan.|Hung Kuang University, Taichung City, Taiwan.
authors.forename: Yii-Ching
authors.initials: YC
authors.lastname: Lee
authors.affiliation: National Changhua University of Education, Changhua City, Taiwan.
authors.forename: Shian-Chang
authors.initials: SC
authors.lastname: Huang
authors.affiliation: Hubei University of Economics, Wuhan City, China.
authors.forename: Chih-Hsuan
authors.initials: CH
authors.lastname: Huang
authors.affiliation: National Changhua University of Education, Changhua City, Taiwan hhwu@cc.ncue.edu.tw.
authors.forename: Hsin-Hung
authors.initials: HH
authors.lastname: Wu
mesh_terms: D000328:Adult; D002055:Burnout, Professional / Q000175:diagnosis*; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D005260:Female; D006784:Hospitals, Teaching; D006801:Humans; D008297:Male; D008505:Medical Staff, Hospital / Q000523:psychology*; D008875:Middle Aged; D061214:Patient Safety; D011795:Surveys and Questionnaires; D013624:Taiwan; D055815:Young Adult
keywords: Maslach Burnout Inventory; Safety Attitudes Questionnaire; burnout; emotional exhaustion; kernel k-means cluster analysis; patient safety; resilience