Data Envelopment Analysis Model for the Appraisal and Relative Performance Evaluation of Nurses at an Intensive Care Unit

Abstract

The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

Talent Management, Work Engagement and Service Quality Orientation of Support Staff in a Higher Education Institution

“…These results are in contrast with previous research which suggests that Talent Management enhance the service delivery of employees (Gracia et al, 2013;Osman et al, 2011;Popescu et al, 2012). More research is needed to investigate the interactive relationship between talent management and service quality orientation.…”

“…The effective talent management of staff are widely recognised as key to the effective service delivery and customer satisfaction (see Gracia et al, 2013;Osman, Berbary, Sidani, Al-Ayoubi & Emrouznejad, 2011;Popescu, Avram & Dana, 2012) Service Quality is defined by the relationship between the expectations of customers and how they perceive their experience (Parasuraman, Zeithaml & Berry, 1988). Service quality can be measured in terms of five main dimensions namely Tangibles, Assurance, Responsiveness, Reliability and Empathy (Parasuraman at al., 1988).…”

“…Schneider, White and Paul (1998) maintain that employees can only deliver a quality service if the organisation supports them through resources, training, management practices, and assistance. In particular talent practices such as skills training, competence development, career development plans and sound performance appraisal systems are key to the enhancement of service quality (Osman et al, 2011;Popescu et al, 2012). Other researchers also highlighted talent attraction and retention practices as an important determinant of quality service delivery (Scott & Revis, 2008;Zheng, 2009).…”

COBRA framework to evaluate e-government services: A citizen-centric perspective

“…Third, like other studies, this study is limited to identifying the most important factors that predict user satisfaction and ultimately e-government service success, so researchers are invited to build on the current study and provide an insight analysis and useful information through using operational research and/or data mining techniques. For example, data envelopment analysis technique is a useful tool for assessing, monitoring and controlling any e-service, (Osman et al 2011a). Furthermore, a classification and regression tree (CART), which is a data mining technique, is a useful tool to classify e-services and/or users according to their satisfaction level, hence policy makers can use this information by targeting unsuccessful e-government services and/or unsatisfied users.…”

Assessing the Queuing Process Using Data Envelopment Analysis: an Application in Health Centres

“…These include private versus public hospitals (Chang et al, 2004;Jehu-Appiah et al, 2015), primary healthcare centres in urban versus rural municipalities (Ramirez-Valdivia et al, 2012), acute care hospitals (Hollingsworth and Parkin 1995;Ersoy et al, 1997;Puig-Junoy 2000;Tsai and Molinero 2002), family health services authorities (FHSAs) (Salinez-Jaminez and Smith 1996) and district health authorities (DHAs) (Thanassoulis et al, 1996). A few other studies were concerned with the efficiency comparison of physicians (Chilingerian and Sherman 1997;Wagner et al, 2003), nurses (Osman et al, 2011), ambulatory surgery centers (ASCs) (Lewis et al, 2011), immunization activities in primary care practices (Rouse et al, 2011) and dialysis facilities (Flokou et al, 2011).…”