Prioritization of the factors affecting the performance of clinical laboratories using the AHP and ANP techniques

Abstract

Efficient and effective clinical laboratory (CL) services have a key position in health-care systems. It is important to define the CL performance indicators to improve patient satisfaction and minimize potential errors in laboratories. As a result of the literature review and expert judgements, the factors affecting the performance of CL are collected under four main criteria. These main criteria were defined as “Cost,” “Quality”, “Resource Management” and “Time.” Under these 4 main criteria, 21 sub-criteria were considered in total. In this study, two different multi-criteria decision-making methods, analytic hierarchy process (AHP) and the analytic network process (ANP), were used to prioritize and weight CL performance indicators and investigate their relations. These methods offer health-care systems a straightforward way to analyse the status of CL. According to the results, “Quality” achieved the top priority, followed by “Cost.” In addition, “Resource Management” and “Time” were stated as the third and fourth priorities, respectively. Moreover, the study analysed the global ranking of each sub-criterion. While “Errors During Testing Process”, “Quality Control Mistakes” and “Accidents” were determined as the vital factors in the AHP method, “Late Delivered Tests”, “Quality Control Mistakes,” and “Accidents” took the first three places with respect to the ANP method. The slight difference in the order of the sub-criteria can be explained by the network structure of the ANP method, which also considers the interdependence between the factors. Although the prioritization of the criterion can be performed with both methods, it can be asserted that the ANP method is more complementary since it considers the influences among the criteria.

Appendices

Appendix A: Pairwise comparison matrix

Appendix B: Pairwise relation matrix

*After five experts’ evaluations, the filled matrices were gathered and evaluated together to generate one main pairwise relation matrix. In the evaluation part, the plus signs were filled according to the decision of the majority.