Using Graphically Enhanced Computer Software to Improve Healthcare Technology Assessments and Enhance Clinical Decisions

Using case studies, this paper describes how clinical engineers and healthcare CIOs, CTOs, and IT/IS specialists can use the Analytic Hierarchy Process (AHP) to improve the quality of diverse and important decisions that hospitals face today. AHP is a versatile and proven decision support tool that allows the user to design a hierarchical structure for decision-making and weighs the trade-offs between decision criteria and alternatives (Saaty 1977, Saaty 1996). The recommended steps for examining and weighting the relative importance of the multiple criteria affecting many decisions have been summarized as: 1) identifying the alternatives available and the personal criteria on which they will be evaluated; 2) determining how well the alternatives achieve personally meaningful criteria, based on an assessment of available data and personal preferences; 3) determining the importance of each criterion in the decisionmaking process; and 4) making a choice among the alternatives after synthesizing the results from the previous steps (Liberatore et al. 2002). The process also weights the alternatives for each criterion. These recommended steps help resolve the conflicts that often exist when choosing between multiple competing criteria and alternatives (Davies 1994, Keeny and Raiffa 1976, Dolan et al. 1989). Many books and articles exist that delve into the details and applications of AHP (Dolan et al. 1989, Foreman and Selly 2002, Liberatore and Nydick 2002), but the following brief introduction is provided as background. First, AHP is a PROCESS: the process requires elucidating personal criteria and evaluating the relative importance of each criterion and then determining how the alternatives achieve each of the criteria. Second, AHP organizes the decision into a HIERARCHY of criteria and alternatives: the criteria are organized according to perceived logical and natural groups to improve the clarity and usability of the model and to create properly-proportioned sub-categories that assure all important criteria are accounted and receive the proper weight in the decision. Third, AHP is ANALYTIC: it uses pairwise comparisons to help the user express the perceived relative importance of every criterion against every other criterion within each hierarchical group to establish the proportional weight each criterion should receive in the decision, and it uses the relative importance of each group to establish that group’s weighted importance. Every criterion must have an evaluation system established as well, and several alternative modes may be used for this. One mode uses pairwise comparisons of the alternatives’ relative performance for each criterion. Another mode allows creation of scoring or rating categories that are assigned the appropriate relative proportion for meeting a criterion’s goal. A third mode allows use of an equation to convert numeric performance of an alternative into a relative performance value. Pairwise comparisons can be time consuming, especially if there are many pairs. Sometimes, too, as will be illustrated in the following example, the user cannot be completely consistent in every case. Therefore, the pairwise comparisons are statistically evaluated for consistency as well, to help identify and resolve gross inconsistencies. This prevents inappropriate application of a model that may have serious hidden internal defects.