AAMI's Benchmarking Solution: Analysis of Cost of Service Ratio and Other Metrics

Abstract

AMI's Benchmarking Solution (ABS) is a new online tool designed specifically to help clinical engineering (CE) departments measure their practices, policies, and procedures against similar depart- ments at other facilities; monitor their progress through- out the year; and share best practices. The tool includes an online survey with more than 100 benchmarking and best practice questions, both qualitative and quantitative, and a set of graphical analysis tools to help CE depart- ments analyze benchmarking data results against other facilities of particular demographics. The survey tool—developed by a software company, NeuraMetrics, with the guidance of a team of clinical engineering experts hired by AAMI—is structured to en- courage both numeric and non-numeric responses, allow- ing those departments that do not have all the numeric information to participate and enter the data that they do have. This overcomes the problem of some prior clinical engineering benchmarking studies which required many numeric responses that were difficult for some potential participants to provide. Although a very new product, ABS already has a high participation rate. This study looks at the first set of ABS data focusing on and further analyzing some of the quantitative mea- surements available. One key statistical analysis technique used in this study is linear correlation. Linear correlation quantifies the strength of a linear relationship between two variables. When there is no correlation between the two variables, then there is no tendency for the values of one quantity to increase or decrease with the values of the second quantity. When there is 100% correlation, every increase or decrease in one variable has an equivalent in- crease or decrease in the other one. Linear correlation is typically represented by a number between 0.00 (no cor- relation) and ± 1.00 (100% correlation). Sample size in lin- ear correlation studies is relevant; one of the weaknesses of some prior studies 1 is their small sample size. ABS has overcome the small sample size issue with more than 100 institutions subscribing in less than one year of operation. At the time of this analysis (April 2010), 86 organiza- tions had entered data into the ABS database. The pro- cess for data analysis included the following: