AAMI's benchmarking solution: Analysis of cost of service ratio and other metrics

Abstract

ABS has tremendous possibilities as a clinical engineering benchmarking service and metric research tool. With the large amount of data collected in less than one year and future plans to collect more data, as well as improving data validation, future years will see opportunities for further confirmation of existing metrics as well as new metrics. There are many other possibilities for metrics that can be calculated in ABS. Also, in the future developments of ABS, one or more other common denominators used in other healthcare benchmarking (e.g. adjusted discharges) will also be considered for inclusion. These common denominators are important to hospital administrators and other C-suite executives who use these numbers to justify staffing levels and other resource allocations (positive or negative). Of course, any new metrics developed will need to maintain relevance to clinical engineering workloads in order to be of any practical value. In order for ABS, or any benchmarking service, to make further inroads into the establishment of commonly used and referenced metrics, there is a need for high quality data. ABS shows that although large improvements have been made in data quality there are many strange outliers including: $460 per hour of internal cost, $30 million of support per technician, zero shop space, 0.3% cost of service ratio, and more. CE departments and computerized maintenance management systems (CMMS) vendors need to do a much better job of collecting and providing accurate data and quality tools in order to improve the ease and accuracy of data collection. The inability of many departments to accurately measure cost is still a major issue within the CE community. Another challenge for ABS is the anonymity of the data. Although being anonymous helps recruit more participants, one of the attributes of benchmarking is its ability to identify best practices. With ABS, the data can be manipulated so that potential best practice partners can be identified based on demographics and other data selections. In order for the people doing an across-ABS analysis to confirm that a best practice is really a best practice, and not a data anomaly, and in order for individuals to really learn something from a potential partner, that partner will need to be identified. In the future, AAMI will have to determine a way to promote communication, with permission of course, between potential benchmarking partners. Already, ABS subscribers have access to an e-forum where they share questions, comments, and advice.