Ted Cohen

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

AMIs 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:

Staffing Metrics: A Case Study

Ted Cohen, CCE, is clinical engineering manager at the UC Davis Medical Center in Sacramento, CA. He is a benchmarking expert who was instrumental in the development of AAMI’s Benchmarking Solution. E-mail: tedcohen@pacbell.net. One of the uses for benchmarking and metrics is to determine if your staffing levels are appropriate compared to your workload and to similar institutions. In these times of budget constraints, and with personnel costs often being the largest percent of the budget, it is very important to have metrics that quantify staffing needs based on workload. AAMI’s Benchmarking Solution (ABS) directly or indirectly provides several quantitative metrics and qualitative survey responses that can assist in establishing staffing metrics. These include acquisition value of equipment, full-time equivalent (FTE) counts, device counts per FTE, derived hourly cost, cost of service ratio (COSR), and others (Table 1). The following is an example of a sequence of steps one might take to use ABS to establish and apply staffing metrics: 1. Within ABS, determine the demographics selection to use (e.g., acute care hospitals, hospital size, location, timeline etc.), or use all ABS respondents for a particular timeline (e.g. ,2010). 2. Measure and report your current and historic COSR. (For more detail, see the article “AAMI’s Benchmarking Solution: Analysis of Cost of Service Ratio and Other Metrics” in the July/August 2010 issue of BI&T.) A COSR of less than about 6% validates that the overall expenses for current workload are within reason, i.e., average or better compared to all ABS respondents. 3. Measure and report current and historic staffing ratios based on the amount of equipment supported. There are several measurements that can be made, including amount of equipment (in acquisition dollars) supported by one FTE; equipment counts supported by one FTE (only useful for low cost equipment); workload estimates by type of equipment based on historical data, manufacturer information and other published workload data (e.g., ECRI Institute data). 4. If the measurements are being used to justify additional staff, measure and report the net increase in workload estimated for the new project’s equipment. 5. Split the net increase in workload into “one-time startup workload” (e.g., planning, installation, incoming inspection) and long-term, continuing repair and maintenance workload. Make sure to remove from the long-term workload analysis any replaced equipment that will be leaving. 6. From the long-term workload increase, and the metrics listed above, calculate estimates In these times of budget constraints, and with personnel costs often being the largest percent of the budget, it is very important to have metrics that quantify staffing needs based on workload. Human Factors Guidance

The future of clinical engineering: Technology that enables improved patient care

Speakers at this years ACCE Symposium will describe in detail the scope and depth of technological and other factors that are at work changing the healthcare delivery paradigm. Other presenters will explore the impact the changes will likely have on the clinical engineering profession. Still others will outline the steps necessary for clinical engineers to take to effectively prepare for the challenges facing them. The experts agree: clinical engineering is at a critical crossroads. No one who intends to pursue clinical engineering or healthcare technology management over the next 10 years can afford to miss this years meeting.