Whenever the economic aspects of business get tough, do more with less is a phrase heard everywhere. Of course, doing more with less just means becoming more productive. There is no scarcity of literature on productivity, and some authors claim to have identified more than 20 definitions for productivity. Economists use the term to measure the capacity of nations to use their human and physical resources to produce economic growth. A simpler definition, though, is that productivity equals output divided by input. The output is the product or service delivered, and the input is what was consumed in creating the output.
Many different inputs are consumed in producing a product or delivering a service, but it’s simplest to consider one at a time. The output of an imaging operation could include procedures performed, patients served, reports signed, revenue generated, or even something more abstract, such as RVUs generated. Input is most often expressed as some measure of labor; this could be hours, FTEs, or labor dollars. Even for this simple definition, there are still many analysis options. Which outputs and inputs should be used depends on your reasons for measuring productivity, as well as on who will be using the measurement.
Why measure productivity? If the goal is to do more with less, you won’t know whether you have achieved the goal without a measurement. When you consider declining reimbursements and rising salaries/benefits, maintaining profitability becomes a concern. Imaging operations are characterized by large fixed costs that are not easily lowered. One quickly realizes that the salaries/benefits area is one where there is a possibility of improvement.
Probably the best reason for measuring productivity is to substantiate your success at improving it. This idea highlights an important aspect of measuring productivity: You need to take multiple measurements over time. The absolute value of the productivity measurement is less important than how the measurements are trending.
Consider a few examples. The first example takes a high-level financial view. Two readily available numbers, for many operations, are the revenue generated and the total dollars spent for salaries/benefits. Revenue is the output, and salaries/benefits dollars are the input, so our high-level productivity measure is revenue divided by salaries/benefits.
If your revenue for the month was $500,000, and you spent $100,000 on salaries/benefits, the productivity measurement would be five: For every dollar spent on salaries/benefits, you generated $5 in revenue. This single measure is not all that useful, but if you checked it every month, you could identify the trend in labor productivity.
This financial measurement really does not tell you why productivity is trending the way it is, nor does it say much about how hard your people are working. Financial productivity could be trending downward because reimbursement for the service offered is decreasing, for example, or because the cost of benefits for your employees is going up (and both are likely scenarios for imaging operations today).
The next example yields another high-level performance measurement that is not monetary. This example employs RVUs as the output and FTEs as the input. RVUs are very useful in considering multiple modalities at the same time because they account for the differences in complexity of the exams. One FTE equals 40 hours of labor, but when I calculate FTEs, I usually exclude paid time off (since as-needed staffing and overtime are used to cover the person on vacation, who is not producing any output). The formula, then, is RVUs divided by FTEs.
If your center produced 1,000 RVUs in a week, with 20 FTEs, your productivity number would be 50 RVUs per FTE. By watching this number from week to week, you get a feel for the natural variation in the measurement and can determine the trend. This measurement focuses on the performance of people and removes (or, as some might say, hides) the effects of declining reimbursement and the rising cost of salaries/benefits. It is still a high-level measurement, and it does not provide much insight into what is causing changes in productivity.
An obvious refinement would be to group the FTEs into functional areas. In this example, FTEs are broken into groupings of those performing clinical work (technologists and nurses)