All clinical laboratory directors must manage a wide range of metrics to account for employee time spent working in the lab. The productivity metric is a measure of a unit of work as compared to a unit of time, and it can be utilized to determine department efficiency. While this metric is evaluated differently in every hospital department (eg, adjusted patient days, ED visits, number of surgical procedures, etc), the laboratory method is determined by productive hours over billable tests. Both billable tests (work) and productive hours (time) have detailed definitions, and those definitions may differ from facility to facility. Gaining a better understanding of these concepts and their driving principles will enable greater productivity and more effective conversations with C-suite decision makers.
A billable test refers to a CPT (current procedural terminology) code. Thus, a basic metabolic panel is considered one test, not eight. Furthermore, billable tests must be considered in relation to each other. In general, a chemistry assay does not take as long as a blood bank test, so should each test be treated the same?
With laboratorians taking on different roles in the hospital, the amount of time spent doing actual bench work varies. Should all “working” hours count as productive time? It becomes difficult to determine this if a staff member is, for example, working offsite, involved in training, or attending meetings or professional conferences. Certainly this is a conversation to be had with peers and superiors in the facility such that their expectation of productive time is clear. Based on my experience, the following should be noted:
As our goal is to compare efficiency of like laboratories, we need to be able to explain other key service differences within the hospital. Here are some common service lines to consider:
If you have access to other laboratories’ data, the above questions (and similar) should be considered so that an accurate comparison can be made. Volume alone does not determine what the ideal productivity should be. Thus, the goal of asking these questions is to compare like facilities. We need to establish this baseline to determine where and how we have room to improve. If the facility belongs to a health system, it may be easier to find a similar facility within that system.
As not all facilities are part of a health system, we will approach the following example as if it is a standalone facility and our only comparison can be made to prior performance. Our goal is to draw conclusions from data presented by The Fantasy Medical Center (FMC) in 2018, while determining the coming year’s goals. All of the following data is loosely based on a real facility, so actual conclusions may be drawn. For the purpose of our example, imagine a new director was brought on in late 2017 who had a productivity centered focus.
Overtime and the Story It Tells
To better understand the facility, the first metric to review is overtime. In TABLE 1, we see the lab overtime for the entire year. The facility has established a criteria goal of less than 2.5%. We see that in Q1, a high OT rate is reported (an average of 5.4%). This number decreases in Q2 (3.8%) and continues to improve in Q3 (3.1%). Other than October, Q4 continues the trend.
One assumption may be that there was high turnover toward the end of 2017. Hiring may have occurred in Q1 with training completing in Q2. The third quarter shows improvement, but a further look at non-productive hours may indicate summer vacations. October is an outlier that is unexplained by this data, but overall, an assumption could be made that 2019 overtime data would be superior to 2018 performance of 4.0% based on the available trends.
Productive vs. Non-Productive Hours
Reviewing productive vs. non-productive hours throughout the year may reinforce our conclusions, allowing us to better predict future vacation usage. In TABLE 2, we see that the fewest productive hours came in Q1, reinforcing the short staffing concept, perhaps due to turnover. Notably, we see a correlation of spikes in OT in any month where non-productive hours also spike. This is indicative of paid-time-off (PTO) hours being backfilled. Again, October seems like an outlier with a higher than expected rate. It could be reasonably assumed that a project was tackled in October based on the spike in both productive hours and overtime. For purposes of this example, assume an LIS conversion took place and that the full validation occurred during October. In this way we can correlate our overtime, productive, and non-productive hours to better understand the department.
Revenue and Usage
In viewing TABLE 3 from a workflow perspective, we can begin to assume that Q4 is slow for this facility, though additional years of data would be necessary to draw solid conclusions. Using this data, we can calculate our true productivity by using the simple calculation of productive hours divided by billed tests. TABLE 3 includes the prior year productivity for this facility as well so that we can see a true comparison.
We can see that there was no goal (and likely no tracking) of productivity in 2017 (see TABLE 4). In 2018, productivity averaged 0.139. We know that we can go significantly lower than 0.139, but reviewing Q1 data shows that overtime may be exceedingly high as a consequence. This can result in further employee turnover and workplace mistakes, thereby erasing any savings that may have been achieved by working short.
Predicting the Future
As seen in TABLE 5, assuming that no significant changes in workflow occur (eg, the addition of auto-verification, automated lines, new service lines, etc), we can expect the same growth in volume for 2019. We can also see that our productive hours dropped by 25%. The largest gains will always be achieved in the first year, and it is unrealistic to expect a similar drop in 2019. The CFO for FMC has just put out his criteria, and his expectation is a 0.12 productivity for 2019. Using this information, we can calculate our projected hour allowance, then review our staffing model to determine feasibility. Assuming the projected volume with our productivity goal allows us an average of 1037 hours weekly (25.9 FTEs). While the creation of a proper staffing model is the subject of another discussion, in this case let us assume that our staffing model produces a need of 1132 labor hours per week (28.3) FTEs for routine operation of the lab.
Our staffing model of 28.3 FTEs translates to 58,500 hours of labor per year and, combined with the volume projection, produces a projected productivity of 0.13 (a savings over the prior year). This is where the art of negotiation comes into play. We are able to go back to our CFO and state that this goal is achievable, but there are capital expenses necessary to reach it. By walking the CFO through the data, we can demonstrate the risks of only reducing staff (eg, unnecessary turnover from burnout). Thus, an argument can be made that in order to achieve the target goal, the lab requires automated lines, auto validation, improved instrumentation, or a myriad of other options.
As always, the determination may be made to simply use the revised goal of 0.13. Ultimately, performing a process review such as this will shed light on the productivity of your laboratory. That it may be used to increase laboratory leverage is an additional benefit.
Michael Veri, MLS(ASCP), MS, is the laboratory director at Landmark Medical Center located in Woonsocket, Rhode Island.
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