Staffing to Workload: Direct Effort

During our "Staffing to Workload" series, we will be discussing the mechanics of performing a staffing-to-workload analysis in the testing laboratories. However, before beginning this topic, I would like to discuss why this topic is important to managing a laboratory.

Resources and funding are becoming more constrained, and laboratories are being challenged more and more to balance the needs of our patients with financial demands. Our patients and physicians rely on us to provide timely and accurate results, while the marketplace challenges us to reduce our costs and improve the patient experience. As labor tends to be in our top three expenses, this means, we need to do our best to have the right people, in the right place, at the right time.

A staffing analysis should strive to accomplish striking a balance between minimizing overstaffing situations that lead to financial waste and minimizing understaffing situations that lead to reduced patient care.

To get an accurate picture of the staffing needs, there are three primary areas of focus: direct effort, indirect effort, and operational needs. Each will be explained in detail in the subsequent posts, but the first up will be direct effort.

Direct effort refers to the labor effort related to hands-on time with the patient specimen(s) that transforms it from a specimen to a result. To determine how much direct effort is needed, one can use one of two calculations:

Basic Calculation for Direct Effort

A laboratory tech takes five minutes to perform the interpretation of a melt curve for a molecular virology test, including keying in the results into the laboratory system and finalizing the result. The tech does this 100 times per day:

5 minutes X 100 occurrences/day = 500 minutes/day

500 minutes/day ÷ 60 minutes/hour = 8.3 hours/day of direct effort

8.3 hours/day ÷ 8 hours/FTE = 1.04 FTE (headcount)

Note: FTE is an acronym for "full-time equivalent"; it is the equivalent of 40 hrs/week. This can be accomplished with one person working 40 hours or two people working 20 hours each, etc.

There are strengths and weaknesses of this type of calculation:

  • Highly automated assays: About 20% of the labor effort for highly automated assays can be accounted for in this way (though there are exceptions). The remaining 80% of the labor effort is accounted for in the indirect effort and operational needs, which will follow in the next post in this series.
  • Semi-automated assays: About 50% of the labor effort for semi-automated assays can be accounted for in this way (though there are exceptions). The remaining 50% of the labor effort is accounted for in the indirect effort and operational needs.
  • Manual assays: About 80% of the labor effort for manual assays can be accounted for in this way (though there are exceptions). The remaining 20% of the labor effort is accounted for in the indirect effort and operational needs.

Service Level (Turnaround) Impacts on Direct Effort

The basic calculation above assumes an engineering concept called "level loading," meaning that if more work is received than can be completed, that work is carried over to a later time. For some assays, this is not an issue, while for others, this is a big issue. The decision on the criticalness of the service level typically revolves around how the physicians will act on the results, how quickly they will act, and the ramifications to the total episode of care for the patient (extended stay in hospital).

If the assay in question falls in the category of a critical (STAT or Urgent) result, the basic calculation changes to what is below.

A laboratory tech takes five minutes to perform the interpretation of a melt curve for a molecular virology test, including keying in the results into the laboratory system and finalizing the result. The tech does this 100 times per day, of which 80 show up in the first 2 hours of the 8-hour day, while the remaining 20 show up throughout the remainder of the day:

5 minutes X 80 occurrences/day = 400 minutes/first 2 hours

400 minutes/first 2 hours ÷ 60 minutes/hour = 6.7 hours of direct effort in first 2 hours

6.7 hours ÷ 2 hours = 3.3 FTE (headcount) needed in the first 2 hours of the day

This calculation presents several questions, including, where do the extra 2.3 FTE come from (the difference between 1 FTE needed in the level-loaded model and the 3.3 in the critical-service level model)? Do we have extra staff who can help in those first couple of hours? Do we hire 3.3 staff and try to figure out what to do with them for the remaining 6 hours of the day? Is cross-training the staff an option? All are questions that would need to be answered.

Multitasking: Good or Bad?

Another assumption made in either the level-loaded or critical-service level calculations is that staff can multitask, which is to say that when techs encounter a situation where they are waiting for an action to complete before they can proceed to the next step, they can do something else productive. This is a very tempting thing to do, but it all depends on how long that waiting period is.

For example, if you have a five-minute centrifuge step, you could either have the techs wait for the centrifuge or give them something else to do. If that other task is in the general area, the centrifuge can alert the techs when it is done. This allows the task to be set aside quickly. However, studies have shown that the time it takes to reorient oneself to a new task and then return to the old task can affect a person's productivity at either. If it is determined that working on the other task will be detrimental to the original task, the staff should not multitask. The centrifuge time would then be classified as direct effort, and you would need to staff to it.

On the other hand, if you have a two-hour incubation on a manual ELISA assay, this is the perfect opportunity for multitasking. The key here is to make sure there is an effective mechanism to notify the techs when the incubation is getting close to being done so that they can wrap up what they are working on and pick up on the ELISA assay.

My next blog post will discuss indirect effort. As teased above, there can be some significant hidden or overlooked staff needs here that are critical to providing patient care.

michaeljba

Mike Baisch

Mike Baisch is a Systems Engineer within the Department of Laboratory Medicine and Pathology at Mayo Clinic. He partners with the various testing laboratories on their quality and process improvement projects including staffing to workload, root cause analysis, and standard work & training. Mike has worked at Mayo Clinic since 2005 and holds a degree in Industrial Engineering. Outside of work, Mike enjoys cooking and sampling cuisine from around the world, home brewing, and gardening.