Applying the Staffing-to-Workload Methodology: Basic Staffing Model

In my previous blog posts, I discussed the theory on how to perform a staffing-to-workload analysis. In particular, the focus was on the three components: direct, indirect, and operational needs. With this series of posts, I would like to start applying the staffing-to-workload methodology to a series of case studies. I will start with a “basic” staffing model, and over the series, progressively take the case study through more of the advanced topics discussed earlier.

With that said, let’s begin. Below is an example of a staffing-to-workload model created for an offsite testing laboratory we have for a remote outpatient care clinic. The laboratory has a total of 8 tests it performs; it is open 6 days a week for one shift; and there is low urgency with completing the testing. The low urgency allows us to complete testing as we can, even if that means we need to leave some specimens overnight to be picked up first thing the next day.

Direct Effort

Volume

We start by retrieving our average daily volume for each of the eight tests. If you have access to reports from your laboratory information system (LIS), that would be the easiest way to get this information. Then, have the report run for a period of three months to get an equal number of weekdays in it, and then average them all out. If you don’t have access to an LIS report, you may be able to get data from the instruments themselves or manual counts. If you need to go down this route, a shorter period of a few weeks is probably all that is feasible. Just keep in mind that there could be some unaccounted-for variation with using a shorter period.

Direct Time

Now, we need to identify how much direct time (a.k.a., hands-on time) is involved with each test. At a high level, direct effort can be bucketed into pre-analytic, analytic, and post-analytic tasks. In our example, all eight tests are instrument based, so once the test is loaded on the instrument, it is hands off. If we observe each test being performed by a number of different techs, we can then get an average of those pre- and post-analytic tasks and add them together to get our total hands-on time, or "direct time."

Calculating the Direct Effort

Now, we can start the direct-effort calculations. The table below outlines how to take volume and direct time to determine the amount of full-time equivalent (FTE) staff needed for direct tasks:

Calculation Details Volume × Direct Time Total Direct Time (minutes) ÷ 60 FTE (8 hours/shift) Total Direct Time (hours) ÷ FTE
Volume (Test Count) Direct Time (Minutes) Total Direct Time (minutes) Total Direct Time (hours) FTE (hours/shift) Total Direct Effort (FTE)
Test 1  212  0.5 106  1.8  8  0.2
Test 2 158 1 158  2.6 8  0.3
Test 3 23 5 115  1.9 8 0.2
Test 4 89 4 356 5.9 8 0.7
Test 5 116 2 232 3.9 8 0.5

 

Test 6 6 8 48 0.8 8  0.1
Test 7 12 10  120 2.0 8  0.3
Test 8 71 3  213  3.6 8 0.4
Total Remote Clinic Lab Direct Effort 2.8 

Indirect Effort

Indirect Tasks

We need to create a list of tasks that staff members perform that isn’t directly related to the patient, specimen, or test results. We can find out what these tasks are using a number of sources:

  • Standard operating procedures (SOPs) can identify:
    • Daily maintenance.
    • Monthly maintenance.
    • Comparability studies.
  • Asking the staff can identify:
    • Answering the phone.
    • Stocking supplies.
    • Miscellaneous tasks that aren’t always the same each time, but do happen.
  • Observation can identify:
    • CAP inspection.
    • Proficiency testing.

Task Duration and Frequency

Once we have a list of tasks, we need to know how long it takes to perform them. We can either use estimates or observation. I prefer observation for those frequently occurring tasks, such as daily maintenance, as it gives you a more accurate value and can help better familiarize yourself with the task(s). Estimates are typically used with tasks that happen infrequently or are sporadic. You can also blend observation with estimation by having staff members perform self-observation. Provide staff with a data-collection sheet and have them record what task(s) they were performing and how long it took. You may not get many timings, but it will influence your estimates to a more accurate value. A side benefit is it may also catch a few tasks you may have missed.

Calculating Indirect Effort

Now, we can start the indirect-effort calculations. The table below outlines how to take the task list and combine it with the task duration and frequency to determine the amount of FTE staff needed for the indirect tasks. It should be noted that a common denominator is needed to make the frequency standardized. In this case, I’m annualizing the frequency by calling out how many days, weeks, months, etc. there are for a task's frequency:

Calculation Details Number of Occurrences/ Year Duration × Animalization Effort (Minutes) ÷ 60 FTE (2,080 Hours/Year) Effort (Hours) ÷ FTE
Task List Duration (Minutes) Frequency Annualized (Frequency/Year) Effort (Minutes) Effort (Hours) FTE (Hours/Year) Indirect Effort (FTE)
Daily Startup and Calibration  30  Daily 312  9,360  156  2,080 0.08
Weekly Maintenance 60 Weekly 52  3,120 52 2,080 0.03
Stocking Supplies 45 Daily 260  11,700 195 2,080 0.09
CAP Inspection 2,400 Yearly 1 2,400 40 2,080 0.02
Answering the Phone 5 Daily 9,360 46,800 780 2,080

 

0.38
Proficiency Testing 60 Monthly 24 1,440 24  2,080 0.01
Miscellaneous Tasks 30 Daily  312 9,360 156  2,080 0.08
Total Remote Clinic Lab Indirect Effort  0.67

Operational Needs

Finally, we need to take into account all of those things that keep us away from the bench but are required to run a successful operation. Outlined in the table below is a typical list of these operational needs. The time values associated with these needs can typically be found in the timekeeping records or derived from the requirements and schedules:

Direct + Indirect FTE Hours Allotted to Each FTE/Year FTE Base × Allowance FTE (2,080 Hours/Year) Allotment ÷

FTE

Operational Needs FTE Base Allowance/FTE Allotment Hours FTE (Hours/Year) FTE Allotment
PTO 3.47  240  832.8  2,080  0.40
Unplanned Time Off 3.47  20 69.4 2,080 0.03
FMLA 3.47  40 138.8 2,080  0.07
Other Time Away from Work 3.47 10 34.7 2,080  0.02
CE Credits for Certificate Maintenance 3.47 12 41.6 2,080

 

 0.02
Training for Updated Procedures 3.47 45 156.2 2,080 0.08
Lab Meetings 3.47 70 242.9 2,080  0.12
Total   0.74

What It All Means

Given the requirements outlined and the information at hand for the direct, indirect, and operational needs, we can say that:

4.21 FTEs are needed for a successful remote lab.

0.74 FTEs are scheduled to be away from the bench or off work altogether.

0.67 FTE are performing indirect tasks needed to keep the lab and tests operational.

2.80 FTEs are needed to perform the testing done on an average day.

There will be below-average volume days where the lab will look overstaffed, and there will be above- average days where the lab will look understaffed. As we aren’t required to get all the direct work and some of the indirect work done each day, this allows us to balance our low-volume and high-volume days by spreading out that work.

An FTE is a calculated number, but it may not actually be the number of staff members we have. We may have a staff of 6 people that adds up to 4.21 FTE. We do this by saying 3 of the staff work full-time, giving us 3 FTE. The other 3 staff members work part-time: 2 of them work half-time, and the other employee works quarter-time. This gives those 3 staff members the equivalent of 1.25 FTE. When you add both staff groups together, we have a 4.25 FTE out of a 6-person staff.

Next Post

In the next post, we will add some new variables to the mix. Those new variables will be:

  • The work has to be complete by the end of each day, and we can’t be over or understaffed.
  • Some of the tests have a significant repeat rate that “slows us down.”
  • Weekly maintenance happens on our busiest day.
  • There is a need for a supervisor to manage day-to-day tasks.
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.