Applying the Staffing-to-Workload Methodology: Same-Day Testing Staffing Model

By Mike Baisch • June 1, 2017

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In the previous post, we created a staffing-to-workload analysis based on an offsite testing laboratory we have for a remote outpatient care clinic. The laboratory has a total of eight tests it performs; it is open six days a week for eight hours; 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.

Based upon feedback we received from the care providers and their patients, we’ve determined that we need to change some of those criteria. First, we need to complete all of the testing the same day it is collected. This allows the care providers to contact the patients the same day they saw them or first thing the next morning with their results and any next steps in their episodes of care.

Direct Effort

Previous Analysis

Under the original set of assumptions, we created the below analysis to determine the staffing needed for the direct effort. The volume used was based on the daily average across the entire six-day work week.

New Analysis: Completing the Work by the End of the Day

With our new requirement of completing the work by the same day it is collected, we can no longer use the daily average that was based on all six days of the work week. Instead, we now need to perform our calculations based on each day of the week. That means we will need to gather our volume data based on the week day that it was received by the laboratory. The direct-effort table calculation stays the same, but we will now have one for each week day.

What Does It All Mean?

Depending on the day of the week, we need between 0.8 and 3.6 FTEs staffed to complete just the direct effort. With our previous analysis indicating a staffing level of 2.8 FTEs, we now know that only Wednesday would be properly staffed to meet our new requirements. On Tuesday, Thursday, and Friday, we wouldn’t be able to complete our work before leaving, while on Monday and Saturday, we would be overstaffed.

This analysis also indicates the daily volume can vary quite a bit and that variation is reflected in the direct-effort staffing needs.

Finally, the data can tell us is if our test mix changes from day to day.

Balancing Act

Striking the balance between providing the best possible service and care for our clinicians and patients while being good stewards of our expenses (affordable patient care) can be tricky. In this instance, we may develop a plan that includes using the original average direct-effort staffing level of 2.8 FTE, but make the following adjustments:

For Tuesday, Thursday, and Friday "understaff" situations:

• Remove as many indirect effort tasks as possible and reassign them to low-volume days.
• Adjust staff schedules for a combination of 10-, 8-, 6-, and 4-hour days to allow staff to stay longer on high-volume days (still allowing for a full 40-hour work week).
• Have staff work overtime.
• Switch some staff to/with part-time staff to allow for higher staffing levels on busy days by giving a higher headcount on those busy days but maintaining the same FTE levels. A laboratory may have 5 total people (headcount), but with some of them part-time, the full-time equivalent (FTE) would actually be less than 5. Example: 5 staff work in the lab, 2 of them are full-time (40 hours/week) and 3 are half-time (20 hours/week). The headcount is 5, but the FTE is 3.5.
• Work with our clinicians and patient scheduling staff to find opportunities to see patients on a more even schedule.
• Work with our clinicians on test-utilization projects to eliminate unnecessary testing.

For the Monday and Saturday "overstaff" situations:

• Perform the indirect-effort tasks saved up from busier days.
• Adjust staff schedules for a combination of 10-, 8-, 6-, and 4-hour days to allow staff to leave earlier on slow days (still allowing for a full 40-hour work week).
• Send staff home early.
• Switch some staff to/with part-time staff to allow for lower staffing levels on low-volume days.
• Work with our clinicians and patient scheduling staff to find opportunity to see/schedule more patients on these days.

These staffing-to-workload adjustment options are just that—options. Some of them may work in one particular instance but not in another. Understanding the dynamics of each situation is critical to striking the balance of service and cost. A major part of the situational dynamics are the staff members themselves. Too often, a staffing-to-workload analysis is performed with very little to no input from the staff. Thus, when changing schedules, adjusting hours, or working overtime starts being discussed, resistance to those changes will be voiced. A good portion of that resistance will come from a lack of information and knowledge of what is driving the changes. One of the best ways to get that information and knowledge to staff members is to have them help with the staffing analysis. Transparency throughout the process is critical, while restating the needs of patients and care providers helps us to keep our primary objective in view. It should also be noted that defined goals around work-life balance should be outlined before starting any staffing-to-workload analysis.

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.