When You Own the Numbers, You Own the Message

The first time administration tried to cut my staff, I didn’t even know it was happening.

I had just met with finance and gotten my budget for the new fiscal year. Looked at the FTE line and thought, “Whew. That’s plenty.” The number was right there. Let’s say it showed 60 FTEs, and we were running 56 at the time. Comfortable. No alarms.

Then a few months into the year, I got an email I wasn’t expecting: I needed to join a meeting about not meeting my FTE budget. I pulled up our finance dashboard and saw we were running four FTEs over. Four. I was confused. How could I be four over when the budget said I had 60?

Here’s what I didn’t understand yet: the budget had me at 80,000 tests per month, but we were actually running about 62,000. Because our lab is a variable cost center tied to a flex budget, those inflated volumes meant inflated FTEs on paper. When actual volumes came in lower, the flex budgeted FTEs adjusted down automatically. Suddenly I was “over budget” without ever having added a single person.

That was my oh shit moment. I had been looking at the wrong number.

“If you don’t understand your own budget, someone else will make decisions about your staff for you.”

And this isn’t just about headcount. When staffing decisions are made based on bad math, the consequences show up in turnaround time, in quality, in patient safety. A budget that quietly removes five FTEs doesn’t just mean five fewer people on paper. It means longer TATs, overworked techs, and departments that can’t cover critical benches. That’s the real cost.

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The Number That Actually Matters

If your lab operates on a flex budget tied to test volume (and most hospital labs do), then the raw FTE number on your budget is likely misleading. It moves every month based on volume. The number you need to know, defend, and own is your expected productivity rate: worked hours per billable test.

The formula is simple:

Worked Hours ÷ Test Volume = Productivity Rate

Think of it this way: productivity rate is the number of hours it takes your lab to perform a single test. If the C-suite lowers your productivity rate, they’re telling you to do more testing with fewer people. That’s the direct translation. A lower rate means fewer worked hours budgeted for the same volume, which means fewer staff. It sounds like a small decimal change, but it hits like a truck.

“A lower rate means fewer worked hours budgeted for the same volume, which means fewer staff. It sounds like a small decimal change, but it hits like a truck.”

Between two budget cycles, my productivity rate was lowered from 0.12 to 0.11. That doesn’t sound like much. But across a year, it averaged out to five fewer FTEs for the same test volume. Five people, gone on paper, without a single conversation about workload.

One more thing that makes this tricky: not all tests are created equal, and not all staff contribute to the volume number. Your phlebotomists are clocked in and working, but they don’t perform billable tests, so they raise your productivity rate without adding to volume. Blood bank might need three techs to process 100 tests in a shift because of the complexity of crossmatches and antibody workups, while chemistry is reporting 1,000 tests with the same number of techs on automated analyzers. The productivity rate treats all of that as one number. When you’re explaining your staffing to administration, you need them to understand that one test is not one test.

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The Budget Trap: Why the FTE Line Lies

One budget can look generous on paper while actually cutting your staffing, if the volume assumptions are inflated. Here’s an example.

Say you receive two budget proposals for your department. Last year you performed 37,000 tests per month and currently run 24 FTEs. Which budget looks better?

	Budget 1	Budget 2
Budgeted Volume	48,000 tests/mo	37,000 tests/mo
Budgeted FTEs	27.06 FTEs	24.15 FTEs
Productivity Rate	0.100 hrs/test	0.1157 hrs/test

Budget 1 looks more attractive at first glance. 27 FTEs! But look at the budgeted volume: 48,000 tests. You ran 37,000 last year. What happens when actual volume comes in at 37,000 again?

	Budget 1	Budget 2
Budgeted Volume	48,000 tests/mo	37,000 tests/mo
Budgeted FTEs	27.06 FTEs	24.15 FTEs
Productivity Rate	0.100 hrs/test	0.1157 hrs/test
Actual Flex FTEs (at 37,000 tests)	~20.9 FTEs	~24.2 FTEs

Budget 1’s flex FTEs drop to about 21. That’s three fewer than you’re running today, buried inside a budget that looked like it gave you more. Budget 2 has a realistic volume projection and a higher productivity rate. At 37,000 actual tests, you flex to about 24 FTEs — right where you are now. That’s the budget that actually protects your staffing.

The lesson: if your lab FTEs are tied to test volume, check the productivity rate first. Not the FTE number. The FTE number is a function of volume times rate. If the volume is wrong, the FTE number is wrong.

And that’s how the budgeting cycle works. Finance looks at what you ran last year. If last year included an abnormal spike, they’ll assume you can sustain that rate. “Well, you did it at this productivity before, so we’ll keep you there.” But last year was abnormal, and you can’t hold that same rate at normal volumes. If you don’t catch that assumption and challenge it early, it becomes your budget. And then it becomes your problem.

I was able to talk my way through that first year. But I knew I’d gotten lucky. That experience is why I tell every new lab manager: learn your budget before budget season, not during it.

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The Second Time: Coming With Receipts

The next year, they lowered my FTEs in a different way. This time the story was that our FTEs had been “redistributed” to another department. A C-suite executive had made the call at the last minute. Three FTEs, gone.

But this time I was ready. I had spent the entire previous year going deep into finance, learning how flex budgets actually work, where the assumptions come from, and where they break down. When I got that budget, I could see the problem immediately.

Here’s what I did:

I calculated a specific productivity rate needed for our lab, not FTEs. I knew from our historical data that we’d only met the proposed target rate during COVID surges, which we’d seen exactly twice in five years. At our actual average volume, the proposed rate would put us significantly short.

I pulled five years of historical data. I calculated what the proposed rate would have looked like applied to the previous five fiscal years. The result: we’d have been over budget almost every single month, except during COVID. That’s not a staffing problem. That’s an unrealistic budget.

I separated productivity by job role and department. This helped aid the conversation, allowing the executive team and finance to visualize how the lab operates and why all areas are not equal when it comes to impact on productivity rate.

I built a core staffing floor. This was the backbone of my argument. I mapped out exactly what minimum staffing looks like for each shift: how many techs on days, how many on nights, covering chemistry, hematology, blood bank, microbiology, coag, and urinalysis. I showed them the bench assignments, who’s doing what, where there’s no overlap. If you pull one person, someone is now covering two departments alone. On weekends, we were already running bare minimum. There’s no fat to cut.

“When you can show administration a visual of your staffing model at a detailed level and say ‘tell me which bench you want to leave empty,’ the conversation changes fast.”

I neutralized every alternative they could propose. I knew they would try to find somewhere to cut, so I went through every area before they could ask:

Phlebotomy? I showed hourly draw volumes by shift. Day shift phlebotomists were averaging 50 patients per shift, with the morning run hitting 6 to 8 patients per hour per person. Plus phlebotomists don’t just draw. They review pending lists, cancel duplicate testing, relieve processors for breaks. I told them: I would not recommend increasing this workload.

Blood bank? They already knew that they could not cut the blood bank. Not an option.

Quality staff? I listed every non-productive function: CAP surveys, QC review, correlation studies, competency assessments, proficiency testing. All of it regulatory. You can’t cut it without creating compliance risk for our hospital.

I benchmarked favorably and fairly. I compared our lab’s productivity against other labs in the system. But I controlled for the fact that we’re a phlebotomy-collect facility. Our worked hours include phlebotomists who don’t perform billable tests, which inflates our rate compared to nurse-collect facilities. When I removed phlebotomy hours from the comparison, we were more productive than every nurse-collect lab in the network.

If you don’t have internal system data to compare against, look into external benchmarking. CAP (College of American Pathologists) has published Q-Probes studies on laboratory staffing benchmarks that compare productivity across institutions. It’s not perfect, but having national benchmarking data in your back pocket gives you another layer of credibility when administration starts questioning your numbers. There are other sources to benchmark too, like Vizient for example, but take these with a grain of salt as all labs are not equal.

We weren’t overstaffed. We were already one of the most efficient labs around, and they were trying to cut us further.

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The Meeting

I presented all of this to the executive team and finance. I walked them through the data, the historical trends, the core staffing model, the phlebotomy workload, the benchmarking.

Here’s something important to understand: finance isn’t trying to sabotage your lab. They’re trying to standardize, compare, and control labor cost across the system. That’s their job. But their tools are blunt. They see FTEs and productivity rates. They don’t see bench coverage or test complexity or the difference between a glucose and a crossmatch. That’s exactly why you need to translate your staffing model into language they understand and numbers they can defend to their bosses.

I told them: if you insist on budgeting minus three FTEs, that’s your call. But you need to know that we will not be able to meet our turnaround time targets. Will the ED and OR be OK with that? Because we cannot do the same work with fewer people.

By the end of the meeting, the executive team was impressed. Not because I yelled or threatened, but because I knew my numbers better than the finance team did. I could answer every question they threw at me. When they said “what about phlebotomy?” I had the data. When they asked about off-shifts, I had the schedules. When they proposed a lower productivity rate, I could show them exactly which months it would have failed over the last five years.

Thankfully after all that discussion, senior leadership backed me up. The cuts didn’t happen.

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The Playbook: What You Need Before Budget Season

If there’s one thing I want you to take from this, it’s this: by the time you get your budget, it’s already too late to start learning the numbers. You need to know them before the conversation starts.

Know your productivity rate and what it means. Not just the number. Understand the formula, understand how it connects to FTEs, understand that when someone lowers your rate by 0.01, that translates to real humans. Use the interactive calculators with this article to play with the numbers until it’s intuitive.

Know your volumes. Real volumes, not budgeted. Pull your actual test volumes for the past 3 to 5 years. Know the average. Know the trend. If the budget projection doesn’t match your historical reality, that’s not a forecast — it’s a fantasy. Flag it before it gets locked in.

Build your core staffing model. Map every shift, every bench assignment, every department. Know the minimum number of people needed to safely operate. This is your floor. This is the line you can’t cross without removing testing or extending turnaround times.

Anticipate their moves. Think about every area they might try to cut. Then build the case for why they can’t, before they ask. Have your workload data, quality function hours, and regulatory requirements ready.

Benchmark, but benchmark fairly. If you can compare against other labs in your health system, do it. But control for the variables that matter. Don’t let an apples-to-oranges comparison be the reason you lose staff.

Say it early and say it clearly. Don’t wait for the budget to land in your inbox. In the pre-budget discussions, tell them exactly what you need and why. If they cut you anyway, they can’t say they weren’t warned.

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Your Job Is to Communicate the Needs of the Lab

At the end of the day, this is what the job comes down to. As a lab director or manager, you are the person who represents the laboratory to the rest of the hospital. Most executives don’t understand how lab productivity actually works. They don’t know what happens when you pull a tech off the hematology bench. That’s not a criticism of them. It’s just not their world. It’s yours.

If you can’t explain the value of lab services, if you can’t translate FTEs and productivity rates and quality functions into language that a CFO or COO understands, then you will lose control of your budget. And their version won’t include patient safety, turnaround time, or the fact that your blood bank tech is already covering micro on weekends.

I’ve seen directors accept whatever budget comes down, not because they don’t care, but because nobody taught them how to read it. They don’t push back because they don’t feel confident in the numbers, or they think they don’t have any control, or they just don’t know where to start. I get it. I was there. The first time this happened to me, I was scrambling. But here’s what I’ve learned: when you own the numbers, you own the message. When the finance team throws a question at you and you can answer it with data — not “I think” or “I feel” but actual historical evidence — the conversation changes. You’re not asking them for staff. You’re showing them why the staff is necessary.

“When you own the numbers, you own the message.”

So learn your budget. Learn the formulas. Pull the data. Build the case. And don’t be afraid to put your job on the line for your team. Because if you won’t, who will?