What’s In Your Algorithm? The Quiet Biases in Laboratory Standardization

I plugged in the numbers—height, weight, sex—and the algorithm spit out a total blood volume of 8 liters.

Eight liters. That’s more than the average adult elephant. Okay, not really—but it was definitely more than was physiologically plausible for the patient in front of me.

On paper, the formula worked. In practice, it made no sense.

And that’s the quiet danger of laboratory standardization: the illusion of precision without the reality of accuracy. Algorithms, equations, and scoring systems are everywhere in lab medicine. We use them to estimate total blood volume, calculate corrected count increments, determine transfusion thresholds, risk-stratify patients, and more. They are essential. They are powerful. And they are, too often, blindly applied.

Because we’ve come to equate “standardized” with “valid,” even when the standard was built on shaky ground.

The Allure—and Illusion—of the Standard

Standardization is the bedrock of laboratory medicine. It’s what lets us compare results across institutions, apply clinical guidelines, and run multicenter trials. Without it, evidence-based medicine would fall apart.

But standardization is only as good as the data and assumptions it rests on. And in lab medicine, those assumptions are rarely neutral.

When you dig into where these formulas come from—whether it’s Nadler’s formula for TBV, the use of sex-specific reference ranges, or scoring systems for conditions like heparin-induced thrombocytopenia or DIC—you often find something surprising: a very narrow foundation. Many of these tools were derived from datasets that are small, homogeneous, and unrepresentative of the patients we see today.

And once a tool is canonized—once it makes it into the LIS, into the protocol, into the reference manual—it becomes difficult to question. But we should.

Where Bias Hides in Plain Sight

Bias in lab medicine isn’t always dramatic. Sometimes, it’s a small nudge—enough to make a test look slightly more normal than it should, or an algorithm overshoot the mark. But when scaled across thousands of patients, those nudges matter.

🧮 Biased Formulas

Take total blood volume. Nadler’s formula is widely used and appears straightforward: plug in height, weight, and sex, and out comes a number. But Nadler’s equation was derived from a limited sample of healthy individuals in the 1960s—primarily white, young, and lean. Apply that formula to a patient with obesity or fluid retention, and you may end up drastically misestimating their TBV. That misestimation can lead to inappropriate collection during apheresis procedures.

📉 Reference Intervals

We treat reference intervals as facts, but they’re often closer to educated guesses—highly dependent on the population used to derive them. Hemoglobin and creatinine reference ranges, for instance, are typically stratified by sex assigned at birth, but this binary stratification fails to reflect the diversity of physiologic reality. Patients on hormone therapy, those with chronic conditions, or those with differing muscle mass may fall outside “normal” ranges while being perfectly healthy—or may be misclassified because the ranges were never built with them in mind.

Even more problematic, many intervals were never validated in pediatric, geriatric, or racially diverse populations. And yet these ranges shape everything: decisions to transfuse, to screen further, to diagnose. The reference range becomes the gatekeeper to clinical action—even when it shouldn't be.

📊 Risk Scores

Scoring systems like the 4Ts for HIT or the ISTH DIC score assume access to certain labs, follow certain clinical patterns, and reward conformity. They can underperform in patients with atypical presentations or resource-limited settings.

🤖 Machine Learning and AI

Even newer tools aren’t immune. Predictive models built from EHR data can reflect and amplify existing disparities—especially if the training data skews toward certain populations or omits key variables like socioeconomic status, language access, or prior healthcare usage. Bias doesn’t just live in the past—it gets encoded into the future.

The Cost of “Close Enough”

We love numbers in the lab. But “close enough” doesn’t cut it when you’re estimating blood volume for a patient with reduced muscle volume, or when a scoring tool steers you away from a diagnosis you should be considering. Small inaccuracies in algorithms can lead to real-world consequences: missed diagnoses, under-treatment, over-transfusion, delayed care.

And the worst part? These errors often go unrecognized—because the numbers looked clean, the boxes were checked, the equation was “standard.”

Toward Better, Fairer Standardization

So what do we do?

We start by asking better questions:

• Who was this algorithm validated on?

• What assumptions does it make?

• Where does it fail?

• How does it perform in patients who don’t look like the “standard”?

  
  

We need laboratory professionals involved not just in implementing tools, but in designing and validating them. We need to stop treating standardization as a destination and start treating it as a continuous process—one that requires transparency, adaptability, and humility.

And most of all, we need to resist the seduction of certainty. Algorithms can guide us, but they can’t replace judgment.

The Patient Didn't Have 8 Liters

That patient didn’t have 8 liters of blood. But the algorithm said they did—and if we hadn’t caught it, we might have used that number to justify unsafe collection volumes.

That’s the danger: when standardized tools are treated as facts, patients bear the consequences.

Because in the end, the algorithm was wrong.

And we knew better.