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Sahana C: In Fertility, the Messiness of Clinical Analytics Is Real
Photo: DepositPhotos
Aug 31, 2026, 16:51

Sahana C: In Fertility, the Messiness of Clinical Analytics Is Real

Sahana C, Former Data Analyst of Biostatistics at Fakih IVF Fertility Center, shared on LinkedIn:

“Something people outside healthcare don’t expect: a lot of the hard work in clinical analytics isn’t the analysis itself.

It is getting the data into a state where the analysis is actually possible.

In fertility, especially, the messiness is real.

Missing values. A field left blank because someone was rushing or simply forgot. Typos that can completely change the meaning, like a BMI of 32.5 becoming 325.

And then there’s unit confusion, which can quietly break your numbers.

AMH is a good example. It can be reported in ng/mL in some settings and pmol/L in others, with the values differing by a factor of about 7.

So the same patient’s result can look completely different depending on which unit was recorded.

If you don’t catch that early, your analysis can be wrong before you’ve even started.

Then there are medications and dosages, which are a whole adventure of their own when you’re trying to reconcile records.

So before you can answer the interesting questions, there’s often a lot of work happening behind the scenes:

  • Cleaning.
  • Standardising.
  • Reconciling.
  • Validating.

Until all those scattered pieces finally become one coherent picture of the patient.

It’s not glamorous.

Nobody puts it in the brochure.

But honestly, that’s where a surprising amount of the value lives.

And I’ll admit, I’ve started to enjoy this part.

There’s something oddly satisfying about taking a tangled mess of data and turning it into something clean enough that the real questions finally become answerable.

Anyone else in data know this feeling?

Where the ‘boring’ cleanup turns out to be the whole game?”

Other articles featuring Sahana C on Fertility News.

AMH anti-Müllerian hormone Biostatistics Clinical Data Data Quality Data Standardization Data Validation Fertility fertility data Fertility News Healthcare Analytics Healthcare Data HealthTech in vitro fertilization infertility IVF Medical Statistics OBGYN reproductive health Reproductive Medicine Sahana C Women’s Health
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