Your Fertility Specialists Are Spending Too Much Time Typing

In most fertility clinics, the best-paid and hardest-to-replace people spend hours a day on documentation. Not on patients. On typing. For an owner or a medical director, that is not a small annoyance. It is a workforce problem hiding in plain sight.

A single IVF cycle creates a large amount of records. Ten to fifteen monitoring visits. Lab reviews. Procedures. Medication changes. Counselling. A specialist runs many cycles at once, each on its own schedule. The notes pile up. And because each entry depends on the ones before it, falling behind is not just untidy. It is risky.

This is where AI is starting to help. Not by replacing anyone. By taking the typing off the people who should not be doing it.

Think about where a specialist's day actually goes. A consultation runs for twenty minutes, and much of that is spent looking at a screen, typing while the patient talks. Then there is the catch-up after clinic — the notes that did not get finished, written from memory in the evening. That evening work is where accuracy slips and where good people start to burn out. It is also invisible on any schedule, which is why it goes unmanaged for years.

What an AI scribe actually does

An AI Scribe listen to the consultation in the background. It turns the conversation into a structured clinical note and fills in the right fields in the record. The doctor reviews it, edits anything that needs changing, and signs off. No typing during the visit. No dictation afterwards. The review usually takes under a minute, because the doctor is checking and approving rather than writing from scratch. And because the tool is built for fertility, it captures the full picture a cycle depends on — history, stimulation details, lab findings, the plan — not just a short note of symptoms.

The point is not speed for its own sake. A 2025 JAMA Network Open study found that documentation load is a leading cause of physician burnout. In a field already short of specialists, losing one to burnout is expensive and slow to put right. So a tool that gives clinicians their time back is not really about productivity. It is about keeping your team. Why fertility teams are moving to AI documentation is, underneath, a staffing story.

Why a general AI tool is not enough

Here is the part that is easy to get wrong. A general medical AI, trained on hospital and primary-care language, struggles with fertility. It mishears terms like antral follicle count, blastocyst grading, or ICSI. When it gets the words wrong, someone has to fix them. So it creates work instead of saving it.

A tool built for IVF knows the vocabulary. More important, it puts the information straight into the right fields in the system the team already uses. A note that lands in a separate app, waiting to be copied across, has only solved half the problem.

How to judge one

A useful test when a vendor demos a scribe: ask it to document a real fertility consultation, with the real vocabulary, and watch what lands in the record. Does the note use the right terms? Do the values go into the correct fields, or into a block of free text that someone has to sort out later? Does it work in the languages your patients actually speak? A scribe that passes those three checks will save time. One that does not will quietly add to the workload it promised to remove.

The quiet benefit: better data and better attention

There is a bigger payoff that clinics rarely buy for. When every doctor writes notes their own way, the records cannot be compared across people or sites. That makes coordinated care harder and outcome analysis almost impossible. When the documentation is structured the same way every time, a pile of notes becomes data you can actually use. That matters more as you grow. The moment you have more than one location, inconsistent notes make it almost impossible to compare performance fairly or to spot a problem early. Structured documentation is what lets a network see itself clearly.

There is a human payoff too. When a doctor is typing, they are not fully with the patient. What changes when the screen is out of the way is simple: the appointment becomes a conversation again. In IVF, where the consultation carries a lot of emotional weight, that attention matters - for trust, and for whether a patient stays with your clinic.

So the real gain from AI is not faster typing. It is better, more consistent information, and clinicians who are more present in the room.

Adopt it carefully, not blindly

Because these tools work, the important questions are about control, not capability. A scribe should listen and document. It should not diagnose, prescribe, or make clinical decisions. Someone has to review and sign each note before it becomes part of the permanent record. That step is where accountability sits, and skipping it simply automates risk. You also need to handle patient consent to being recorded, and know where the data is processed. None of this is a reason to wait. It is the difference between adopting well and adopting carelessly.

What this means for you

You do not need to predict the future of AI to act on this. Your clinicians are spending too much of their day on a keyboard. That costs you time, money, and, over the long run, people. A fertility-specific scribe, used with proper review, gives that time back and leaves you with cleaner data and better consultations.

Start there. Not because AI is the future, but because the documentation problem is already sitting on your desk today. The clinic across town is not waiting for a trend report to fix it, and neither should you.

Prashant Talesara - A co-founder with 8+ years of experience in building solutions on fertility informatics, reproductive data governance & the realities of running Ivf at scale.