Struck by a Duck

You are ordering plane tickets. You have written your name, passport number, and date of birth, and now you must select your profession from a drop-down menu. To your horror, the drop-down menu has 14,000 options. You scroll through the endless list of professions: Superyacht Interior Outfitting Coordinator, Competitive Eating Event Logistics Manager, Haunted Attraction Prosthetics and Gore Technician, Medieval Reenactment Armorer, Plate and Chain Specialization, and so on. After half an hour, you finally find your profession: School Teacher. You ordered the tickets, but your request has been denied. The airline company found out that you are a high school teacher, and that school teacher was too vague.

Now, move away from your laptop and go to the hospital.

Every time a patient sees a doctor, someone must document each diagnosis by finding the correct label from a list of more than 14,000 options. In the United States, that list has 72,000 options. These labels are called medical codes. They determine whether the hospital gets paid, whether the insurance claim goes through, and whether the visit gets counted in national health statistics. Find the wrong one, and the consequences range from a rejected bill to a patient’s medical history reflecting the wrong thing.

Many of these codes, at first glance, seem absurdly specific. There is a code for burns due to water skis on fire. There is a code for problems with your in-laws. There is a code for injuries sustained while knitting, and separate codes for tackle football and flag football. In London, lampposts were once padded because so many people were walking into them while looking at their phones - and yes, there is a code for that too. There is even a code for being struck by a duck.

But the specificity is not madness. It exists because the system must account for everything, even the absurd. In this report, we will demonstrate this by analyzing the most frequent codes in each country. An anthropological study told with alphanumeric codes. The codes will tell a story of how people in each nation live: what animals they share space with, what they do on weekends, how they celebrate, and what their domestic life looks like from the emergency room.

With this knowledge, the governments can make policies to prevent injuries and deaths. This is why reporting these sometimes absurdly specific codes is so important. It helps us understand society, so that we can improve it.

Between July 2024 and June 2025, American drivers filed 1.7 million animal collision insurance claims. Of these, 1.1 million involved deer - 65% of all animal collisions, with total economic losses estimated at $10 billion annually. Collisions are 14 times more likely in the two hours after sunset. Peak months are November, October, and December, accounting for 41% of all claims.

Ranked by event frequency and cultural specificity ¹ - focused on codes that reveal something distinctively American.

The entire Y93 activity chapter - hobbies, sports, domestic activities - does not exist outside the U.S. A medical coder, or an AI system, working across borders must understand not just which codes to use, but which entire code chapters exist in each system - and what to do when they don’t.

Ranked by NHS admission volume and cultural specificity ² - the codes that reveal something distinctively British.

The UK uses WHO ICD-10 - shorter codes, broader categories, no activity chapter. The same dog bite that generates W54.0XXA with Y93 activity context in the US produces W54 in the UK with no equivalent detail. That missing information is a design choice - one with consequences for population health research, resource allocation, and any AI system that must navigate both.

Ranked by event frequency and cultural specificity ³ within ICD-10-GM.

Germany’s ICD-10-GM has different structure and clinical conventions from both the US and WHO systems. A hand laceration at Oktoberfest coded in ICD-10-GM follows different documentation rules than the same injury at an American county fair. A human coder switching between systems needs months of retraining. An AI built on a single national dataset simply fails.

Ranked by regional frequency and coding system specificity ⁴.

The WHO base system cannot distinguish a sauna burn from a heatstroke, or a recreational cycling fall from a road traffic collision. These are not errors in the data - they are design constraints. Constraints that a coding AI working across borders must understand and navigate, not simply inherit.

It is worth pausing on what is consistent across all four geographies - and, likely - across every healthcare system on Earth.

Every spring, the same thing happens. The weather improves, public holidays arrive, and millions of people pick up tools they haven’t touched since autumn - with more ambition
than preparation. The codes that follow are entirely predictable.

In England, there are approximately 8,500 hospital admissions per year for DIY and gardening-related injuries, with 56% occurring in the six-month window between April
and September.

The lawnmower is the most common culprit: the age group most affected is 40 to 74 year-olds, who account for 58% of admissions. Ninety percent of all DIY and gardening injury admissions are male. In the United States, lawn and garden equipment injuries generate an estimated 3.2 million emergency attendances over a decade - approximately 320,000 per year - with lawnmowers alone responsible for around 26,679 hospitalizations annually.

The codes themselves - W27 (contact with non-powered hand tool), W28 (contact with powered lawnmower), W29 (contact with other powered hand tools and household machinery) - exist in all national ICD-10 variants. They are among the most seasonally predictable entries in the entire system. In at least two of the four countries covered by
this report, the coding system has already anticipated Easter weekend. The others almost certainly follow the same pattern - but have yet to publish the data to prove it.

In NHS England, an average of 1,218 nasal foreign body removals and 2,479 aural foreign body removals are performed on children every year, at a total annual cost of approximately 
£2.9 million.

Children aged 1–4 favour their noses; those aged 5–9 prefer their ears. Common objects include jewellery, pencils, pieces of plastic toy, and - this being Britain - cotton buds.

The figure has not declined over any measured period. The title of the peer-reviewed study that established this, published in the Annals of the Royal College of Surgeons of England, is - without editorial embellishment - Will Children Ever Learn?

In the United States, ear foreign bodies alone generated an estimated 446,819 emergency department visits over a single decade. The mean patient age was 7.2 years. The codes T16 and T17 exist in all national ICD-10 variants.

Problems with in-laws (Z63.1). Billable in the US, the UK, Germany, Denmark, and approximately 100 other countries. Classified under ’problems related to primary support group.’ Present in every major ICD-10 revision. No country has removed it. Several have had the opportunity.

Step away from the duck! Step back from the beer stein lacerations and the lamppost collisions and the Danish cyclists with no helmets and no blood alcohol limit. Look at the full picture.

Four countries. Four healthcare systems. Four versions of ICD-10 that share a common ancestor and diverge in ways that are not random - they are geographically, economically, and socially specific. America’s deer problem is written in its rural highway data. Britain’s class geography is visible in its alcohol admission rates. Germany’s festival calendar shows up in hand laceration spikes. Denmark’s cycling culture looks, to a coding system trained elsewhere, like a road accident epidemic.

And underneath all of it: a system that processes billions of clinical encounters every year - determining where healthcare money flows, where public health resources go, and which patients get flagged for follow-up - operating at an error rate that costs the industry $36 billion annually in the US alone, and that, in at least one documented case, missed three in every four suicide attempts in an entire national dataset.

The codes are not the problem. The codes are, in their strange and hyper-specific way, trying to tell the truth. The problem is reading them accurately - across 72,000 variations, across systems that describe the same event in fundamentally different ways, across clinical notes written in shorthand by exhausted clinicians - at a scale and consistency that neither human coders working under time pressure nor AI systems trained on single-country data have been able to reliably achieve.