Free-text consultation notes are the backbone of veterinary clinical documentation, but they were designed for clinical reasoning, not data extraction. When a vet writes "I suspect we may be looking at early-stage otitis," they are communicating diagnostic uncertainty to a colleague. When an automated coding system reads the same sentence, it may miss the diagnosis entirely, misclassify it, or assign a code with a confidence level the clinician never intended. This gap between how vets write and how coding systems read is one of the primary reasons clinical coding in veterinary practice remains unreliable, and why the downstream data that depends on it is so often incomplete.

What clinical coding in veterinary practice actually requires

Clinical coding is the process of converting the key information within a clinical narrative into standardised, structured data using a controlled vocabulary. In veterinary medicine, the most widely used frameworks include VeNom (the Veterinary Nomenclature) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms). The purpose is to transform a consultation note, written in natural language, into discrete, unambiguous concepts: a diagnosis, a species, a body system, a procedure.

Coding systems, whether operated manually or driven by natural language processing (NLP, a branch of artificial intelligence that interprets human language), parse records looking for specific signals. They need to identify a codeable endpoint, a confirmed or suspected condition, and map it to a term within the controlled vocabulary. What they cannot reliably do is infer clinical meaning from narrative context, resolve ambiguous terminology, or distinguish between a current active problem and a historical one mentioned in passing.

As research published in npj Digital Medicine observed, veterinary notes differ substantially in writing style and vocabulary across clinics, and their diagnosis codes use a terminology framework different from human medicine. Unlike human healthcare, where a robust coding infrastructure is embedded in clinical systems, most veterinary records are free text without standard diagnosis coding, and the absence of that infrastructure means the full weight of codability falls on the note itself. For more on how clinical coding in veterinary practice differs from human medicine, see our dedicated overview.

The specific narrative patterns that break automated coding

Several distinct free-text habits consistently cause coding failures or omissions. These are not random errors. They are predictable consequences of writing notes for clinical communication rather than structured data extraction.

Embedded diagnoses in hedging language. When a vet writes "I suspect we may be looking at early-stage otitis," the diagnosis is present but buried in epistemic hedging. Automated tools frequently miss or misclassify diagnoses expressed this way. The coding system cannot reliably determine whether this represents a suspected condition that warrants a code or a speculative remark that does not.

Abbreviations and practice-specific shorthand. Terms like "bilat. ear," "PU/PD query Cushing's," or "?IBD" are immediately legible to a colleague in the same practice but are inconsistently interpreted across coding systems. Research into NLP for veterinary records has found that non-standardised terminology is one of the most persistent barriers to reliable automated extraction, particularly when mapping prescribing data and clinical descriptions to specific conditions at scale.

Differential diagnoses recorded as conclusions. When a vet lists rule-outs, "DDx: hypothyroidism, Cushing's, diabetes," without stating a confirmed working diagnosis, coding tools are left without a codeable endpoint. The differentials may be clinically appropriate, but they do not give the system anything to anchor a code to.

Procedures described narratively. "Took a quick look at the skin and clipped the area" does not surface as a discrete procedure code. Procedural actions embedded in descriptive prose are routinely missed by automated systems that look for recognisable procedure signals rather than narrative accounts of what happened during the consultation.

Species and breed assumptions. When patient signalment, species, breed, age, sex, is not repeated or referenced in the note body, automated tools may apply codes designed for a different species context. If the coding system reads a note in isolation from the patient file, the absence of species context can result in misapplied or ambiguous codes.

Negation and historical context mixed with active findings. "No vomiting, though the owner reports it was an issue last year" is a common and clinically sensible construction, but it can cause false-positive coding of vomiting as a current condition. Early work on free-text search performance in veterinary records established that search sensitivity for diagnoses in unstructured notes ranged from 33 per cent to 98 per cent, and positive predictive value from 2 per cent to 74 per cent, depending on the condition and search strategy used—a range so wide as to render aggregate data unreliable without significant manual review. Negation handling remains one of the most technically difficult problems in clinical NLP.

Why hedging language is particularly problematic for coding accuracy

Clinical uncertainty is not a documentation failure. It is a legitimate and important part of veterinary reasoning. The problem is that the language used to express uncertainty in free-text notes collapses a distinction that coding systems need to preserve.

There is a meaningful difference between:

• A condition that is suspected and should generate a "suspected" or "query" code

• A condition that is being actively ruled out and should not be coded at all

• A condition mentioned only as historical context

In free-text prose, all three can appear in nearly identical constructions. "Query Cushing's," "ruling out Cushing's," and "owner mentioned Cushing's was raised by a previous vet" may look superficially similar to an NLP model, but they have entirely different coding implications. Research into automated coding accuracy has found that accuracy rates can fall as low as 60 per cent in challenging free-text coding tasks, in part because hedging, negation, and contextual ambiguity remain computationally difficult to resolve.

The underlying issue is that vets are trained to document their reasoning process, including uncertainty, in narrative form. Coding systems are designed to extract conclusions. When the note contains reasoning rather than conclusions, the system has limited material to work with.

How omissions are often more damaging than errors

A miscoded record is a problem, but an omitted record is invisible. When a condition or procedure is not captured at all, there is no flag, no anomaly, and no prompt for review. The absence simply becomes part of the data.

This invisibility has real consequences:

• Population health data gaps. Text-mining studies on companion animal enteric syndrome surveillance demonstrated that unstructured records require significant computational effort to extract even basic case counts, and that without systematic coding, case ascertainment for prevalence and incidence studies is fundamentally unreliable.

• Missed recall triggers. In practice management systems, automated alerts for follow-up, vaccination, or repeat dispensing often depend on coded records. Coded records support automated alerts that free-text records cannot reliably trigger.

• Inaccurate practice reporting. Clinical audit in veterinary practice has been shown to be difficult retrospectively due to barriers in establishing diagnosis from clinical records, with the absence of consistent coding identified as a primary obstacle.

• Distorted disease surveillance. National surveillance programmes such as SAVSNET (Small Animal Veterinary Surveillance Network) and VetCompass depend on the quality of data flowing from individual practices. When conditions go uncoded, disease patterns appear less prevalent than they are, a distortion with implications for antimicrobial stewardship reporting and emerging disease monitoring.

A study using gradient boosted models for case ascertainment from free-text veterinary records found that NLP tools could achieve high accuracy for specific, well-defined conditions, but only after extensive data cleaning, custom veterinary spell-checking, and training on manually annotated records. The computational overhead required to compensate for unstructured notes is substantial.

The structural note changes that make records more reliably codeable

The adjustments that improve codability do not require a fundamental change in how vets think or reason. They require small, consistent changes in how clinical conclusions are surfaced in the written record. Most can be implemented without adding meaningful time to a consultation.

State a working diagnosis explicitly. Even under uncertainty, a consistent phrase such as "Working diagnosis: otitis externa" gives the coding system an unambiguous signal. The uncertainty can still be documented in the narrative body, but the codeable endpoint needs to be visible.

Separate active findings from historical context and ruled-out differentials. A simple structural distinction, for example grouping active problems separately from "history" or "background," reduces the risk of historical conditions being coded as current ones. Ruled-out differentials should be labelled as such rather than listed alongside the working diagnosis.

Use consistent terminology for common conditions. Rotating between "ear infection," "otitis," "bilateral ear," and "aural inflammation" across different consultations creates inconsistency that compounds over time in practice-level data. Settling on a preferred term for frequently encountered conditions, even informally, improves both manual and automated coding reliability.

Record procedures as discrete actions. "Cytology performed — ear swab, right ear" is codeable. "Had a look and took a swab while the owner held him" is not. The distinction is not about formality. It is about giving the record a recognisable procedural signal.

Include species-specific context when the note may be read independently. This is particularly relevant for practices using systems where notes may be extracted, shared, or processed outside the patient file, for example in referral summaries or surveillance datasets.

Where structured templates and AI assistants can help, and where they cannot

For a broader overview of AI documentation tools for veterinarians and what to evaluate before adopting them, see our dedicated guide. Structured templates address the codability problem directly by replacing open narrative fields with discrete, required fields: species, presenting complaint, working diagnosis, procedures performed. Research has demonstrated that large language models trained on large volumes of curated, manually coded data can achieve meaningful improvements in automated diagnosis coding, but the quality of the underlying training data and the quality of the incoming notes remain the ceiling for performance.

AI medical assistants that generate structured notes from consultation audio can reduce the burden of manual note-writing and improve consistency, but they face the same ambiguities that affect manual coders. If a vet expresses a diagnosis verbally with hedging language, the assistant will transcribe that hedging. If a procedure is described narratively rather than named, the assistant may not surface it as a discrete codeable action.

Templates, meanwhile, reduce narrative freedom and can feel burdensome in fast-paced clinical environments. Research into veterinary NLP consistently identifies writing style variation across clinics as a major challenge for model generalisation, meaning that even well-trained automated systems may perform differently across practices depending on how consistently templates are used. Neither tool eliminates the problem. Both are constrained by the quality of the note they are working from.

What reliable coding looks like in practice

The following example illustrates the same clinical encounter written in two ways. The clinical content is identical; the codability is not.

Typical free-text version:

"Bella came in with her owner who was worried about her ears. She's been scratching a lot and shaking her head. On exam the right ear looked quite inflamed and there was some dark discharge — could be yeast, could be bacterial, hard to say at this stage. Left ear looked okay. Cleaned out the right ear and took a swab. Started her on Surolan for now and will see how she gets on. Owner to come back in two weeks if not improving."

From this note, an automated coding system faces several challenges: the diagnosis is implied but not stated; the procedure ("cleaned out the right ear and took a swab") is embedded in narrative; the laterality is present but not structured; and the treatment rationale is expressed as uncertainty rather than a clinical decision.

Structurally adjusted version:

"Presenting complaint: head shaking and pruritus, right ear.
Active findings: right otitis externa — erythema, dark ceruminous discharge. Left ear NAD.
Working diagnosis: right otitis externa, likely yeast (Malassezia) — bacterial component not excluded.
Procedures: right ear cytology (swab taken), right ear flush performed.
Treatment: Surolan otic — 5 drops right ear BID x 14 days.
Follow-up: recheck in 2 weeks if no improvement."

In the second version, the working diagnosis is explicit and codeable. The procedure is named and lateralised. The differential is acknowledged without being recorded as a confirmed diagnosis. The treatment is linked to a named product and a discrete action. Every element the coding system needs is present and unambiguous, without any loss of clinical information.

Why this matters beyond the individual consultation

The quality of an individual consultation note has consequences that extend well beyond the encounter itself. At the practice level, unreliable coding means that reporting on caseload, disease prevalence, and treatment outcomes is built on incomplete data. At the national level, disease surveillance programmes that draw on veterinary clinical records, including antimicrobial stewardship monitoring and zoonotic disease tracking, are only as accurate as the records feeding into them.

Epidemiological research using free-text equine records has demonstrated both the potential and the limitations of this data source: text mining can surface meaningful risk factor associations, but the authors note that the approach "requires a conceptual change to disease diagnosis which should be considered carefully." That conceptual change begins with how individual vets write their notes.

The long-term viability of AI-assisted clinical decision support in veterinary medicine also depends on this foundation. Models trained on coded, structured veterinary data can support diagnosis, flag drug interactions, and identify at-risk populations. Models trained on inconsistently written free-text records, without reliable coding, face significant generalisation challenges that limit their clinical utility. Reliable coding depends on reliable records. And reliable records, in veterinary practice, start with the choices vets make about how they write.

Frequently asked questions

▶ Why do free-text veterinary notes cause clinical coding failures?

Free-text consultation notes are written for clinical communication, not data extraction. Automated coding systems need unambiguous, codeable endpoints, but veterinary notes often contain hedging language, practice-specific shorthand, and narrative descriptions of procedures. These habits make it difficult for coding systems to identify a confirmed diagnosis, distinguish active findings from historical ones, or surface discrete procedural codes.

▶ What coding frameworks do veterinary practices use?

The most widely used frameworks in veterinary medicine are VeNom (the Veterinary Nomenclature) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms). Both convert clinical narratives into standardised, structured concepts covering diagnoses, species, body systems, and procedures. Unlike human healthcare, most veterinary records are free text without standard diagnosis coding, which places the full weight of codability on the note itself.

▶ Which specific writing habits most commonly break automated coding?

Several patterns consistently cause coding failures. Diagnoses buried in hedging language, such as "I suspect we may be looking at early-stage otitis," are frequently missed or misclassified. Abbreviations and practice-specific shorthand are inconsistently interpreted across systems. Differential diagnoses listed without a confirmed working diagnosis leave coding tools without an anchor. Procedures described narratively rather than named as discrete actions are routinely omitted. Negation and historical context mixed with active findings can also generate false-positive codes for conditions that are not current.

▶ How does hedging language affect coding accuracy?

Hedging language collapses distinctions that coding systems need to preserve. A suspected condition, a condition being actively ruled out, and a condition mentioned only as historical context can all appear in nearly identical constructions in free-text prose. Research into automated coding accuracy has found that accuracy rates can fall as low as 60 per cent in challenging free-text coding tasks, in part because hedging, negation, and contextual ambiguity remain computationally difficult to resolve.

▶ Why are omissions from clinical coding more damaging than errors?

A miscoded record at least exists in the data and can be flagged for review. An omitted record is invisible. When a condition or procedure isn't captured at all, there's no anomaly and no prompt for correction. This invisibility affects population health data, automated recall triggers, practice-level reporting, and national disease surveillance programmes such as SAVSNET and VetCompass, all of which depend on coded records flowing from individual practices.

▶ What practical changes make veterinary notes more reliably codeable?

Small, consistent changes in how clinical conclusions are surfaced can significantly improve codability. Stating a working diagnosis explicitly, even under uncertainty, gives coding systems an unambiguous signal. Separating active findings from historical context and ruled-out differentials reduces false-positive coding. Using consistent terminology for common conditions, recording procedures as discrete named actions, and including species-specific context when notes may be read independently all help without adding meaningful time to a consultation.

▶ Can AI medical assistants or structured templates solve the coding problem?

Both tools can help, but neither eliminates the problem. Structured templates replace open narrative fields with discrete required fields, which directly improves codability. AI medical assistants that generate structured notes from consultation audio can improve consistency, but they face the same ambiguities as manual coders. If a vet expresses a diagnosis verbally with hedging language, the assistant will transcribe that hedging. The quality of the note remains the ceiling for coding performance, regardless of the tool.

▶ How does unreliable veterinary coding affect disease surveillance?

National surveillance programmes that draw on veterinary clinical records, including antimicrobial stewardship monitoring and zoonotic disease tracking, are only as accurate as the records feeding into them. When conditions go uncoded, disease patterns appear less prevalent than they are. Text-mining studies on companion animal enteric syndrome surveillance have demonstrated that without systematic coding, case ascertainment for prevalence and incidence studies is fundamentally unreliable.

▶ What does a reliably codeable veterinary note look like compared to a typical free-text note?

The clinical content can be identical, but the structure makes the difference. A typical free-text note may imply a diagnosis, embed procedures in narrative, and express treatment rationale as uncertainty. A structurally adjusted note states the working diagnosis explicitly, names and lateralises procedures as discrete actions, labels differentials separately from confirmed findings, and links treatment to a named product and action. Every element the coding system needs is present and unambiguous, without any loss of clinical information.