A history may sit in one document, laboratory results in another, imaging notes somewhere else, and previous clinical reasoning in the medical record or a colleague’s notes. The challenge is not always missing information. Sometimes the relevant detail is already there—it is simply surrounded by many other pieces of the case.

This is where AI can be useful, provided its role is clearly defined.

Responsible veterinary AI should support clinical work, not imitate independent clinical authority. That distinction matters most when an output looks fluent, confident, and easy to act on.

Where AI can genuinely support a veterinary workflow

The most practical uses of AI are often not the most dramatic ones. They are tasks that make clinical information easier to work with without pretending to resolve the case.

Organizing case context

A veterinary case rarely begins as a clean, structured dataset. It may include signalment, an owner’s account, previous notes, laboratory results from different dates, medications, images, and a long PDF received shortly before an appointment.

An AI assistant can help bring those materials into a usable working view: identify dates, organize a timeline, summarize a document, list findings mentioned in the available records, or highlight information that appears to be missing from the context provided.

That does not make the system the author of the clinical assessment. It makes the underlying material easier for a veterinarian to inspect.

The value is not that “the AI knows the answer.” The value is having a more organized view of the available information before deciding what it means for this patient.

Supporting laboratory review

A laboratory value should not be considered independently of the patient and the circumstances in which it was obtained. Depending on the case, clinicians may need to consider the laboratory’s reference interval, species, age, physiological state, methodology, treatment, sample quality, other analytes, and changes over time.

A useful role for AI is therefore not to issue a verdict based on an isolated abnormality, but to help organize the review. It may assist with comparing results from different dates, presenting trends, connecting laboratory data with the history provided, or generating questions for further assessment.

The resulting analysis still requires veterinary review. The clinician working with the patient determines the significance of the findings in the context of the clinical picture and decides what happens next.

Supporting documentation

Documentation is essential for continuity of care and communication between veterinary professionals. AI can assist by turning rough notes into a more structured draft, organizing information into sections, or preparing a concise case summary for review.

The key word is draft.

AI-generated documentation should be checked for dates, doses, units, drug names, omitted information, and language that may state a clinical conclusion more strongly than the underlying information supports.

Used this way, AI helps prepare the record. The veterinary professional verifies its medically relevant content.

The hard lines: what should not be delegated

Responsible use becomes easier when some boundaries are explicit.

Final diagnosis

A diagnosis is based on the totality of information available about an individual patient. A digital tool may have access to only part of that information, and the reliability of its inputs can vary.

AI may help organize differential hypotheses, identify missing information, or generate questions for further consideration. An automatically generated hypothesis, however, does not become a diagnosis without professional veterinary assessment.

Prescribing and dosing decisions

Errors in dose, concentration, route, or frequency can have serious consequences. Automatically generated content should not be used as an independent basis for prescribing a medication.

Therapeutic information should be verified by a veterinarian using appropriate, current professional references and assessed in the context of the individual patient.

AI may assist with finding or organizing information. It does not replace professional verification of a prescription or treatment decision.

Triage and emergency judgment

A text description or uploaded document may not contain information that becomes apparent during direct clinical assessment.

AI therefore should not serve as the sole basis for determining urgency or deciding whether emergency care is required. Those decisions belong to veterinary professionals using clinical assessment and the clinic’s established protocols.

Professional accountability

AI can produce language that sounds authoritative. It does not carry professional accountability for the resulting clinical decision.

The veterinarian reviews the source information, assesses the output, makes the decision, and determines the plan for the patient. A useful AI system should make that process easier to inspect rather than making responsibility less clear.

A practical workflow for AI-assisted work

Veterinary teams can make AI use safer and more consistent by defining in advance what the system is used for and which outputs require verification.

A practical workflow can include:

1. Define the task first. Do you need a document summary, a clinical timeline, comparison of laboratory results, questions for further review, or a documentation draft?
2. Provide relevant context. AI can only work with information available to it. Teams should also follow applicable requirements and clinic policies for handling patient and client data.
3. Review content, not just presentation. Fluent language is not evidence of accuracy. Check numbers, units, dates, medication information, and categorical clinical statements carefully.
4. Separate extracted information from inference. Know what came directly from the source material and what represents summarization, interpretation, or a generated hypothesis.
5. Preserve uncertainty. When the available information does not support a conclusion, the appropriate output is to identify the gap rather than turn an assumption into certainty.

How Terav approaches the role of AI

Terav is being built as an AI assistant for veterinary professionals working with clinical cases. It helps bring patient context together, supports work with laboratory results, and allows clinicians to work with clinical documents in PDF and image formats.

The goal is not to transfer the clinical decision to the system. Terav is intended to make available information more structured and easier for a veterinary professional to review.

Its outputs should therefore be treated as part of the clinician’s workflow rather than as final clinical conclusions. Verification of source data, assessment of the patient, and decisions about further diagnostics and treatment remain with the veterinary professional.

This is a deliberate product boundary: AI helps clinicians work with clinical context; the veterinarian determines what that context means for the individual patient.

Responsible AI is a workflow standard

The most important question for a veterinary team is not whether AI can produce a convincing answer. Modern systems can do that remarkably well.

The more useful question is whether the tool improves the path from fragmented information to careful professional review—and whether it remains clear where system assistance ends and clinical decision-making begins.

In a responsible workflow, that division remains visible. AI can help collect, organize, compare, and present information. Veterinary professionals assess its significance, make decisions, prescribe, communicate with clients, and remain professionally accountable.

It may be a less dramatic vision of clinical AI. But it is a far more useful one.

The post What Should AI Actually Do in Veterinary Practice? appeared first on Terav.

The clinical challenge is to turn that information into a defensible next decision.

Before that reasoning can happen, however, clinicians may also need to retrieve a previous result, reconstruct a timeline from several notes, compare values across documents, or remember why a differential was deprioritized earlier in the case.

Research in human healthcare suggests that the way clinical information is organized and presented can contribute to clinicians’ cognitive demands. Workflow fragmentation, interruptions, task switching, manual data work, and difficult-to-navigate electronic records have all been studied in this context. [1–5]

Most of this evidence comes from human rather than veterinary medicine, so it should not be treated as direct evidence of the same effects in veterinary practice. It does, however, provide a useful human-factors framework for asking how veterinary clinical systems should be designed.

Cognitive load is partly a property of the work environment

Clinical reasoning necessarily requires cognitive effort.

An ambiguous patient should require thought. Comparing competing explanations, evaluating uncertain evidence, and deciding whether new information changes the plan are not forms of workload that an interface should attempt to eliminate.

Other demands are different.

Searching for information that already exists, reconstructing chronology, repeatedly moving between documents, or re-entering the same case details do not themselves resolve the clinical question.

Studies of electronic health record use in human medicine have identified substantial cognitive demands associated with maintaining an overall picture of the patient and reasoning across distributed clinical information. [4,5]

A 2026 study of 564 physicians across 32 specialties also found that both EHR data usability and system usability were associated with physicians’ cognitive load, reinforcing the idea that information design matters alongside the inherent complexity of clinical work. [1]

This does not establish that a particular interface improves clinical outcomes. It does suggest that clinical information systems can add to—or help manage—the cognitive demands surrounding the clinical task.

Context switching is not always harmful

Veterinary practice is inherently interruptible.

A clinician may move from a consultation to laboratory interpretation, then to an urgent patient, a client call, a team question, documentation, and finally back to the original case.

Some of these interruptions are necessary. Healthcare research specifically cautions against treating every interruption as harmful: interruptions may communicate urgent information and support coordination of care. [2,3]

The more useful question is what happens when the clinician returns to the interrupted task.

Experimental and healthcare literature shows that the effects of interruptions depend on factors including working-memory demands, task type, interruption timing, and similarity between tasks. [2,3]

Clinical workflow studies also describe workflow fragmentation in terms of task switching and interruptions. In studies of electronic health record use, clinicians reported additional cognitive effort when re-entering interrupted work, navigating distributed information, and reconstructing the patient narrative. [4]

Again, these findings are primarily from human healthcare. Direct veterinary evidence on context switching and cognitive load remains limited.

For that reason, it is more accurate to describe fragmented veterinary workflows as a plausible human-factors concern than to claim that context switching has been shown to impair veterinary clinical reasoning.

The challenge becomes clearer in longitudinal cases

Laboratory interpretation is rarely a matter of deciding whether an isolated number is high or low.

Its relevance may depend on species, age, breed, medication exposure, presenting signs, examination findings, sample conditions, concurrent abnormalities, previous results, and change over time.

When those inputs are stored in different places, the clinician’s task includes both interpreting the case and reassembling the information required to interpret it.

That distinction matters.

One source of complexity belongs to the patient.

Another belongs to the way information about the patient is stored and retrieved.

The second is a legitimate target for workflow design.

Design around the clinical question

Clinical data are often organized by technical format.

Laboratory results live in one interface. Referral reports arrive as PDFs. Images sit elsewhere. Client messages may be in another system. Previous reasoning may be embedded deep inside a progress note.

Clinical reasoning is organized differently.

The veterinarian wants to know:

• What is happening with this patient now?
• Which findings meaningfully change the assessment?
• What has changed since the previous encounter?
• Which data support or weaken the current differentials?
• What information is still missing before the next decision?

This suggests a useful design principle: organize the working view not only around document types, but around the patient, the timeline, and the active clinical question.

Primary source material should remain available. A summary is useful only if the clinician can return to the original laboratory report, note, or image and verify what it says.

It is therefore helpful to distinguish two activities:

• assembling and checking available facts — for example, aligning laboratory results with dates, treatment changes, and clinical observations;
• making a clinical decision — deciding on further diagnostics, monitoring, treatment, referral, or reassessment.

Technology can assist with the first and provide tools that support the second.

The decision itself remains a professional responsibility.

Practical ways to reduce avoidable cognitive friction

1. Start with a clinical question.

“Review the laboratory results” is broad. A more useful starting point is to identify what needs to be understood now: whether the trend matches clinical improvement, which explanations remain plausible, whether a test should be repeated, or which missing information could change the interpretation.

2. Preserve chronology, not just attachments.

A result, medication change, change in symptoms, and follow-up test become more informative when they can be reviewed as a sequence.

3. Separate data from interpretation.

A primary document, a clinician observation, an automated summary, and a working hypothesis are different kinds of information. Keeping those layers distinguishable makes it easier to verify and revise reasoning when new information arrives.

4. Make handoffs problem-oriented.

The next clinician may not need every document at once. They usually need the active problem, material findings, relevant timeline, unresolved questions, and next planned step.

Problem-oriented and longitudinal patient summaries have been explored in medical informatics specifically as a way of helping physicians review complex electronic records. [6]

5. Define reassessment points.

In complicated cases, it is useful to specify which new finding would materially change the working interpretation or plan. This keeps data collection tied to a clinical question rather than allowing it to become an end in itself.

Cognitive load and burnout are not the same claim

Burnout is a significant issue in veterinary medicine.

Veterinary research has associated burnout symptoms with workplace factors including workload and perceived control. In a study of 1,204 veterinary emergency care providers, workload was the workplace variable most strongly associated with emotional exhaustion and depersonalization. [7]

That finding should not be stretched into a claim that switching between screens, searching for a laboratory value, or fragmented clinical software directly causes burnout.

Those are different propositions.

A more defensible conclusion is that workload has organizational as well as individual components, and that reducing avoidable workflow burden is a reasonable design objective.

Whether a particular technology reduces workload, cognitive load, time spent on a case, or burnout must be tested rather than assumed.

What technology can and cannot do

A new clinical tool is useful only if it fits into the clinician’s actual work.

The relevant question is therefore not only:

What can the system do?

but also:

What does the veterinarian have to do in order to use it?

If a clinician must manually reconstruct the patient history, copy data from several systems, build a long prompt, and then check whether important context was omitted, an AI tool may simply introduce another layer of work.

Clinical technology should also preserve transparency.

The veterinarian should be able to inspect the underlying information, understand what context informed an output, and challenge or correct the result.

Automatically generated text does not become a clinical decision because it sounds confident.

Source documents may be incomplete. Laboratory values may need confirmation. A summary may omit nuance. An AI-supported interpretation may be based on insufficient context.

Those limitations belong inside the workflow, not in fine print after the answer.

How Terav approaches connected case context

Terav is being designed as a workspace for licensed veterinary professionals working across clinical cases, laboratory results, clinical documents, and AI-supported clinical dialogue.

Rather than treating each new laboratory value or document as an isolated query, the product is designed around the context of the patient: available history, current findings, relevant documents, and the clinical question being considered.

Terav supports laboratory-result workflows, PDF and image documents, and case-oriented clinical dialogue.

It is not intended to function as an autonomous diagnostic system. It does not replace physical examination, the quality of primary clinical data, professional experience, or the veterinarian’s responsibility for the final decision.

The design goal is narrower:

to make relevant case context easier to retrieve and review without requiring the clinician to reconstruct the same information manually each time.

At this stage, that should be understood as a product design objective, not as a validated product outcome.

Claims that Terav reduces cognitive load, saves a specific amount of time, reduces burnout, or improves clinical outcomes would require dedicated usability studies and prospective product evaluation.

Veterinary medicine will remain cognitively demanding because patients are complex and uncertainty is unavoidable.

The better goal is not to remove that complexity.

It is to design clinical workflows so that as much of the veterinarian’s attention as possible can be directed toward the patient and the next clinical decision rather than toward finding where the relevant information was stored.

References

1. Merriweather CA Jr, Lyytinen K, Aron D, Cauley MR. When better data meets better design: How EHR data usability and system usability shape physicians’ cognitive load. npj Digital Medicine. 2026;9:104. DOI: 10.1038/s41746-025-02243-4.
2. Rivera-Rodriguez AJ, Karsh B-T. Interruptions and distractions in healthcare: review and reappraisal. Quality & Safety in Health Care. 2010;19(4):304–312. DOI: 10.1136/qshc.2009.033282.
3. Li SYW, Magrabi F, Coiera E. A systematic review of the psychological literature on interruption and its patient safety implications. Journal of the American Medical Informatics Association. 2012;19(1):6–12. DOI: 10.1136/amiajnl-2010-000024.
4. Moy AJ, Hobensack M, Marshall K, et al. Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. Journal of the American Medical Informatics Association. 2023;30(5):797–808. DOI: 10.1093/jamia/ocad038.
5. Pfaff MS, Eris O, Weir C, et al. Analysis of the cognitive demands of electronic health record use. Journal of Biomedical Informatics. 2021;113:103633. DOI: 10.1016/j.jbi.2020.103633.
6. Liang JJ, Tsou C-H, Dandala B, et al. Reducing Physicians’ Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record. AMIA Annual Symposium Proceedings. 2021:763–772.
7. Holowaychuk MK, Lamb KE. Burnout symptoms and workplace satisfaction among veterinary emergency care providers. Journal of Veterinary Emergency and Critical Care. 2023;33(2):180–191. DOI: 10.1111/vec.13271.

Evidence note: Most of the research cited here on task switching, EHR usability, and cognitive load comes from human healthcare. It provides a relevant human-factors basis for veterinary software design, but it should not be interpreted as direct evidence that identical effects occur in veterinary clinicians.

The post Cognitive Load in Veterinary Practice: Designing for the Next Decision appeared first on Terav.

For veterinary teams, a structured laboratory review workflow can make the process more consistent without turning interpretation into a checklist exercise. The objective is not to outsource judgment to a reference interval, a protocol, or a software tool. It is to ensure that relevant context, patterns, uncertainty, and next steps are considered deliberately.

Why flagged values are not a clinical interpretation

Reference intervals are useful comparison tools, but they are not diagnoses. They are typically derived from a defined reference population, and their interpretation depends on the laboratory method, species, breed, age, physiological state, sample quality, and the individual patient’s prior values. A result just outside an interval may be less meaningful than a marked change within the interval for that particular patient.

This is a familiar principle in clinical pathology: analytical and biological variation affect measured results, and trends can carry information that a single data point does not. The American Society for Veterinary Clinical Pathology (ASVCP) provides guidance on reference intervals and their appropriate use, including the importance of laboratory-specific intervals and clinical context.

Under time pressure, the common risk is not lack of knowledge. It is premature closure: noticing one prominent abnormality, forming an early explanation, and not returning to the broader pattern. A reliable workflow creates a short pause before that happens.

A practical sequence for laboratory review

The exact sequence will differ by practice and patient type, but a consistent order helps reduce omissions.

1. Confirm the patient and the clinical question

Start with basics that are easy to overlook in a busy workflow: patient identity, species, age, breed, sex/neuter status, collection time, fasting status where relevant, medications, and the reason the tests were ordered.

A pre-anaesthetic panel, a workup for polyuria and polydipsia, and monitoring for a patient on chronic medication should not be read with the same question in mind. Defining the clinical question first helps distinguish incidental findings from results that change immediate decisions.

2. Check sample and reporting context

Before interpreting physiology, assess whether pre-analytical factors could matter. Hemolysis, lipemia, delayed processing, sample handling, collection technique, and anticoagulant issues can alter some measurements. If a result is unexpected or internally inconsistent, the appropriate next step may be verification, repeat sampling, or a conversation with the laboratory rather than an immediate clinical conclusion.

The laboratory report itself should also be read carefully: units, method, reference interval, comments, analyzer flags, and whether a manual review or smear evaluation has been performed can all be relevant.

3. Review the panel as a pattern

A structured scan can move from broad to specific:

• Identify results that may require same-day action in the context of the patient’s presentation.
• Review related analytes together rather than in isolation.
• Compare with prior results when they are available and comparable.
• Look for internal coherence and for findings that do not fit the apparent pattern.
• Reconnect the pattern to history, examination findings, imaging, urinalysis, cytology, or other available evidence.

For example, a renal value should not be interpreted independently of hydration status, urine concentrating ability, trends, blood pressure where relevant, and the rest of the clinical picture. Likewise, a stress leukogram, inflammatory leukogram, or regenerative response is a pattern-based assessment—not a conclusion generated by one cell count alone.

4. State uncertainty and define the next question

The endpoint of laboratory review is often not a diagnosis. It may be a more precise question: Is this change persistent? Does the patient need a blood smear review, urinalysis, imaging, repeat testing, or referral? Is there an urgent management implication today?

Documenting this reasoning in concise language helps the next clinician understand what was observed, what was considered, and what remains unresolved. It also supports clearer client communication without overstating certainty.

Workflow design matters as much as expertise

Experienced clinicians develop efficient mental shortcuts. These are necessary in practice, but they can be fragile when there are interruptions, handovers, or high caseloads. A team-level workflow can provide useful guardrails:

• define which result types trigger prompt review or escalation;
• standardize where prior laboratory data and relevant documents are stored;
• use a consistent note structure for interpretation and follow-up;
• make it easy to request a second review for discordant or high-stakes cases;
• distinguish preliminary impressions from final clinical decisions.

These steps are not about adding administrative burden. They are about making the reasoning already happening in the clinician’s head easier to revisit, communicate, and audit.

Where AI assistance can be useful

AI assistance is most credible in this setting when it supports organization and review rather than claims to diagnose a patient. It can help a veterinary clinician bring together a case history, laboratory results, and relevant clinical documents; surface relationships worth checking; summarize information for review; and help formulate follow-up questions.

That support may be particularly useful when results are distributed across reports, PDFs, images, and case notes. However, an AI-generated observation is not a validated clinical conclusion. The veterinarian must assess the source material, patient-specific context, plausibility, and urgency—and remains responsible for every decision.

Terav is designed for licensed veterinary professionals who need to work across case-based clinical dialogue, laboratory interpretation support, and clinical document workflows. It helps unify these materials in one workspace while keeping the veterinarian in control of clinical decisions. It does not replace examination, clinical pathology expertise, laboratory consultation, or professional judgment.

Limitations and safeguards

No workflow eliminates uncertainty. Reference intervals may not reflect every patient population; serial data may be unavailable; samples may be compromised; and important disease processes can exist despite unremarkable routine testing. In complex, discordant, or high-risk cases, consultation with a clinical pathologist, internist, or other appropriate colleague may be the safest next step.

Technology adds its own limitations. Outputs may be incomplete, may miss context, or may present information in a way that appears more confident than the underlying evidence warrants. Teams using AI-supported workflows should verify original reports, retain appropriate clinical documentation, protect patient data, and establish clear escalation pathways.

The practical aim is modest but valuable: make laboratory review more deliberate when the day is not. A structured sequence—patient question, sample quality, pattern recognition, trend review, uncertainty, and next step—helps veterinary teams use laboratory data as evidence within clinical reasoning, rather than as a list of flags to process quickly.

Sources

• American Society for Veterinary Clinical Pathology (ASVCP), Guidelines for the Determination of Reference Intervals in Veterinary Species and Other Related Topics: https://onlinelibrary.wiley.com/doi/10.1111/j.1939-165X.2012.00485.x
• ASVCP Quality Assurance and Laboratory Standards resources: https://www.asvcp.org/page/QALS
• eClinpath, Cornell University College of Veterinary Medicine, laboratory medicine educational resource: https://eclinpath.com/

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