A demanding veterinary day is not difficult only because of the number or complexity of patients. Each case generates a moving set of information: history, examination findings, laboratory results, imaging reports, previous treatments, owner communications, and follow-up plans.
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.