A repeatable review process helps here—not because every case should follow the same path, but because a shared structure makes omissions easier to spot when the day is busy.

Start with the patient, not the flags

A laboratory report is one part of a case record, not a diagnosis. Before focusing on abnormal values, reconnect the result to the patient: species, breed, age, sex and reproductive status; presenting complaint; timeline; examination findings; medication and supplementation history; diet; and the clinical question that prompted testing.

That context changes the weight of a result. A mild abnormality may be expected in one patient, pre-analytical in another, and important because it is new or trending in a third. The same applies to a value within the reference interval: a “normal” result does not automatically resolve a concern if it has changed materially for that individual patient.

A useful first pass is to ask four questions:

1. What question was this panel meant to answer? Screening, monitoring, triage, anaesthetic assessment and investigation of a specific syndrome call for different emphasis.
2. Is the sample and result technically credible? Consider collection conditions, haemolysis, lipaemia, sample handling, timing, and whether the analyte is stable under those conditions.
3. Which findings form a pattern? Single values matter, but relationships among results often matter more.
4. What needs action now, what needs clarification, and what can be monitored? This is a prioritisation task, not a flag-counting task.

Distinguish variation from change

A small numerical difference between two results can reflect more than disease progression. Analytical variation comes from the measurement process; biological variation reflects normal fluctuation within and between patients. Collection conditions and patient state can add further noise.

That does not make modest changes irrelevant. It means they need interpretation in context. Was the same laboratory and method used? Were samples collected under comparable conditions? Is the direction of change consistent with the history, examination, and other laboratory findings? Is there a plausible reason to repeat the test before building a larger work-up around it?

Reference intervals are population-based decision aids. They are not a line between “healthy” and “diseased,” nor do they define an individual patient’s baseline. Species, breed, age, physiological state, and concurrent treatment can all affect interpretation. A flagged result should therefore trigger a clinical question—rather than function as a diagnosis label.

Review patterns and trends before building a plan

Pattern recognition is one way to reduce cognitive load without pretending that interpretation is automatic. In practice, that may mean reviewing a CBC as a set of relationships, looking across renal markers and urinalysis rather than isolating one chemistry value, or considering liver-associated changes alongside history, examination, imaging, medication exposure, and sample quality.

Trends are often particularly useful when the clinical question is monitoring. A single result may be ambiguous; a sequence can show persistence, progression, response, or inconsistency worth investigating. But trends also need discipline. A graph with missing dates, uncertain sample conditions, or results from different methods can create false confidence.

The practical output of a review is not always an answer. It may be a better next step: confirm the history, check the original report for analyser comments, review a smear, obtain urine data, repeat a targeted measurement under comparable conditions, discuss imaging findings, or document why watchful follow-up is reasonable. The appropriate step remains case-specific.

The workflow problem is often fragmented context

Many lab-review errors are workflow errors before they are knowledge errors. The result may sit in one system, the history in another, an imaging report in a PDF, and the reasoning behind the previous plan in a message thread or handwritten note. When the clinician has to reconstruct this picture repeatedly, important context can be missed and handovers become harder.

A team-level process can reduce that friction. For each result that changes the working plan, record the relevant patient context, the findings that matter, the competing interpretations, the uncertainty, and the follow-up trigger. This does not require a long narrative every time. A concise, consistent note can make the next review safer and quicker.

Where AI assistance can help and where it cannot

AI assistance may be useful at the workflow layer. It can help a veterinary team consolidate case material, review laboratory results alongside clinical documents, and surface questions that deserve a clinician’s attention. In a case-based workspace, it may reduce the time spent locating the relevant history, prior values, or document details before a review begins.

That is different from deciding what a result means. AI cannot establish a diagnosis, determine urgency, prove causality, or select an appropriate plan without veterinary judgment. It also cannot resolve gaps in the history, validate a poor sample, or know whether a numerical change is clinically meaningful without the information a clinician brings to the case.

The most useful implementation is therefore specific: use assistance to organise and interrogate information, then make the clinical decision through the normal professional process. A tool should make it easier to see the case, not encourage the team to treat a generated summary as a conclusion.

A practical checklist for a repeatable review

Before closing a lab review, a clinic can ask:

• Do we know why this test was run and what has changed clinically since sampling?
• Have we checked patient factors, collection conditions, and report comments that could affect interpretation?
• Are we responding to a coherent pattern or to an isolated flag?
• Have we compared relevant prior values, methods, and sampling conditions?
• Is the next action documented clearly: immediate intervention, further investigation, repeat testing, monitoring, or no change?
• Can another clinician understand the reasoning and follow-up trigger from the case record?

Terav is designed for this kind of case-based work: licensed veterinarians can bring clinical dialogue, laboratory interpretation support, and clinical documents—including PDF and image workflows—into one workspace. It supports review and documentation, while the veterinarian remains responsible for the interpretation, diagnosis, priorities, and plan.

Sources and further reading

1. Flatland B, Freeman KP, Vap LM, Harr KE. ASVCP guidelines: quality assurance for point-of-care testing in veterinary medicine. Veterinary Clinical Pathology. 2013.
   https://doi.org/10.1111/vcp.12099
2. Friedrichs KR, Harr KE, Freeman KP, et al. ASVCP reference interval guidelines: determination of de novo reference intervals in veterinary species and other related topics. Veterinary Clinical Pathology. 2012;41(4):441–453.
   https://doi.org/10.1111/vcp.12006
3. Arnold JE, Camus MS, Freeman KP, et al. ASVCP Guidelines: Principles of Quality Assurance and Standards for Veterinary Clinical Pathology (version 3.0). Veterinary Clinical Pathology. 2019;48(4):542–618.
   https://doi.org/10.1111/vcp.12810
4. Gunn-Christie RG, Flatland B, Friedrichs KR, et al. ASVCP quality assurance guidelines: control of preanalytical, analytical, and postanalytical factors for urinalysis, cytology, and clinical chemistry in veterinary laboratories. Veterinary Clinical Pathology. 2012.
   https://doi.org/10.1111/j.1939-165X.2012.00412.x
5. Fraser CG. Reference change values. Clinical Chemistry and Laboratory Medicine. 2012;50(5):807–812.
   https://pubmed.ncbi.nlm.nih.gov/21958344/
   DOI: 10.1515/CCLM.2011.733

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