Digital Triage as a Gateway to Veterinary Medicine

1. Introduction

Digital symptom checkers and online triage systems have established themselves as early forms of user-side decision support. They systematically record Symptoms: and are intended to provide an initial assessment of urgency. However, systematic analyses show that their diagnostic and triage accuracy is variable and remains limited, especially in complex clinical situations (Semigran et al., 2015; Chambers et al., 2019; Wallace et al., 2022).

In parallel, LLMs have given rise to a new generation of digital health systems. Unlike rule-based approaches, they enable conversational interaction, the processing of free text input, and the integration of context over multiple dialogue steps. Medically, such systems are increasingly discussed as applications close to clinical decision support, but at the same time are critically evaluated due to clinical reliability, bias, and potential misjudgments (Topol, 2019; Singhal et al., 2023; Thirunavukarasu et al., 2023).

This development is particularly relevant for veterinary medicine. Clinical information here is not based on self-reports from patients, but on observations by pet owners. Symptoms: are described indirectly, often non-specifically, and emotionally charged. This leads to increased Uncertainty / disorientation in assessing urgency and the need for action, especially in acute situations.

LLM-supported triage systems can help to reduce the gap between subjective observation and medical initial assessment. However, this requires that natural language processing is combined with medically validated decision logic, red-flag detection, and veterinary control. The aim of this article is not to view digital triage in isolation as a technical tool, but as part of a socio-technical care system.

2. New Approaches to Veterinary Care

Access to veterinary care is changing not only due to new digital tools, but also due to a shift in the first decision point. The central question increasingly arises before Contact with the practice or the hospital: How urgent is the situation and what care step is appropriate?

The classic approach was based on direct Contact: pet owners called the practice, described the Symptoms:, or presented the animal immediately. The urgency was then assessed by the veterinary staff. Digital contact points shift parts of this process forward and influence decisions about observation, appointment scheduling, or emergency presentation even before the first personal Contact.

This shift is relevant for care. It affects patient flows, workload, and potentially patient safety. Unstructured, contradictory, or non-individualized digital information can promote overtriage and undertriage. Earlier symptom checkers addressed this problem with standardized questionnaires and decision trees but remained limited in complex or non-specific situations (Semigran et al., 2015; Chambers et al., 2019; Wallace et al., 2022).

LLM-supported systems change this initial situation because they can design the digital first Contact as a conversational interaction. Pet owners describe Symptoms: in free language; the system asks follow-up questions, clarifies unclear information, and structures relevant information. The centrally important progress is not that an LLM makes clinical decisions, but that it can serve as an interaction layer between pet owner observation and medical decision logic.

For responsible application, this language layer should be limited by rule-based red-flag detection, defined escalation mechanisms, and veterinarian-approved content. Modularly structured systems can separate symptom collection, risk assessment, triage classification, and user communication. This improves traceability, quality control, and clinical verifiability.

3. Information Behavior of Pet Owners

This search differs fundamentally from human medicine. Pet owners do not research their own complaints, but observations of an animal. They must interpret behavior, food intake, activity, breathing, pain expressions, Vomiting, Diarrhea, Lameness, or urination, without the animal being able to verbally communicate its complaints. Search queries therefore often begin with non-specific observations such as altered behavior, withdrawal, inappetence, or reduced activity (Lai N, Khosa DK, Jones-Bitton A, Dewey CE, 2021)

Especially outside regular practice hours, this Uncertainty / disorientation becomes relevant. Digital search can then become the primary source of orientation, without reliably taking over the structuring function of professional triage. LLM-supported systems can take on a specific task here: converting vague observations into a more medically structured information base, asking follow-up questions, recording the temporal course and accompanying Symptoms:, and checking for potential red flags.

Thus, a controlled LLM triage system differs from an unstructured internet search. While search engines provide information, digital triage conducts a dialogue and converts information into a defined decision logic. However, the quality of the assessment depends on the quality of the information. An LLM can clarify unclear descriptions but does not replace a clinical examination. Therefore, conservative escalation rules, transparent system limits, and clear indications for veterinary clarification are indispensable.

4. Burden on Veterinary Emergency Service Systems

Veterinary emergency service structures in many regions are under considerable organizational pressure. Causes include limited personnel resources, increasing expectations from pet owners, increasing specialization, regional differences in availability, and many inquiries whose medical urgency is difficult for pet owners to assess. Comparable problems are known from human medicine, where the non-urgent use of emergency structures and Uncertainty / disorientation in symptom interpretation are described as relevant influencing factors (Chambers et al., 2019; Wallace et al., 2022).

From a care structure perspective, not only the number of emergency contacts matters, but also the fit between clinical urgency and the chosen care pathway. Emergency service systems function efficiently when acutely critical cases are quickly identified and prioritized, while non-urgent concerns can be directed to regular structures. Misdirection is problematic in both directions: overtriage ties up resources, undertriage can delay necessary treatment and endanger animal welfare (Semigran et al., 2015; Wallace et al., 2022).

Our own clinical data support this problem. In a retrospective analysis of 13,921 emergency consultations from January 1, 2020, to March 15, 2026, 17% of cases were classified as true Emergencies. 45% were urgent, 38% medically not acute. This distribution clearly shows that a significant proportion of emergency service utilization could be characterized by Uncertainty / disorientation in urgency assessment, and highlights the need for upstream triage and control mechanisms.

Digital triage systems can serve as an upstream support level in this context. Their function is not to replace emergency service decisions, but to structure the phase before Contact with the practice or the hospital. LLM-supported systems expand this potential because they can better capture emotionally charged, incomplete, or everyday symptom descriptions than classic questionnaires.

The security architecture is crucial. An LLM must not function as an autonomous decision-making instance in emergency-related applications. A hybrid model of natural language processing, conservative rule-based red-flag detection, and clear escalation mechanisms is required. In cases of Uncertainty / disorientation or potentially critical Symptoms:, the system should escalate in favor of veterinary clarification (World Health Organization, 2021; Liu et al., 2020; Vasey et al., 2022).

For practices and hospitals, such systems can support structured preliminary histories, early identification of critical cases, more targeted telephone inquiries, and the referral of non-urgent concerns. Whether this actually leads to relief and better patient management depends on the medical quality, the safety-oriented design, and the integration into real practice and hospital processes.

5. What is Digital Triage?

Digital triage refers to the structured digital initial assessment of Symptoms: with the aim of classifying the urgency of further veterinary clarification and supporting the next appropriate care step. It is clearly to be distinguished from general health information and diagnosis. Information offerings provide knowledge; diagnosis aims to identify a disease; triage prioritizes and navigates within a care pathway (Chambers et al., 2019; Semigran et al., 2015).

In human medicine, triage is an established principle for classifying treatment urgency. Digital triage transfers this principle to the upstream area, i.e., the phase before direct Contact with medical personnel. For veterinary medicine, it must be considered that pet owners do not experience Symptoms: themselves, but interpret observations.

Digital triage can therefore be understood as an mediating structure between pet owner observation and veterinary care. It translates subjective, often non-specific information into a more structured information base and supports the decision of whether observation, regular presentation, timely clarification, or immediate emergency care is clearly indicated.

Earlier digital triage systems were often rule-based questionnaires or decision trees. This model offers standardization but is limited when users describe Symptoms: in a lay, incomplete, or freely formulated manner. LLM-supported systems supplement this model with a conversational interaction layer: they can ask follow-up questions, structure information, and convert relevant information into a medically usable format.

A secure model typically includes free symptom collection, structured follow-up questioning, red-flag checking, triage classification, non-diagnostic action recommendations, and escalation in cases of Uncertainty / disorientation. Modularly structured systems can separate these functions, thereby improving traceability, quality control, and clinical verifiability. Digital triage is thus less a single tool than a process within a care pathway.

6. The Role of AI in Veterinary Triage

Artificial intelligence can primarily extend digital triage where classic rule-based systems reach their limits: in processing unstructured, lay, and context-dependent symptom descriptions. Its central role is not in autonomous diagnosis, but in supporting structured information gathering and preparing veterinary assessment (Topol, 2019; Esteva et al., 2019).

LLMs can process free text input, recognize linguistic patterns, and consider context over multiple interaction steps. This is relevant for veterinary medicine because the initial information comes from pet owners who must observe and interpret behavior, activity, food intake, breathing, pain expressions, or other clinical signs (Thirunavukarasu et al., 2023).

In this context, an LLM can serve as a conversational interaction layer: it formulates follow-up questions, clarifies unclear information, and systematically collects relevant information. In this way, the quality of the preliminary history can be improved before veterinary Contact occurs. This function remains supportive and must not be understood as an independent veterinary assessment (Singhal et al., 2023; Thirunavukarasu et al., 2023).

For safety-critical applications, a freely responding LLM is not sufficient. Triage requires a conservative, traceable decision logic, especially in potential Emergencies. Therefore, a hybrid system design is necessary: language processing supports interaction, while medically defined rules, red-flag criteria, and escalation mechanisms control risk assessment (World Health Organization, 2021; Vasey et al., 2022).

A possible architectural principle is the functional separation of individual system components, such as symptom collection, red-flag checking, urgency classification, user communication, and escalation. Such a modular or agent-based architecture can improve transparency, auditability, and clinical verifiability, but should itself be systematically evaluated (Liu et al., 2020; Vasey et al., 2022).

The limits remain clear: even specialized or database-supported LLM systems can generate incorrect or clinically inappropriate outputs. Linguistically convincing answers must not be equated with clinical reliability. Clinical assessment, diagnosis, and therapy decisions remain veterinary tasks.

7. Opportunities for Practices, Hospitals, and Emergency Services

LLM-supported digital triage systems can become relevant for veterinary practices, hospitals, and emergency services if they are implemented not in isolation, but as part of veterinary care pathways. Their benefit lies less in the automation of medical decisions than in the structuring of upstream information and communication processes.

A central function is to improve the initial information quality. Pet owners often describe Symptoms: with Uncertainty / disorientation, emotionally, or only partially. A conversational system can systematically collect relevant information, such as the temporal course, accompanying Symptoms:, pre-existing conditions, age, medication, or potential key symptoms. This creates a more structured preliminary history that prepares subsequent veterinary decisions without replacing them.

For practices, this can be particularly relevant for initial communication. Telephone inquiries tie up personnel resources and often occur with Uncertainty / disorientation. Digital pre-structuring could make conversations more targeted, capture relevant information more quickly, and prepare decisions about appointment scheduling, callbacks, or emergency presentations. Requirements for description, evaluation, and human-AI interaction are addressed in CONSORT-AI and DECIDE-AI (Liu et al., 2020; Vasey et al., 2022).

In the emergency service context, further potential lies in upstream patient management. LLM-supported triage could help to identify critical cases early and direct non-urgent concerns more specifically to regular care. However, human medicine studies show that digital symptom checkers and online triage systems vary in their accuracy; their benefit should therefore be evaluated context-dependently (Semigran et al., 2015; Chambers et al., 2019; Wallace et al., 2022).

Digital triage can also improve access to care, for example, outside regular practice hours or in regions with limited veterinary availability. The prerequisite is that systems are understandable, secure, low-threshold, and linked to real care offerings (Rodriguez et al., 2020; World Health Organization, 2021).

In the long term, LLM-supported systems can contribute to the standardization of veterinary care pathways. Frequent key symptoms such as Vomiting, Diarrhea, Lameness, respiratory problems, Convulsions, or urination disorders can be pre-structured based on defined risk parameters. However, benefits remain hypotheses as long as veterinary validation and implementation studies are lacking.

8. Risks and Limitations

LLM-supported digital triage systems are associated with relevant risks. These concern not only technical accuracy, but also patient safety, user behavior, responsibilities, and integration into existing care structures (World Health Organization, 2021; Thirunavukarasu et al., 2023).

The reliability of digital triage largely depends on the quality of the information provided. Incomplete, misleading, or emotionally charged information can lead to erroneous assessments. In veterinary medicine, this problem is particularly pronounced because clinical information is based on observations and interpretations by pet owners.

A specific risk of LLM-supported systems is incorrect or clinically inappropriate outputs. Even curated or database-supported systems can generate faulty context interpretations, incomplete information processing, or incorrect clinical weightings. In a triage context, even small misjudgments can lead to overtriage or undertriage. Linguistic coherence must therefore not be confused with clinical reliability (Singhal et al., 2023; Thirunavukarasu et al., 2023; World Health Organization, 2021).

Further risks concern automation bias, bias, and generalizability. Pet owners may over-rely on digital recommendations, especially if they appear personalized and empathetic. AI systems can also adopt biases from training data or knowledge bases. In veterinary medicine, data varies considerably depending on animal species, breed, age, housing, region, and care setting.

Regulatory and ethical challenges include responsibility, liability, Privacy Policy, and data security. It should be clearly defined who is responsible for medical content, system updates, error monitoring, data processing, and user communication. Responsible implementation requires data minimization, secure storage, transparent consent, and protection against secondary use without an adequate basis.

Ultimately, there is a risk of insufficient integration. If digital triage is used in isolation, without connection to practice processes, emergency service structures, or telemedicine, information gaps arise. Users receive a digital assessment but no clear connection to appropriate care. Therefore, not only models but also the entire socio-technical processes must be validated and monitored.

9. Integration into Veterinary Care Pathways

The future role of LLM-supported digital triage depends on whether such systems can be meaningfully integrated into existing veterinary care structures. Crucial is not only the technical performance of individual models but also their integration into clinical processes, responsibilities, communication, and quality assurance (World Health Organization, 2021; Vasey et al., 2022).

Rastogi N. Healthcare’s new frontier: the digital front door. BMJ Innovations. 2022;8(2):129–132. doi:10.1136/bmjinnov-2021-000874

At the practice level, digital triage systems can be used as structured preliminary histories before appointment scheduling or consultation. The collected information would have to be prepared in such a way that it is quickly verifiable and clinically usable for veterinary personnel. Digital information must not be adopted unchecked but serves as a preparatory information base.

In the emergency service context, such systems can support upstream risk stratification. Due to the risk of undertriage, they must be designed conservatively and, in cases of Uncertainty / disorientation, decide in favor of veterinary clarification. Another integration possibility is the connection with telemedicine: orientation and structured data collection can, if necessary, transition into telemedical consultation and subsequently into in-person care.

For sustainable integration, defined processes for data transfer, documentation, responsibility, and quality assurance are required. Privacy Policy, consent, and data security must also be considered, especially when information about the animal, owner, location, health history, or insurance context is processed.

Acceptance by veterinarians is of central importance. Digital triage systems will only be used meaningfully if they are understood as support for clinical work and not as an alternative to veterinary assessment. Veterinary professionals should therefore be fundamentally involved in development, validation, monitoring, and continuous improvement (Vasey et al., 2022).

10. Conclusion

LLM-supported digital triage marks a possible further development of pet owner initial assessment from static symptom checkers to conversational, structured, and integrable systems within care pathways. Its central contribution lies not in autonomous diagnosis, but in organizing the upstream decision phase: collecting non-specific observations, structuring relevant information, and supporting the initial classification of urgency (Semigran et al., 2015; Chambers et al., 2019; Wallace et al., 2022).

This development is particularly relevant for veterinary medicine because clinical information is usually conveyed indirectly through pet owners. LLM-supported systems can serve as an interaction layer that converts lay descriptions into a more systematic information base. However, their benefit depends on whether they are combined with validated medical decision logic, red-flag detection, and conservative escalation mechanisms (Thirunavukarasu et al., 2023; Singhal et al., 2023; World Health Organization, 2021).

The greatest opportunities lie in improved preliminary history, structured communication, potentially better patient management, and expanded access to orientation before the first veterinary Contact. Nevertheless, central risks remain: mis-triage, incorrect or clinically inappropriate outputs, bias, automation bias, unclear responsibilities, and a lack of veterinary validation data.

It follows that LLM-supported digital triage should not be understood as an independent decision-making instance. It should fundamentally develop and evaluate as a clinical decision support, a closely related, veterinarian-controlled component within veterinary care pathways. Crucial are transparent system limits, prospective validation, continuous monitoring, human-in-the-loop structures, and integration into real practice and hospital processes (Liu et al., 2020; Vasey et al., 2022; World Health Organization, 2021).

The future value of such systems will be determined less by linguistic performance than by clinical safety, organizational integration, and acceptance within the veterinary profession. LLM-supported digital triage can only become a meaningful component of future veterinary care if technological innovation is consistently linked with evidence-based development, medical responsibility, and care structure integration.

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