The information problem
Health data is heterogeneous because health itself is contextual.
Some information is structured. Some arrives as a laboratory result, prescription, study, report, or form. Some is spoken during a consultation. Some is an observation from a patient, family member, animal owner, teacher, therapist, physician, or veterinarian. Some is a generated summary of something else.
A useful system should not flatten these into indistinguishable text. It should preserve enough structure to answer basic questions about every important piece of information.
Document, consultation, professional note, owner or family observation, imported result, transcription, or generated summary.
Creation time is not always the same as the clinical time to which the information applies.
Authorship and role matter when multiple people participate in care.
Events, medications, symptoms, results, plans, documents, follow-up, and previous history give a record meaning.
Longitudinal information
Instead of treating the current value as the whole truth, longitudinal systems preserve change. This is relevant to SmartMatrix, Animal Care, and rAIn: the value is not only what a record says today but how the current state relates to what was observed before.
Interoperability research
The work has included examination of standards and terminology systems used to exchange or normalize healthcare information, plus the question of how much of that ecosystem translates to veterinary medicine.
Studied as modern healthcare interoperability approaches for exchanging structured clinical information and resources.
Explored in relation to structured clinical documents and continuity-oriented information exchange.
Considered for standardized identification of laboratory and clinical observations.
Relevant to the question of veterinary-specific terminology and the limits of directly reusing human-health models.
Retrieval before generation
Artificial intelligence becomes useful in health when it can summarize, explain, organize, translate, or connect information without silently replacing the underlying record. The preferred architecture is therefore evidence-first.
The system should distinguish retrieved information, human observations, generated summaries, and model inferences. If authorized context is insufficient, “there is not enough information” is a valid and often preferable outcome.
rAIn as a concrete contextual design
rAIn provides the most detailed design example of this approach. Its health workflow connects appointments, prepared questions, event capture, audio, transcription, structured summaries, extracted medications/dates/decisions/follow-up, documents, previous events, permissions, and later natural-language questions.
The rAIn design also treats consent, permissions by domain and event, traceability, access logs, revocation, retention, export, and deletion as part of the architecture rather than simply UI settings.