Why fragmented pet tech data is falling short in modern pet care: full analysis
The pet tech industry is generating more health-related data than ever, but much of it still lives in separate apps, devices, and practice systems. That’s the core tension behind a new GlobalPETS report on fragmented pet tech data, which highlights MOVA’s strategy of linking smart hardware to an AI-based “digital twin” for pets. The company’s pitch is that signals from daily life, like litter box use, feeding, hydration, and movement, become more useful when they’re connected into a single picture of health. (barchart.com)
The idea lands at a moment when veterinary medicine is already grappling with broader data fragmentation. In the AVMA Journals podcast Veterinary Vertex, Lauren Grant described efforts to build an integrated companion animal health surveillance approach that can combine veterinary, human, and environmental data. She noted that many existing data sources are not compatible with one another, even though better integration could support more complex analyses, closer-to-real-world disease modeling, and faster risk mitigation. In Canada, and similarly in the US, companion animal surveillance still depends heavily on data submitted by veterinary professionals for specific diseases or syndromes, leaving important gaps in population-level visibility. (veterinaryvertex.buzzsprout.com)
That challenge isn’t new. Companion animal surveillance programs have been trying for years to make better use of veterinary electronic records and lab data. Earlier US work at Purdue and Banfield used electronic medical records from hundreds of hospitals, along with diagnostic lab feeds, to monitor emerging and zoonotic diseases. Other published work, including VetCompass and syndromic surveillance projects, has shown that practice management software can be mined for research and near-real-time surveillance when data can be extracted and standardized. (pubmed.ncbi.nlm.nih.gov)
What’s changed is the volume and type of data now being created outside the clinic. In MOVA’s recent funding announcement, the company said its system uses sensor-equipped devices and AI analytics to track indicators such as a cat’s weight, bathroom frequency, and duration, then convert those signals into longitudinal reports for pet parents. Industry commentary has made a similar case that wearables, connected feeders, and other home devices can build continuous baselines that may reveal changes before they become obvious during a routine visit. At the same time, researchers in digital health are pushing for interoperability standards, including FHIR-style approaches, to make pet and even human-pet wearable data more usable across care settings. (barchart.com)
There are also clear warnings in the background. A VCA One Health framework calls for formal data-sharing protocols, real-time surveillance systems, and stronger collaboration between veterinary hospitals and diagnostic laboratories during public health threats. That reflects a practical reality for clinics: more data isn’t inherently better if it arrives in incompatible formats, lacks context, or creates extra review burden without reimbursement or clinical validation. Even advocates for integrated pet AI acknowledge that data-sharing models, consent, and workflow design will determine whether practices participate. (vcahospitals.com)
Why it matters: For veterinary professionals, fragmented pet tech data is really a clinical governance and workflow issue. If home-device data remains locked inside consumer platforms, veterinarians may miss useful trend information while pet parents assume their devices are already “monitoring health.” If that same data becomes portable, standardized, and clinically interpretable, it could strengthen preventive care, chronic disease monitoring, post-treatment follow-up, and One Health surveillance. But getting there will require better data standards, clearer validation of what these devices can actually measure, and practical rules for how outside data enters the record and influences care. (veterinaryvertex.buzzsprout.com)
What to watch: The next milestone isn’t likely to be another smart bowl or collar. It’s whether pet tech vendors, veterinary software companies, diagnostic labs, and researchers can agree on interoperability, consent, and evidence standards that make connected-pet data clinically useful at scale. If they can, the payoff could extend beyond individual wellness tracking to earlier disease detection and stronger companion animal surveillance. If they can’t, the market may keep producing more data without delivering much more insight. (veterinaryvertex.buzzsprout.com)