The son of a veterinarian, I grew up in a clinic and, like many of my colleagues, knew from my earliest memory that I would become a veterinarian. My recollection of companion animal practice revolves around large shifts in the industry. I remember hearing the word “parvovirus” in the late 1970s, when I was a child, and the need for veterinarians to vaccinate dogs against the disease. In the 1980s, fleas, heartworm disease and vaccines were a mainstay of my father’s Florida practice. For most of my professional life, from the late 1990s until recently, the development and expansion of specialty and referral practice occupied me. Today, I can see with absolute confidence how advancements in technology, such as the application of machine learning and artificial intelligence, have transformed primary practice from a historic preventive care model to one that delivers high-quality diagnostics and care.
Patients Stay Closer to Home
“Why Software Is Eating The World” was a 2011 headline in The Wall Street Journal. The essay’s author, venture capitalist Marc Andreessen, explained that major technology companies were pivoting from selling hardware to investing in software. That was only a decade ago.
Technological improvements, driven by advances in computational ability and large amounts of data, are inevitable. What’s also unavoidable is that technology will change veterinary medicine, creating greater opportunities. Rather than fear technology, we have ample reason to understand that change happens and that we can adapt.
In the not-so-distant past, a companion pet needing specialty care typically was referred to the nearest veterinary college. Clients drove for hours to see a specialist, while GP and primary matters stayed in local hospitals.
When I opened my first referral-only specialty hospital in 1996, clients were pleased not to have to drive several hours. However, within a decade, referred clients began expressing an unwillingness to drive across town during rush hour for the same level of care. Branch hospitals and multiple types of specialty care popped up within large metropolitan areas.
What also quickly became evident was that pet owners preferred to visit their primary care practitioner for the treatment of routine illnesses and injuries, a trend that continues today. Clients and patients are best served by primary care practitioners heavily invested in an animal’s care and treatment.
The Financial Equation
Technological innovation is transforming veterinary care faster than ever. The opportunity to keep yesterday’s specialty cases in-house (a client preference) is growing. Primary veterinarians can provide a level of service similar to that of specialists, practicing medicine that is more interesting to GPs and driving more revenue to their practices.
And let’s not forget that the strong human-animal bond has led to clients spending more on their pets’ care than ever before. Pet owners increasingly choose advanced medical care and are willing to pay for it, and they expect the care their pets receive to be similar to what human medicine provides.
Today’s challenges are twofold: a shortage of specialists and routine referral specialty care that is quickly becoming cost-prohibitive to families who care deeply for their pets. GPs need assistance in providing high-quality advanced care at affordable prices.
Easier Radiology Reads
Radiology is a diagnostic tool for which many clinicians do not get enough training or practice to stay sharp, and they rely heavily on pricy consults. Even worse, some clinicians might not use radiology as often as they should; they don’t feel comfortable reading a radiograph in-house and don’t want to charge their clients a consult fee. The solution: Technology-aided pattern recognition of routine radiographs, an innovation that enables veterinarians to do more within their primary care practices.
As technology advances, more and more veterinary practices are turning to artificial intelligence for clinical support, keeping more radiology services in-house.
The use of artificial intelligence in veterinary care might seem the wave of the future, but today it can increase the quality of care without driving up the cost to the client.
Understand that microprocessors are exceptionally good at predicting outcomes based on recurring patterns, and automated systems can be trained to recognize specific syndromes on plain films with a high degree of accuracy. A doctor looking for foreign gastric material might miss a pulmonary nodule, but with AI reads, all the films are scanned for all syndromes, every time. No additional hardware is required, just an internet connection. The answers arrive almost in real time as the films are obtained.
Room for Growth
The short-term results of AI in such a scenario are:
- High-quality radiographic reviews for most common syndromes.
- An increase in clinician confidence.
- A reliance on radiology as a diagnostic tool.
- Better patient care at primary practices.
Long term, radiology AI will expand to include less-common syndromes and niche areas such as dental films. With AI as clinical support, GPs could routinely use radiology as a diagnostic tool and call specialists for truly unusual cases. Additionally, given how fast AI learns and innovates, I see a time when it expands further into the diagnostic thought processes, even prioritizing clinical differentials and diagnostic outcomes.
Diagnostic tools like radiology AI will never replace the need for a specialist, but the technology will help practitioners in their decision-making more and more. The result will be increased quality of care by primary practitioners and heightened expectations among clients, referring veterinarians and specialists. The proverbial rising tide lifts all boats.
The boundaries of specialty care will continue to grow, leading to an enhanced quality of care at all levels and care that is less costly to clients. As veterinary care innovates faster than ever before, now is an exciting time to be a veterinary clinician.
THE BIRTH OF AI
The field of artificial intelligence is believed to have started in Hanover, New Hampshire, in 1956.
According to Dartmouth College, “A small group of scientists gathered for the Dartmouth Summer Research Project on Artificial Intelligence. … The initial meeting was organized by John McCarthy, then a mathematics professor at the college. In his proposal, he stated that the conference was ‘to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.’”
Also present was one researcher each from Harvard University, IBM Corp. and Bell Telephone Laboratories.