Analyzing veterinary data: It's all about the 'why' axis

Analyzing veterinary data: It's all about the 'why' axis

AVMA committed to developing a robust picture of profession's economic situation.
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Jul 01, 2014


GETTY IMAGES/MARTIN BARRAUD
In April's dvm360, I noted that the first step to producing a more efficient, better-performing veterinary services market is to collect, analyze and distribute better information. However, a California veterinarian recently noted that the American Veterinary Medical Association "has been collecting, analyzing and distributing economic and market information for decades." This, he reasons, implies that a "great amount of time, money and effort expended heretofore by the AVMA and its hires has largely been wasted, inasmuch as the information gathered does not adequately answer the essential needs."

I want to address this apparent contradiction—why the AVMA is conducting more research when its efforts to this point have not prevented the problems we're experiencing—both here and in upcoming issues of dvm360. In the AVMA Eye on Economics column, I'll outline how the AVMA is approaching the economics of our profession, both in terms of data analysis and the crafting of solutions.

The current situation

Even before I arrived at the AVMA as economics director, I took stock of the data that was being collected and found it to be generally of sufficient statistical quality to perform a basic economic analysis. But I also found considerable gaps that made it difficult to analyze the markets adequately. For instance, when I started hunting for information, I found that the price and quantity of veterinary services (taken together) was unavailable.

I also noted that AVMA data was routinely "cherry-picked" to deliver a specific message. To quote an old adage, the data was used the way a drunk uses a lamppost: for support and not illumination. In most of the published literature regarding veterinary medicine, I found that the hypothesis often steered the collection and use of data in its own support, rather than the data being objectively collected and analyzed to prove or disprove the hypothesis.