How to overcome elemental and structural data challenges to advance pet health data

Aug 31, 2021 | Industry Trends

I admit it: I’m that person who says things like, “Excel is the most powerful tool in the office.” I’m also one who thinks the right data — and the right approach to data — can help us achieve amazing things for animals, for their caretakers, for veterinarians, and for our industry.

What I’m not is one of those people who think our ability to improve animal health outcomes through data will magically happen. Or that “AI will take care of it all.”

We must overcome elemental and structural data challenges to advance pet health data. Understanding how we collect data is intrinsic to understanding how we can use data. My goal is to describe what I see as the major hurdles with pet health data and what it’s going to take to get us to a place where the promises of “big pet health data” can be fulfilled. 

(Note: It might be good timing to say that I think AI has the potential to be an immensely powerful tool in analyzing pet health data, but there are big limitations based on our current datasets.) 

Who is in charge of pet health data?

Everyone and no one manage pet health data—it’s a shared responsibility. As I’ve learned about data management over the years, the two terms I’ve really latched onto are governance and stewardship. Here is the simple way I like to explain it:

  • Data governance — The  process by which an entity (person, group, organization) decides what data will be collected and what those data will look like
  • Data stewardship — The process by which those data are collected and managed, and the culture that surrounds it

In a typical independent small animal practice, for example, pet health data is recorded in a commercial practice management system (PMS):

The PMS provider is taking the role of data governor, establishing what data fields they will make available to the practice, what units of measurement are valid in certain fields, and, in many cases, the workflows or policies that govern the collection of those data

Conversely, the practice takes on the role of the data steward* — they decide what data gets entered into which boxes, as well as control the rigor of the processes that help maintain the quality and consistency of those data

Setting aside the question of who owns those data, both the governor and the steward have degrees of responsibility for the ability to collect the data, and for the quality of the data collected.

How do these data roles play out in real life?

Let’s say that we, as an industry, decided that we were going to give ourselves the best chance of tackling the pet obesity epidemic. After passing around the collection plate, consulting all the relevant stakeholders, and paying a strategic marketing agency a handsome sum, we have a plan. A national media campaign, celebrity spokespeople, social media gamification, nutrition partnerships, a gym membership for every pet, and the buy-in of every PMS system and veterinary clinic in the country.=

We have decided to measure success by looking at the national average of every pet’s body condition score (BCS) on a month-by-month basis. We’ll collate the data from every PMS system for every exam that was performed in that month and average out the results.

What’s the role of governance and stewardship in this type of effort?

  • Governance — The PMS system first must be capable of recording the BCS as a data field. If there’s nowhere to put the data, it’s much harder to meaningfully collect and collate it. The PMS system, as the data governor, will also have to decide whether they’re going to force the user to choose the BCS system that scores out of 5, or the one that scores out of 9. (It could allow both, but let’s keep things simple.) Then to make it easiest for the end-user, work out where in their established workflows it makes sense to ask for the data. All these small but important tasks will make or break whether a practice could comply with this data effort.
  • Stewardship — Effective change management around the use of technology is not easy in clinics. Ensuring that everyone is trained on where and how the data are to be input (“I like the BCS out of 5, not 9” etc.), setting up reporting to see where the process might be failing, reporting on average BCS by staff member (is Dr. X’s BCS average two points higher than Dr. Y’s?)—these are all responsibilities that will fall on the practice.

The goal isn’t for this to feel like an impossible mountain to climb, but just to illustrate how much cooperation and collaboration are necessary to establish even a single standardized pet health data metric.

How do we get where we need to be?

The scale of the effort needed to improve pet health data can feel daunting, but there are a few key areas I think are critical in improving the quantity, quality, and integrity of pet health data:

1: Cultivate a culture of data

Two veterinary doctors intently gazing at a tablet screen on a desk.To quote the excellent AVMA – AAVMC Veterinary Futures Commission Executive Summary (2019), it’s going to be on us, as a profession and as an industry, to create a culture where “digital competence, data literacy [and] technological familiarity” are valued traits and ones that are given a place in our training, our CE and our career paths.

Building on the work of organizations like the Association for Veterinary Informatics, and the Evidence-Based Veterinary Medicine Association, influential coalitions of veterinary professionals and external stakeholders are essential to educating and guiding our profession. The adoption of systemic changes such as clinical terminology standards is inevitably a long and arduous process, which means we must ensure that new (or newly interested) individuals can learn, contribute and continue to carry the cause(s) forward.

2: Evolve with technology partners

Over the shoulder view of two people looking at a laptop while one points at a list of data on the screen.The way I describe the PMS system is how most of them currently work. They’re primarily acting as a well-organized electronic file folder. Electronic record keeping is immensely valuable, but to reach the next level of how we use pet medical data, we must evolve. Some of the newer generations of PMS systems have included features like “clinical entity recognition” — instead of trying to change how veterinary professionals interact with the PMS, they use machine learning to recognize parts of the medical record that could relate to clinically useful data like clinical signs, diagnosis, or recommended treatments. These types of innovative platforms work alongside veterinary professionals instead of insisting that they change the ways they work. Now, because PMS manufacturers also have a vested interest in collecting high-quality data, we’ll likely continue to see the evolution of the major platforms, and/or competitive pressure from more agile platforms.

Practices with leadership who understand not just what they want to achieve with data, but also have an appreciation for the larger data landscape, will make better decisions about which PMS they support, how up to date their PMS system is (one of the advantages of cloud-based systems) and influence how PMS companies will steer their future development efforts.

3: Set BHAGS — big hairy audacious goals

A doctor in scrubs looking confident with their arms crossed in front of them.Big hairy audacious goals. Because they often rely on fundamental changes in infrastructure and process, strategic data projects are slooooow. Without powerful, emotive goals, the best-intentioned initiatives can run out of steam. Recent analysis by Drs. Salois and Golab at AVMA did an excellent job of using data to establish the current state of the veterinary workforce crisis—could this be a spur for establishing better data standards for workforce analytics? Is it pet obesity? The advance of tick-borne disease as a sentinel for human infections?? We need to establish, champion, and fund big ideas that will force change.

I’ve got a couple of thoughts, how about you?

Where does the road lead from here?

The application of “big data” principles in animal health is the veterinary industry’s next big challenge, and we all must be part of the solution. In terms of the impact on pet health outcomes, it’s as potentially big a game-changer as vaccinations or parasite preventives were.

Those were last century’s challenges. This is ours, right now.

Massive change never comes easily, no matter the clear potential benefits. The ability to collate and analyze data on a national or global scale is a big lift, and I am not suggesting otherwise. That doesn’t make it an unworthy or unreachable goal. You can be another voice and another pair of hands to uplift the role of data and the importance of how we collect and use data.

At Nationwide, we’re already excited about the data collaborations we’ve started with organizations like VetSuccess. We’re looking forward to leveraging our data to improve pet health outcomes and to provide care for more pets.

Follow me on LinkedIn to keep track of what we’re up to or drop me a note!

*Some of the good news is that, as a practice, there are some talented professional data stewards that can help you out, especially when it comes to practice performance data. While it’s still important for the practice to maintain data quality and consistency, the experts at VetSuccess do a lot of the behind-the-scenes data heavy-lifting to appropriately catalog and classify practice data so that you not only get to see the trends within your own practice but also how they compare to other practices. They’re nice folks and they provide valuable data insights to the industry, to boot!

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