The next operational lessons for early-stage clinical trials in humans might not come from pharma but from a cattle ranch in Nebraska.
While human clinical research debates decentralized trials, adaptive protocols, novel technologies, and new approaches to oversight, the veterinary health industry has spent decades working through a similar set of challenges human early-stage trials are now running into with increasing frequency:
- data capture outside controlled clinic settings
- contributors who are not career clinical research professionals
- high variability across locations, and
- frequent mid-study changes that need to be absorbed without derailing the trial.
These constraints forced veterinary trials to design for variability, non-experts, and constant change decades before human trials had to.
That does not make animal health a perfect blueprint for human trials. The regulatory frameworks differ, and many aspects of modern human research are genuinely new. Still, the comparison offers a useful lesson on what good trial design and data capture looks like when conditions are variable and study design is expected to evolve.
This article explores what human drug development can borrow from animal health trials as a practical operating model, especially as clinical studies increasingly deal with more complexity, more distributed data, and more frequent iteration.
1. Messy settings force better data discipline
A large share of animal health studies take place outside controlled environments. Data is captured in homes, kennels, farms, barns, and fields, where variability is unavoidable. Data entry involves a wider mix of contributors: veterinarians, technicians, farm managers, and owners, many of whom are not clinical research professionals. Those conditions leave little margin for fragile workflows or ambiguous instructions which show up immediately as missing, delayed, or inconsistent data.
Human trials are moving in a similar direction. One registry-based analysis of neurology trials on ClinicalTrials.gov found that the relative frequency of trials using digital health technologies rose from 0.7% in 2010 to 11.4% in 2020. (nature.com) While this is not a universal measure, it is a clear signal that more studies are incorporating tools and workflows that generate data outside traditional site visits and clinical staff entry. (nature.com)
This highlights a fundamental challenge for an increasing number of human trials: when conditions are messy and contributors vary, data quality is largely determined at the moment of entry. Many legacy electronic data capture (EDC) workflows were built around review after the fact: trained coordinators entering data, stable visit structures, and monitors catching issues later.
While that approach works in controlled settings it tends to push complexity onto the people recording the data, increasing variability and rework. Animal health workflows do the opposite: they reduce decisions at the point of entry and make the next required action obvious. That design choice is not cosmetic; it’s one of the most practical ways to prevent avoidable issues before it reaches monitoring and cleaning.
In short, human trials data entry is designed for the monitors in veterinary trials for the data creators – a better way to ensure that clean, complete data is collected even in “messy” environments.
Why legacy EDCs struggle with the new baseline
It is tempting to blame this friction on execution, but in many cases it is structural. Legacy EDCs were built for the older operating reality: of controlled sites, specialist contributors, and stable protocols.
Those design assumptions shaped the systems. They optimized for heavy up-front build, centralized control, and review after the fact. As trials become more distributed and iterative, the same choices create predictable pain: workflows that are too complex for occasional contributors, small changes that ripple across schedules and logic, and updates that require broad re-validation and coordinated releases. This is not a question of competence but what happens when the baseline shifts and the underlying model remains tied to stability.
2. Protocol changes are the norm, not the exception, in animal health research
Animal health trials often evolve mid-study due to emerging safety or efficacy signals, practical constraints in the field, and regulatory or claims-driven adjustments. In this environment, clinical trial teams expect change, and systems are built to support iteration rather than treating it as an exception.
Human early-stage trials increasingly experience the same reality of mid-study changes. The critical operational question is not whether mid-study changes will happen but whether the trial infrastructure can absorb them without turning every update a long, expensive emergency. Often human trials struggle not because of regulation, but because their systems assume a stability that is no longer the reality in the clinic.
3. Scale looks different, and the data model has to follow
Animal health trials often have fewer sites, but each site, e.g. a farm, can have hundreds or even thousands of animals and can generate far more observations and data points than many human clinical sites. That reality pushes workflows toward group-based entry and longitudinal tracking that does not fit neatly into the classic “one patient, one visit” structure.
Human trials are increasingly running into similar patterns. Participants remain individuals, but the rise of real-world evidence, wearables, and population-scale programs is increasing data volume and cadence in ways that strain traditional, visit-centric EDC assumptions. Not every study needs a new data model, but trial teams will benefit from systems that fit that new reality.
4. Lean teams drive operational creativity
Animal health research groups are often lean, even inside large organizations. Heavy process overhead is hard to sustain, and fragile systems tend to fail quickly in the field. The result has been simpler workflows, faster study builds, and pragmatic tradeoffs that still preserve compliance.
This is now a common reality in early-phase human trials and in medical device clinical research: small teams coordinating multiple vendors, limited time for rework, and a strong need to keep sites and internal stakeholders aligned.
The lesson learned from animal studies is not ‘move fast and break things.’ It is closer to moving steadily and avoiding unnecessary rework.
5. Evidence longevity is a systems problem
In animal health, “trial complete” is often not the end of the story. Sponsors may need to reuse historical data across products, defend claims, and respond to regulatory or commercial questions years after a study ends. That long time horizon forces a different mindset: evidence needs to be maintained with clear lineage and provenance, not simply collected and archived.
That long time horizon also pushes teams to look beyond the boundaries of a single protocol. When questions extend across studies and time, the strength of the evidence depends on the larger system around the data: change control and governance, vendor handoffs, how reviews happen, and decisions are documented so the study can be reconstructed later.
Human drug development is moving in the same direction. Regulators, payers, and internal governance groups increasingly ask longitudinal questions about consistency across cohorts, durability of effect, subgroup behavior, and long-term safety signals. As trials become more distributed, confidence in the evidence relies less on any single dataset and more on traceability across the full system, including clear versioning and an audit-ready record of electronic source (eSource) data and downstream transformations.
Insights Human Drug Development Should Adopt
Animal health trials rarely get credit for the operational maturity they have built under severe constraints. Now that human drug development is converging on some of the same design requirements, clinical teams can learn a few lessons from their colleagues over on the veterinary side:
- Assume change is constant. Build systems and processes that treat protocol amendments as routine, not exceptional.
- Design for non-experts, like caregivers or patients. Complexity at the backend; simplicity at the point of entry.
- Treat evidence as a long-term asset. The value of your trial data doesn’t end at database lock. Invest in traceability and provenance now.
Are You Ready for Trials That Change Mid-Flight?
There are three questions for clinical operations leaders:
- Could your current EDC handle a protocol change in days, not weeks without downtime?
- Could a non-specialist contributor enter data correctly on their first attempt?
- Can you query evidence from three years ago with full provenance and an audit trail?
If the answer to any of these is “no,” your infrastructure may be designed for a trial environment that no longer reflects the reality of early-stage human trials.
The future isn’t just faster trials but increased confidence in the evidence those trials produce, even years later. Animal health had to figure that out a while ago, human drug development has the opportunity to learn from it now.
About the Author:
Tommy Buchanan Jackson
Tommy Buchanan Jackson is an experienced CEO and operator who has spent the past seven years leading Prelude’s transformation from a niche EDC provider into a rapidly scaling innovator in clinical technology. With a background spanning private equity, digital strategy, and product leadership, he brings a rare blend of analytical rigor and entrepreneurial vision. Tommy’s career reflects a consistent focus on building high-performing teams and scaling organizations through pivotal growth stages.
