A community oncology research program shares exactly how they built an evidence-based staffing case and got it approved.
ADAM TORRES | Senior Director of Research Highlands Oncology | TRIALNAV OASIS™ Clinical Research Operations | Workforce Planning
For years, asking for more staff in clinical research felt like a ritual exercise in optimism. You would compile a list of everything your team was drowning in, walk into a budget meeting, and hope that the people holding the purse strings could somehow feel the weight of what you were describing. Sometimes it worked. More often, it did not, not because leadership did not care, but because telling them you are overwhelmed is not a financial argument.
This year, we tried something different. And we got 11 positions approved.
Here is exactly how we did it.
The Problem We Were Trying to Solve
Highlands Oncology is a high-volume community oncology research program managing over 140 active clinical trials. Our team of clinical research coordinators is, by any honest measure, stretched thin. We had known for a while that our staffing model was not keeping pace with our trial volume. Telling leadership we need more people does not get budgets approved. Showing them the revenue impact of not having enough people does.
We needed to stop making staffing requests and start making a business case.
The Methodology: Three Steps
- Establish a Revenue Baseline I took our 2025 gross research revenue and divided it by the number of full-time CRCs on staff. That gave me a single, clear number: gross revenue per CRC. This transforms an abstract workforce question into a financial one. What does each coordinator produce, and what does it cost us when we do not have enough of them? Once you have revenue per CRC, you have a denominator. Everything else builds from there.
- Predict Next Year’s Study Volume Asking for staff based on current workload is a losing argument because current workload is already too heavy by the time you make the request. The stronger move is to forecast forward. Using three inputs, including current consents per trial, three years of historical study starts from 2022 through 2024, and our current study type breakdown by percentage, I projected a total new study count for 2026 and applied our portfolio mix to get a complexity-weighted forecast by trial category. A Phase I first-in-human study and a simple biomarker collection study do not consume the same coordinator capacity. Any staffing model that treats them as equivalent is already wrong before it starts.
- Let TRIALNAV OASIS™ Run the Numbers I entered our predicted 2026 study counts and average consents per trial into TRIALNAV OASISTM, then ran the model twice: once with a conservative enrollment assumption and once with an optimistic one, generating a low and high estimate for projected revenue and required FTE count. Two things stood out. First, the spread between the manual and OASISTM revenue estimates was surprisingly narrow at roughly $100,000, which told us we were not working with inflated projections. The model was grounding us in a defensible range. Second, and more importantly, the FTE count the platform predicted matched almost exactly what I had independently calculated using our manual system.
When your data-driven platform and your own manual analysis independently arrive at the same number, you are no longer making a case. You are presenting evidence.
Years of Expertise. One Hour to Run It.
There is one more detail worth naming here, because it says something important about where research operations is heading.
The manual methodology I used to validate the platform output, including the revenue baseline, the historical trend analysis, and the study type breakdown, took years to piece together. Years of trial and error, institutional knowledge accumulated visit by visit and study by study, a system built and refined over time that lived largely in my own head and a collection of spreadsheets.
TRIALNAV OASIS™ ran the equivalent analysis in under an hour.
That is not a knock on the manual process. That accumulated human expertise is precisely what made the platform output trustworthy. I knew what the numbers should look like, which meant I could recognize immediately when the model got it right. The technology did not replace the judgment. It gave the judgment somewhere to go.
This is what genuine technology-human collaboration looks like: years of curated expertise informing a platform that can execute in minutes, with a practitioner who understands the output well enough to stand behind it in a room full of skeptical decision-makers. Neither half works as well without the other.
What We Walked Into the Budget Meeting With
We did not walk in with a list of complaints or a vague appeal to how busy we were. We walked in with:
● A revenue per CRC baseline grounded in actual 2025 financials.
● A three-year historical growth trend showing where study volume was heading.
● A complexity-weighted 2026 portfolio forecast broken down by study type.
● A low and high FTE projection with clearly stated enrollment assumptions.
● Independent validation confirming that the platform output matched our manual calculation.
The conclusion was straightforward. At our projected 2026 volume, the data supported a meaningful expansion of our CRC team, plus a larger cohort of support staff to protect coordinator time for patient-facing work and prevent the administrative creep that drives burnout.
The request was for 11 positions in total. It was approved.
What Made the Difference
The approval did not come because we asked louder or had better slides. It came because we changed the nature of the conversation entirely.
Leadership did not have to take our word for how busy we were. They could see the math. The revenue model showed what each coordinator generates. The forecast showed where we were heading. The platform validation showed that two independent methods arrived at the same answer.
That is the difference between a staffing request and a staffing recommendation. One asks leadership to trust your instincts. The other asks them to respond to evidence.
Research administrators have known for years what their teams need. The gap has never been insight. It has been the language to translate operational reality into financial justification. TRIALNAV OASIS™ closed that gap for us.
The Part We Are Still Waiting On
I will be honest. I am writing this with cautious optimism, not a completed victory lap. The positions were approved in the budget cycle, but hiring takes time, onboarding takes longer, and the trials will keep coming in whether the seats are filled or not.
Fingers crossed that we can get the right people in place before the 2026 activation wave lands.
But the process worked. The methodology is sound. And for any research program that has been struggling to make the staffing case to leadership, one that feels intuitive to the people doing the work but invisible to the people approving the budget, this approach is reproducible.
You do not need a perfect dataset. You need a defensible baseline, a principled forecast, and a platform that can translate both into language that budget committees understand.
Want to Use This Methodology?
The framework we used is built into TRIALNAV OASIS™. If your program is navigating a similar conversation, trying to translate research complexity and trial volume into an FTE justification that finance leadership will approve, reach out. We are happy to share more about how we structured the model and what data inputs matter most.
Because if there is one thing this process taught us, it is that the data was always there. We just needed the right tool to turn it into an argument that got people approved.
About Highlands Oncology:
Highlands Oncology is an independent, physician-owned cancer center serving Northwest Arkansas and the tri-state region. Through six locations, Highlands provides comprehensive oncology services including medical, radiation, and surgical oncology, clinical research, imaging, infusion services, rehabilitation, a pharmacy, and a host of supportive care programs. Highlands Oncology remains dedicated to delivering innovative care with precision and compassion.
