Pharma analytics · Enterprise SaaS · Freelance engagement converted from cold pitch
DRG Fusion is a real-world-data analytics platform for the pharmaceutical market. Delivered entirely in the browser, its tightly linked visualization modules transform vast clinical and claims datasets into clear, interactive stories, giving commercial teams the insight they need to shape evidence-based product and market strategies.
How it started
A former colleague put us in front of Clarivate with an opportunity to pitch. Three of us, all seasoned designers with deep healthcare experience, put our portfolios together, and I presented. We got the project.
It wasn’t the first time that formula had worked. The same three designers had originally come together to join Rune Labs the same way: a collective pitch, a shared track record, a convincing case. We knew how to work as a unit.
The situation
By the time we came in, DRG Fusion was close to launch, and leadership was worried. Commercial pitches weren’t landing. Product Managers were struggling to explain the dashboards to prospective clients, let alone make them compelling. The platform had been built engineering-first: data was presented as it came, shaped around whatever the graphing library could render. It looked and felt like a fifteen-year-old product.
The SVP & GM and the Senior Director of Product were both frustrated. What the platform was missing wasn’t more data. It was a point of view about what the data meant.
The approach
This was a four-month freelance engagement running in parallel with my full-time role at Rune Labs, something I took on deliberately, because the problem was too interesting to pass up. I led the pitch, helped win the project, and owned the client relationship, working on the design alongside two other senior designers.
The core reframe was simple: stop designing dashboards and start designing arguments. Every chart in the platform existed to help a pharma commercial team make a case to a colleague, to a client, to a regulator. If a chart couldn’t be explained in one sentence, it wasn’t doing its job.
That lens drove every decision.
The work
Besides redesigning the landing screens and overall navigation shell, the real challenge lay in the immense work of interpreting data and turning it into an insightful interface for the commercial teams using it to build portfolios. Below are but a few examples of the many visualizations that I redesigned, both visually and functionally.
The blood pressure chart
The original used two separate charts to show systolic and diastolic pressure, already a cognitive split, and hid most of the data behind mouse-overs. Seeing the min and max values for any single age group required hunting.
The redesign collapsed it into a single box plot chart, with key data surfaced in a table alongside. No hovering required. Interpretation time dropped dramatically.
The BMI chart
The original was a stacked bar where each column was a BMI range and each stack an age group. To understand BMI distribution for any given age group, you had to do mental arithmetic. To see distribution across the whole population, it was essentially impossible.
We flipped the axes: each column became an age group, each stack a BMI range. A single “all” bar gave population-wide context at a glance. Hovers let users isolate and compare across age groups without losing the overall picture.
The treatment progression chart
This one required reverse-engineering what the original was even trying to say. The numbers across the top were a funnel, each a subset of the previous, but nothing in the design communicated that. The final number, “naive patients,” corresponded to the leftmost bar, labelled “treatment naive” which was a different label for the same thing.
We redesigned the funnel as a single bar, each slice representing a portion of total confirmed diagnosed patients. Color coding was unified across the funnel and the progression chart that followed it, so the relationship between the two was immediately legible. All numbers were displayed explicitly, making the funneling logic self-evident.
The outcome
“I’m finally seeing what good product design looks like, this has meaningfully improved how my team leads customer pitches”
Jeffrey Wray, Senior Director of Product
The impact the work had on our stakeholders was the measurable outcome at the time we delivered. It meant the work had done something beyond fixing charts, it had shifted how leadership thought about design’s role in their product portfolio, which had mostly been engineering-led.
What I took from it
Working with complex data analytics taught me something I’ve carried into every project since: always use real data. Placeholder graphs in a healthcare analytics product don’t just look wrong, they raise doubts, derail conversations, and undermine trust in the design before it’s even been evaluated. If you’re designing something that will live or die on how it represents numbers, you have to do the maths.
The deeper lesson was about what data visualisation is actually for. It’s not a display problem. It’s a communication problem. The question isn’t “how do we show this data?” It’s “what does this data need to say, and to whom?”
“We have many other products that need this kind of attention.”
Abeezer Tapia, SVP & General Manager