What Abstracts Taught Me About Evidence, Access, and Impact
- Catherine Del Vecchio Fitz
- Dec 15, 2025
- 4 min read
How asking “when does this reach patients?” revealed the real path to impact
TL;DR
When I launched Savvyn Insights, I was focused on building an AI-first way of working—one that reduced wasted effort, protected deep thinking, and improved how work actually gets done. That instinct hasn’t changed. What has changed is clarity around where AI actually creates impact in healthcare. Over the past year, working through real scientific workflows—abstracts included—became an unexpected lens into a deeper problem: fragmented evidence creates uncertainty, uncertainty delays access, and without access there is no impact. This post looks back at how that realization emerged—and how it shaped the system Savvyn is building today.
Looking Back
As 2025 comes to a close, it feels like the right moment to pause—not because anything ended, but because the work has come into focus.
When Savvyn started, I didn’t have a platform or a system diagram. I had an instinct shaped by years inside complex organizations: systems were fragmented, work was largely manual, and progress depended on people constantly stitching things together. Effort went into reformatting and reconciliation instead of advancing the work. It all felt like it was taking far more time than it should have—and most of that time was being burned on avoidable work. It was maddening.
What made it harder to ignore was that, for the first time, there was a credible alternative within reach. AI wasn’t just generating text or speeding up small tasks—it was finally good enough to handle the kind of structured, repeatable work that had been quietly draining time and attention for years.
At first it was helpful to frame this as an efficiency problem—and, candidly, I went on a bit of an AI rampage 😅. Not because AI was the goal, but because it exposed how much friction had been normalized. If work could be structured once and reused, if rules could be encoded instead of rechecked, if information could move without losing meaning, then there was clearly a better way of working—and for the first time, it felt achievable.
What I didn’t yet have was precision around where clarity mattered most.
The Problem Was the Workflow
Early Savvyn was never really about documents or data in isolation. It was about workflows that quietly broke down once work had to move from one step, team, or decision-maker to the next.
Teams had strong ideas. They had data. They had capable people. What they didn’t have was a clean way to move information through the system without constant reformatting, reinterpretation, and re-explanation. Spreadsheets were rebuilt. Analyses lived in personal folders. “Final” decks were endlessly revised. Work kept changing shape—and losing context—along the way.
Abstracts were simply where this dysfunction became impossible to ignore. They force compression under pressure. Claims have to be stated. Gaps surface quickly. And whatever ambiguity exists upstream tends to show up all at once, right when stakes are high.
That made them a useful lens—but not the whole story.
Abstracts as a Lens, Not an Endpoint
The Savvyn Abstract Builder was never about automating writing for its own sake. It was built to answer a more fundamental question: if the right inputs were structured clearly, could the output hold up beyond the moment it was created?
Early iterations showed that messy notes could become structured drafts, tone could be stabilized, and rules could be enforced automatically. Useful, yes—but not the real insight.
Working through them made it impossible to ignore the larger question: how does this ever translate into real-world impact?
An abstract isn’t an endpoint. It’s an early signal. To matter, it has to connect to evidence that holds up, to decisions that enable use, and ultimately to outcomes that reach patients. Once you ask that question seriously, the rest of the pathway comes into view.
The Pathway That Came Into View
Over time, it became clear that what we were running into wasn’t just a document problem—it was a pathway that shows up across healthcare and beyond.
It starts with Innovation: a new idea, product, or approach that genuinely advances the field. This is where creativity and discovery live, and where momentum usually feels strongest.
That innovation has to become Evidence—work that is structured, interpretable, and credible beyond the people who created it. Not just data, but evidence that can travel: across teams, across reviewers, across decision-makers.
Evidence then meets Coverage: formal expectations from payers, regulators, leadership, or budget owners. This is where questions harden and ambiguity becomes consequential.
Coverage determines Access: whether something can actually be ordered, reimbursed, deployed, or operationalized in the real world.
And only then do we reach Impact—when the work changes outcomes at scale.
Abstracts made this pathway visible because they sit early in it. They surface whether the evidence is ready to move forward—or whether it will stall long before it reaches the people it’s meant to help.
From AI-First Thinking to Evidence Intelligence
This is where Savvyn came into focus.
What began as an AI-first mindset—automating the grind and protecting time for judgment—evolved into something more specific: the need for systems that bring structure and continuity to evidence as it moves toward impact.
Today, Savvyn is being built as an AI-powered evidence intelligence platform. Docs, Data, and Dash are simply different surfaces of the same goal: making evidence legible, aligned, and decision-ready so it can move forward with less friction and more confidence.
Why This Matters — and What Comes Next
In healthcare, when evidence is fragmented or misaligned, progress slows. Decisions take longer, access becomes harder, and even strong science struggles to move forward. This isn’t a failure of ideas or intent—it’s a failure of translation.
We started with AI because it exposed inefficiency. We stayed because the work revealed something deeper: evidence only creates impact if it can move—through coverage, into access, and ultimately to patients. Abstracts happened to be the lens that made that impossible to ignore.
Savvyn is being built to help good ideas—especially good science—move predictably toward the people they’re meant to serve.
The work ahead is substantial.
And that’s where we’re headed 🙏.
Thanks for reading,
—Savvyn (your partner in ruthless efficiency)
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From Chaos to Clarity. Amplify Your Impact.
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