Jin Kim is the founder and CEO of Miracle, a company helping biotech teams accelerate clinical trials by automating operational insights. Trials are a data problem and Miracle solves it by unifying siloed systems into one real-time dashboard. That has helped some clients finish trials 3–4 months ahead of schedule.
Many trial operations still rely heavily on Excel. It’s easy, familiar, and free. But once you enter Phase 2 or 3 with CROs, labs, and recruitment vendors in play, things break down fast. Different systems name fields inconsistently (think “data randomization” vs. “randomized data”), and humans can understand that but systems can’t. That’s where the manual labor comes in—pulling data from multiple platforms, cleaning it up, and trying to build reports.
Miracle automates all of that. The system integrates with common clinical tools via API or data exports, normalizes disparate formats, and delivers insights in real time. That means no more waiting for a report on Wednesday to catch an issue from last Friday. Users can respond as problems arise.
We talked about the hype around AI, especially at recent industry conferences like AWS Life Sciences. Jin’s take is that AI is only as good as the data it has access to. And in clinical trials, that data is usually siloed or messy. So before deploying AI, companies need a solid data infrastructure. That’s what Miracle provides: a clean, unified layer that can feed downstream AI use cases.
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Jin stressed that he doesn’t lead with AI in conversations—he starts with the problem. What do clinical operations teams need today? Miracle's customers are typically not engineers. They're researchers, clinicians, and trial managers. That’s why the platform is tailored to non-technical users, with role-specific dashboards and workflows for everyone from the CEO to a clinical operations lead checking in eight times a day. Jin has made sure the tool meets people where they are, surfacing the right metrics based on their goals—whether that’s daily site activity or long-term enrollment projections.
I think it's so important to get down into how is the director of clinical operations using it versus the actual people on the clinical operations team versus even, you know, calling it leadership, how the chief medical officer's using it might be different from how the CEO's using it.
If you’re running a biotech trial and wondering whether you have a data problem, Jin suggests starting with a simple question: “Are we going to finish our study on time?” If that answer isn’t crystal clear, you’ve got work to do. And for most teams, enrollment is the biggest risk factor. Miracle helps teams back-calculate from their timelines using data they already have: how many patients are being screened, how many pass, how many are randomized, and how many drop out.
While Miracle doesn’t handle patient recruitment directly, it can track the entire recruitment funnel from ad spend on Facebook or Google, through study website visits, to completed screenings. That makes it easier to assess the ROI of digital outreach and reallocate spend based on what’s actually converting.
Jin started Miracle while still in grad school, building on his experience in enterprise sales and his background in computer science from MIT. He saw firsthand how data bottlenecks crippled big pharma, and he realized that smaller, resource-strapped biotechs needed a better way.
It just occurred to me as I write this, weeks after I first met Jin, that some companies might run out of money in the middle of a trial, which seems a tragedy for the participants, regardless of whether a product was headed for approval or not. In any case, helping more trials get across the finish line is a worthy cause. Whatever any of us in life science can do to help that happen is a good thing.
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