Skip to main content
Axial Podcast

Analysis of Clinical Trials with Spencer Hey

68 min episode · 3 min read
·
Spencer Hey

Episode

68 min

Read time

3 min

Topics

Productivity, Startups, Fundraising & VC

AI-Generated Summary

Key Takeaways

  • Metascience as a product category: Prism operates at the level above raw experimental data, analyzing sequences of hypotheses, trial outcomes, and research decisions across entire domains. In one published project with Genentech, the team used LLMs to extract central hypotheses from all neurodegenerative disease trials in PubMed, clustered them, and mapped neglected areas — a replicable framework for identifying white space in any therapeutic area.
  • Turnaround compression as a core metric: Prism measures its value by the time elapsed between a strategic question and a stakeholder-ready report. For one major pharma client's biomedical knowledge team, that window dropped from several weeks to roughly one week, then to days. The near-term product goal is a single natural-language prompt that triggers automated data retrieval, analysis, and formatted report generation within hours.
  • Interactive reports over static PowerPoints: Biopharma decisions frequently rest on static slide charts where underlying assumptions cannot be tested. Prism builds web-based, interactive visualizations using the Vega library, allowing decision-makers to adjust parameters and surface thousands of embedded sub-questions from a single report — shifting the baseline expectation from a fixed narrative to an explorable data artifact.
  • Three-pillar product architecture: Prism's platform rests on data visualization (Vega-based custom charts), data curation (ontology-explicit, provenance-tracked datasets), and generative insight (LLM agents that select chart types, format data, and execute multi-step research workflows). The third pillar is the current development focus, with the goal of configuring autonomous agent pipelines that execute full research plans from a single high-level instruction.
  • Epistemic humility as a sales positioning tool: Prism targets strategists inside pharma who already possess data but lack the infrastructure to communicate it persuasively to decision-makers. The product is not positioned as a replacement for domain expertise but as a tool that grounds existing beliefs in traceable evidence — shifting internal conversations from opinion-based table-pounding to data-backed, auditable arguments that stakeholders can interrogate directly.

What It Covers

Spencer Hey, cofounder of Prism, a metascience software company, describes how his philosophy-of-science background led him to build data visualization and LLM-powered tools that help biopharma organizations extract strategic knowledge from clinical trial data, reducing analysis turnaround from several weeks to days, with a goal of reaching hours.

Key Questions Answered

  • Metascience as a product category: Prism operates at the level above raw experimental data, analyzing sequences of hypotheses, trial outcomes, and research decisions across entire domains. In one published project with Genentech, the team used LLMs to extract central hypotheses from all neurodegenerative disease trials in PubMed, clustered them, and mapped neglected areas — a replicable framework for identifying white space in any therapeutic area.
  • Turnaround compression as a core metric: Prism measures its value by the time elapsed between a strategic question and a stakeholder-ready report. For one major pharma client's biomedical knowledge team, that window dropped from several weeks to roughly one week, then to days. The near-term product goal is a single natural-language prompt that triggers automated data retrieval, analysis, and formatted report generation within hours.
  • Interactive reports over static PowerPoints: Biopharma decisions frequently rest on static slide charts where underlying assumptions cannot be tested. Prism builds web-based, interactive visualizations using the Vega library, allowing decision-makers to adjust parameters and surface thousands of embedded sub-questions from a single report — shifting the baseline expectation from a fixed narrative to an explorable data artifact.
  • Three-pillar product architecture: Prism's platform rests on data visualization (Vega-based custom charts), data curation (ontology-explicit, provenance-tracked datasets), and generative insight (LLM agents that select chart types, format data, and execute multi-step research workflows). The third pillar is the current development focus, with the goal of configuring autonomous agent pipelines that execute full research plans from a single high-level instruction.
  • Epistemic humility as a sales positioning tool: Prism targets strategists inside pharma who already possess data but lack the infrastructure to communicate it persuasively to decision-makers. The product is not positioned as a replacement for domain expertise but as a tool that grounds existing beliefs in traceable evidence — shifting internal conversations from opinion-based table-pounding to data-backed, auditable arguments that stakeholders can interrogate directly.
  • Startup survival framed as continuous learning: Hey attributes Prism's resilience to a principle from the BC SSC accelerator: startups fail when founders stop learning, not when they run out of money. Practically, this means weekly iteration, treating the product team as a high-performance racing unit requiring alignment and marginal gains, and using Prism's own tools internally as a live test of whether the product meets the standard it promises clients.

Notable Moment

At a tuberculosis trials consortium meeting, Hey observed that scientists debating conflicting moxifloxacin trial results — two positive, two negative — argued almost entirely from opinion rather than systematically laying out the evidence. That scene became the direct origin of Prism's core thesis: structured data mapping prevents costly, unlearnable decisions.

Know someone who'd find this useful?

Episode Transcript

Welcome to the Axial podcast. Axial is an early stage investment firm based in San Francisco. We partner with great founders and inventors investing in early stage life science companies often when they are no more than an idea. Axial is fanatical about helping the right venture who's compelled to build their own and journey business. Spencer, really appreciate you taking time to talk about Prism and your story. Maybe you can use a brief introduction, and we can just go from there. Sure. Well, thanks, Josh so much for, inviting me to come on and and and talk with you. I'm excited. So yeah. So my name, I'm Spencer Hay. I'm the cofounder and chief science officer, chief product officer of Prism. I don't know. I I'm you know, we're we're a startup, so, of course, you wear many hats. Yeah. You could be the visionary. You know? Yeah. Yeah. I like that. You know? I like that better. I'm the visionary of, because of my partner, Brendan Garen, he is the, like, financial mind. So, you know, it's a it's a really nice partnership, actually. Yeah. So let's see. That's me. What does Prism do? So we are, you know, I would love to say my hope actually is that one day, I can just say we are a metascience software company, and everyone will be like, oh, yeah. Of course. I know what that is. That's amazing. We're not yet there. I think so oftentimes, I will say, like, we are a data science company. We are helping life science organizations maximize the knowledge value of their data. But But what that basically means is, you know, we help, you know, pharma in particular is our kind of, like, lead client, although we do we do work with some academic clients as well. Like, you know, they're sitting on mountains of data. Like, we we sort of live in the golden age of data. Right? There's data everywhere. Everybody you know, there's so much of it. It's growing every day. Everybody knows they everybody knows that they need data. But, actually, like, what you want isn't really the data itself. What you care about is the knowledge. It's like the data contains answers. It contains insights. It's telling you things that you need to know. And that process of, like, how do I extract that from data? Like, that is hard work still. Right? You can have all the data in the world. Right? It's gonna take you time to analyze it, right, and make sense of it. And that's really the space where, you know, Prism is sort of focused. Right? It's just like, how do you make that time between I got my data. Right? Now I want to understand it. I want to extract what is valuable in it. I wanna, like, reduce that time to as little as possible. And so that's kinda where we focus our our software. Yeah. Then, like, I …

Get the full transcript (14,249 words) + summary by email — free

One-time email with the complete transcript and AI summary of this episode. No account needed.

One email, no spam. We’ll also show you what SignalCast does.

Browse all Axial Podcast transcripts →

You just read a 3-minute summary of a 65-minute episode.

Get Axial Podcast summarized like this every Monday — plus up to 2 more podcasts, free.

Pick Your Podcasts — Free

Keep Reading

Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links.

Tools

  • Prism builds web-based, interactive visualizations using the Vega library, allowing decision-makers to adjust parameters and surface thousands of embedded sub-questions from a single report

More from Axial Podcast

We summarize every new episode. Want them in your inbox?

Similar Episodes

Related episodes from other podcasts

Explore Related Topics

This podcast is featured in Best Biotech Podcasts (2026) — ranked and reviewed with AI summaries.

Read this week's Startups & Product Podcast Insights — cross-podcast analysis updated weekly.

You're clearly into Axial Podcast.

Every Monday, we deliver AI summaries of the latest episodes from Axial Podcast and 192+ other podcasts. Free for one show.

Start My Monday Digest

No credit card · Unsubscribe anytime