Season 2 Episode 3 Part 1: AI for Pharma - Faster Answers, Better Decision

Andie Cartwright

Welcome to the Better Living Through Data podcast! I'm Andie, and with me as always is Anthony. We've had an incredible first few weeks with the launch of Study Buddy. Early access Academic researcher users are seeing the real benefit of using Study Buddy to answer their research questions. That being said, we can't forget about life sciences as a whole. Anthony, let's chat about the Pharmaceutical industry. We see teams who want to solve a specific problem: they already have the question, already have the budget, already have internal pressure to move -- and then they hit the wall of, “Can the data actually answer this?”

Anthony Pero

Yeah, that’s the reality. In pharma, this usually is not, “Help me come up with a research idea.” It’s, “We have a hypothesis right now. We need to know if it’s feasible before we sink real time and money into a full study.” That distinction matters because the urgency is different, the funding model is different, and honestly the tolerance for wasted motion is a lot lower.

Andie Cartwright

That phrase “before we sink real time and money”; that’s the thing, right? Because this tool is not just for academic grant justification. So if a pharma team wants to answer their own research questions, they can do so quickly and efficiently with Study Buddy.

Anthony Pero

Exactly. Pharma teams are trying to pressure-test a direction early. Can this dataset support the question? Is this worth pursuing? We are here to tell you that our AI-powered natural language research tool helps answer these questions based in the world’s largest long-term and post-acute care dataset. Pharma companies can get to an informed yes, no, or not yet much faster.

Andie Cartwright

“Yes, no, or not yet” is a great triad. Because “not yet” is also valuable! In marketing, I always think about what job the message is hired to do. For pharma, the job is not admiration. It’s not even novelty. It’s confidence, quickly.

Anthony Pero

Right -- confidence, but not false confidence. That’s the tension. Speed matters because nobody wants to wait around for extracts or custom analysis just to learn the question was off. But confidence matters because moving fast in the wrong direction is expensive. So the value here is earlier insight generation. Shorten the path from question to signal.

Andie Cartwright

Let me try to say that back, and you tell me if I’ve got it wrong. This is not “AI makes research magical.” It’s more like, “AI enables you to answer questions sooner of whether or not your research direction has legs in long-term care data.”

Anthony Pero

That’s much closer. And I’d sharpen one word: exclusive long-term care data. Because the pharma use case really comes alive when you realize they’re not just looking for answers fast -- they’re looking for answers in a part of care delivery that is incredibly hard to navigate. Long-term and post-acute care can be messy, fragmented, difficult to query. If you can interact with that using natural language, earlier in the process, that changes the economics of the decision.

Andie Cartwright

“Changes the economics of the decision” -- that’ll stick with me. Because that’s more executive than technical. You’re not promising the finished study in five minutes. You’re helping teams avoid investing in questions the data can’t support.

Anthony Pero

Exactly. And for pharma, that’s the real AI story. Not mechanics. Not feature lists. AI as the vehicle to unlock earlier feasibility, earlier signal, and faster insight from a dataset they can’t easily work through on their own.

Andie Cartwright

Which is why leading with the name alone doesn’t do enough. “Study Buddy” without context sounds friendly. “AI-powered access to the largest long-term care dataset” sounds consequential.

Anthony Pero

Friendly is nice. Consequential gets attention. And attention gets therapies into the hands of those who need the right therapy at the right time.

Andie Cartwright

Okay, so let’s push on the part I think people are most likely to blur together. There’s a lot of AI noise right now. If somebody says, “Can’t I just ask a general AI tool to do this?” -- what’s the cleanest answer?

Anthony Pero

No, because the differentiator is the DATA. Specifically, proprietary PointClickCare Life Sciences data. Not public web content, not generic training data, not projected data, and not something you can replicate by “putting it into the internet.” The value is AI plus exclusive long-term and post-acute care data. If you remove the actual data; you remove the edge.

Andie Cartwright

“Putting it into the internet” is such a useful phrase because it gets at the misconception. People hear AI and think chatbot. But here, the thing that matters is the underlying source material -- proprietary PointClickCare Life Sciences data that is not publicly available.

Anthony Pero

That’s it. And in pharma, exclusivity is not a side note. It’s the non-negotiable message. If you can’t get these insights anywhere else, then AI becomes a serious strategic advantage instead of a shiny interface. That’s why I always come back to the framing: harness AI to explore real-world evidence you can’t find anywhere else.

Andie Cartwright

And once you say it that way, the practical payoff gets a lot clearer. You can pressure-test hypotheses. You can explore feasibility before committing to a full study. You can ask questions in natural language instead of waiting for a specialist queue or writing code you don’t write every day.

Anthony Pero

Right -- no coding, no dependency on a data scientist just to start exploring, no technical barrier between the researcher and the first layer of understanding. That accessibility matters because bottlenecks distort decision-making. If every question requires an extract request and a long wait, people ask fewer questions. Or worse, they advance the wrong ones because they don’t have early feedback.

Andie Cartwright

“Ask fewer questions” is the part I think people underestimate. When access is hard, curiosity gets expensive. And expensive curiosity means teams may skip the exact feasibility check that would’ve saved them time later.

Anthony Pero

Yes. And that’s where this becomes more than convenience. It’s about focusing investment on the questions worth pursuing. The best outcome isn’t just that you move faster. It’s that you move faster toward the right opportunities, and away from dead ends, with more confidence.

Andie Cartwright

So the dream is not “more dashboards.” The dream is fewer expensive detours.

Anthony Pero

Exactly. Fewer expensive detours, fewer waits for custom analysis, more immediate exploration. Less time asking, “Can someone pull this for me?” More time asking, “Is this direction strong enough to warrant the next investment?”

Andie Cartwright

And that feels like the mindset shift for pharma in one line: not more fascination with AI, more discipline about which questions deserve to move forward.

Anthony Pero

Yeah... and once you start there, the next conversation gets really interesting. Because then the question isn’t whether AI belongs in the workflow. It’s where that earlier signal should change decisions first.

Andie Cartwright

You got that right! I think this is a great place to pause for today. But this discussion isn't nearly over! We will continue with part 2 of this conversation next week. One thing I wanted to mention before we end today's episode; we are currently offering free access to Study Buddy! Head over to the description for today's episode to learn more and register for free access!

Anthony Pero

Yes Andie, and on that note, we will continue with part two of today's episode next Thursday!

Andie Cartwright

Thanks for listening in! We'll see you next week!

Anthony Pero

Bye everyone!