. He can be reached at nsaraceno@mjhlifesciences.com.

Nicholas Saraceno, Editor

Pharmaceutical Commerce: Deloitte cites that about one-third of drug launches miss expectations. What concrete metrics or case examples show how new decision-support tools can improve launch performance compared to traditional approaches?

Many launches fall short because launch teams lack a complete view of the market. Before any drug or therapy reaches launch, you need to understand who will prescribe it, which patients need treatment, where they’re located, and how access will be covered. Traditional market research can provide these insights, but the information is often weeks or months out of date.

Real-time data and AI reveals critical gaps much earlier. In one launch, physicians reported that patients with comorbid mental health conditions were frequently delayed in receiving GI diagnoses. When those findings were combined with congress monitoring and field insights, the company quickly identified an overlooked HCP population, broadened education, and improved alignment across functions. The result was faster diagnosis, stronger engagement, and better patient access.

This validates how modern decision support transforms outdated intelligence into timely, actionable strategy.

PC: When integrating inputs such as field activity, HCP engagement, claims, and social sentiment, how can companies ensure the data is normalized, weighted appropriately, and not skewed by noisy or biased sources?

You want to be working with a partner who specializes in that kind of work. It takes years of experience working with each of those data sets individually to know what really matters, and how it should be weighted in different circumstances, particularly when you’re combining different data sets. At Within3, we've spent years developing our algorithms and data integration approaches to be able to do this effectively. We also have years of client feedback that tells us what we need to adjust to get to a point where the precision level is high enough that launch teams can reliably make good decisions.

Each of these data sets is different in its own right. When looking at global social listening data, for example, it really is a needle in a haystack. It's about managing that noise to understand what matters versus what's irrelevant.

Real-time insights drive faster, more targeted launches.

Data precision depends on expertise and transparency.

Compliance and trust remain central to AI adoption.

PC: What types of independent validation or audits are important to confirm the accuracy and reproducibility of AI-driven insights in the context of product launches? How transparent should the outputs be for commercial, medical, and compliance teams?

It matters what you're using AI for, because certain use cases require different levels of accuracy than others. For example, analyzing markets for launch intelligence requires that we always ensure we provide traceability back to the underlying data. It’s like how in the publications world you have citations that bring you all the way back to the source content. We do the same thing back to the underlying data elements that tell us why we're seeing a signal that suggests you could be doing something different in any particular aspect of the market.

If an organization is as serious about responsible AI as we are, it should be able to publish material that tells people exactly what it's doing—it's not smoke and mirrors. You need a transparent AI governance structure and a regulatory compliance framework that shows how you intend to comply with relevant local, national, and international regulations. You need clear processes for incident management, both how incidents are reported, and the steps for resolution. And you need ongoing training and education to keep critical staff informed on AI best practices.

When building an AI system, you're combining human expertise and analysis with quality assurance processes, data science processes, ever-improving algorithms, and ever-cleaner data. Those things come together so that six months from now, the system is going to be vastly more agile and accurate than it was today.

PC: With sensitive healthcare data in play, how can organizations balance real-time analytics with HIPAA/GDPR compliance, data segregation, and robust model governance practices?

The good news is that in 20-plus years, the types of data we're handling haven’t changed, perhaps with the exception of social data. The industry has been dealing with clinical data, scientific data, and claims-based data for years and years, and has already determined which people and roles within the organization should have access to what data, and at what level of granularity. As a technology company, all you're really doing is overlaying your technology in a way that matches those established rules, and that allows different organizations to access the same system in different ways.

There are also data sets that are used reliably across the industry and are not company-specific. For example, the fact that I was prescribed an antibiotic yesterday is not unique to Pfizer, AstraZeneca, or any other company—it's an established fact about me as a patient. That’s not proprietary data that a pharma company should worry about getting out into the wild. But if you are using sensitive data, then it’s important to be transparent. How is that data being used? Is it being used to train AI tools, or not? In our case, the answer is always no. The Within3 system is set up using the highest compliance safeguards.

However, I think there's a little bit of an overcorrection on the part of pharma companies at this point, because the technology is still new. Right now, we’re seeing a bias towards being very cautious. I believe that will open up over time as compliance teams in particular get more familiar with the domain they're dealing with. It’s the same change resistance we’ve seen with every technology wave over the last 30 years.

By adopting AI-driven decision support, pharma organizations can address drug launch failure rates through precision and compliance. Transparent governance and a commitment to excellence allows companies to navigate complex markets with confidence and integrity. Together, these factors ensure that impactful therapies reach the right patients swiftly and responsibly.

Articles

Pharmaceutical companies often face challenges in drug launches due to outdated market insights. Real-time data and AI can bridge these gaps, enhancing diagnosis speed, engagement, and patient access. Effective data integration requires specialized expertise to manage diverse data sets and ensure accuracy. Transparent AI governance and compliance frameworks are crucial for responsible AI use, ensuring traceability and adherence to regulations. While sensitive data handling remains a concern, established industry practices and cautious adoption of new technologies can mitigate risks, ultimately improving launch outcomes and patient care.

Lance Hill, CEO of Within3, discusses how AI-driven decision support tools are helping pharmaceutical companies achieve launch excellence while maintaining regulatory compliance and data integrity.

Improving Pharma Launch Performance With AI-Driven Decision Support

Finding the Human Touch in a Wired World

According to Jen Butler, CCO at Pleio, researchers are beginning to connect emotional well-being to medication behavior, finding that loneliness can directly undermine treatment consistency and overall outcomes. In one study of over 2,000 chronically ill patients, those who reported feelings of loneliness were significantly more likely to experience poor adherence. Two-thirds said their diagnosis made them feel lonelier than before, highlighting how chronic illness often intensifies emotional isolation.

This growing awareness points to the need for a more holistic, patient-centered approach that goes beyond traditional access solutions. Addressing loneliness, enhancing emotional connection, and streamlining provider workflows are becoming integral to improving adherence and outcomes. Experts suggest that fostering trust and communication, both between patients and providers and within broader care ecosystems, will be key to reducing barriers and achieving meaningful health improvements in an increasingly complex specialty drug landscape.

Looking ahead, how do you see pharma companies balancing technology-driven engagement with the need for more human, emotionally resonant patient support?

You need someone to be there to support you and explain what you need and why it's relevant. I think technology has incredible scalability and support for patients, but it's only as good as they're willing to use it, and so if you can explain why you need a digital drug companion, or why you need a digital health coach, or why these programs are important, then you get the sustainability in using those programs.

There's also the technology reach as well. So they could maybe get reminders, but the reminders are great for maybe some early maintenance, but long term, you really need to understand, it's not nagging. You need to understand really more what's going on underneath the routines, the fear of injections, or the misunderstanding of what's happening to me. It's not just sending a quick text reminder.

I totally appreciate that we live in a wired world, and we're all used to looking at our phone, and that's part of our daily routine and habits, and sure, that should be part of our medication routine. But we also need to realize that a wired world with technology has a lot of noise, and there's other ways to cut through the noise by just having a conversation around it as well. So it truly is a balance that's needed, and there's probably also a balance around different segmentations of patients, like patients or consumers. Consumers has segmentations or behaviors. Not everyone wants to be called, not everyone wants a text, so we’ve got to kind of figure out that balance there.

Videos

In the final part of her Pharma Commerce video interview, Jen Butler, CCO at Pleio, emphasizes that as digital tools become central to patient support, there is value to balancing technology with empathy. 

Emotional Support as Strategy

Why should pharma leaders view emotional support as a business-critical component of adherence strategies, rather than just a “nice to have”?

Pharma leaders, you put so much of your budget into patient acquisition, and rightfully so, but if you're going to invest behind patient acquisition, you also want to make sure you're investing in sustainability and maintenance of and maintaining patients on treatment—otherwise known as your ROI—and that's really where a lot of our programs come in, in looking and supporting patients through the onboarding process.

You get that patient, you want to make sure they are understanding and supported to be able to bring that treatment to fruition, and addressing the emotional support and barriers is actually helping them get to the other investments that are made around co-pay programs, health coaches, digital support, and patients just aren't aware of everything around them.

I talked to friends who are on medications, and I know that there’re support programs, and I said, did you hear of this or that? And they're like, no idea. I think when you're a patient and you're just you're figuring out what your new normal looks like, and you're not always seeking out your pharma manufacturer’s programs, and there's so many amazing programs for patients.

There is already investment that's made addressing the emotional, overwhelming feeling of patience can help them in that rational decision making and mind frame to get them to those programs and stay on treatment

In the third part of her Pharma Commerce video interview, Jen Butler, CCO at Pleio, argues that addressing patients’ emotional barriers is essential to maximizing ROI, improving adherence, and ensuring that existing support programs truly reach those who need them.

Why DTC Drug Models Must Evolve From Access to True Patient Support

Pharma has made major strides in digital engagement, but many efforts remain transactional. What are the limitations of current DTC patient engagement models?

We're looking at a trend of the DTC platforms, and I do want to say that I think there are many benefits to DTC providing access to medications to patients who might not be finding them otherwise. However, we also have to understand that there's a lot of empowerment of the patient that comes along with this, and patients have to be ready and able for that empowerment to help guide themselves through this journey. They're not going to their traditional systems of support with a pharmacist at the ready to answer their questions.

A couple things that are concerning is, especially any drug-to-drug interactions. If these systems are in different places with their medical records, are patients aware to ask the questions of any interactions that this drug might be having with something else, and that they're getting from another DTC site? If you're just getting from DTCs, who's checking that, so that's a concern for sure.

And also then, just the whole approach of who's connecting with the patients to make sure that they're understanding the medication that they just went to the platform to attain. I think that there's a lot more guidance and customer support that's needed—patient support—and it's not just a digital check in. I think you need to have a conversation, so that you start to peel back what's going on with the patient, especially if they're not having their normal pharmacist check ins.

In the second part of her Pharma Commerce video interview, Jen Butler, CCO of Pleio, warns that without stronger education, communication, and pharmacist involvement, digital engagement risks becoming purely transactional.