Introduction

SPEAKERS:

·      Kim Moran, SVP & Head of U.S. Rare Disease, UCB

·      James Smith, Director, Marketing ATTR, AstraZeneca

·      Eric Peacock, Co-founder and President, MyHealthTeam; Chief Patient Officer, Swoop

KEY TAKEAWAYS:

·      Patient panels reviewing campaign asset catch creative missteps no internal team will ever see

·      Ultra-rare disease launches require building physician courage, not just physician awareness

·      Virtual sales reps, dismissed five years ago, are now among the most effective tools for reaching long-tail rare disease physicians

When UCB launched in thymidine kinase 2 deficiency this year, the commercial team faced a stark arithmetic problem. According to the FDA, there are 120 patients living with the disease in the United States, although the condition may be underdiagnosed. Nearly half the patients from clinical trials are seen by a single physician in New York City. TK2d is often fatal, in patients with initial symptoms less than 12 years of age, 50% of patients face mortality within one year after symptom onset. "The primary endpoint is survival," said Kim Moran, SVP and Head of U.S. Rare Disease at UCB. "That ups the ante of what you're doing every day."

That launch, simultaneous with UCB's work in generalized myasthenia gravis, sits at the extreme end of a spectrum that every rare disease marketer navigates. The strategies that work at 120 patients are not the strategies that work at 10,000. And DTC strategies that work in mass-market pharma need tailoring to fit rare audiences.

Finding patients who don't know they're lost

The foundational challenge in rare disease isn't market share. It's diagnosis. Most patients cycle through multiple misdiagnoses for years before anyone names what they have. In myasthenia gravis, roughly 20% of patients present with bulbar symptoms; swallowing difficulty, muscle fatigue, that get misread as MS or other neuromuscular conditions. In ATTR amyloidosis, there are 145 to 150 genetic variants, each presenting differently, some leaning toward cardiomyopathy, others toward polyneuropathy. A cardiologist seeing an ATTR patient may never think to ask about GI symptoms. But those symptoms matter.

"If you're going to a cardiologist, you might still need to talk about constipation or diarrhea or GI issues," said James Smith, Director of Marketing for ATTR at AstraZeneca, "just so that physician has the full scope of what a patient's experiencing to help accelerate that identification."

Geography adds another layer. Certain ATTR variants cluster by ancestry. Portuguese variant patients concentrate in specific U.S. regions. Appalachian and Irish variants appear elsewhere. Smith's team uses that geographic specificity to give community physicians a more targeted picture of which patients are likely to walk through their doors - and what to look for when they do.

Moran's team has taken a different angle on the identification problem. UCB has been experimenting with symptom trackers and assessors that help patients articulate what they're experiencing before they see a physician. The goal is to sharpen the patient-physician dialogue. "We try to allow them to really be crisp on the symptoms so that when they are having the patient-physician dialogue, they are able to name it a bit more," Moran said. For a disease where the diagnosing physician may have had zero minutes of training on the condition, a patient who arrives with precise symptom language can make a difference in misdiagnosis.

The patient panel that killed a campaign

UCB's rare disease unit launched with four words as its operating principle: patient-first, digital-first. The team assembled a panel of 15 people living with generalized myasthenia gravis who reviewed everything - patient enrollment forms, patient services, HCP marketing, patient-facing campaigns.

One concept depicted patients using a battery-level metaphor, the kind of visual shorthand that tests well in internal creative reviews. The panel's response was unanimous and immediate. "Do not depict me as a battery," Moran recalled. "This is offensive. It's inappropriate."

The lesson wasn't just about that one creative execution. It was about the gap between what looks clever to a marketing team and what feels accurate to someone living with the disease every day. UCB now consults the panel at least monthly. One member is a young man who was diagnosed with MG at 15 and joined the panel at 18, after his mother had participated as a caregiver. His presence on the panel represents something a metaphor could never capture.

Eric Peacock, co-founder of MyHealthTeam and Chief Patient Officer at Swoop, sees the same dynamic across the 70 patient communities his organization runs, 20 of which are in rare diseases. Roughly 80% of community members are information seekers, not advocates. They're served by local neurologists or cardiologists who may have had 30 minutes - or zero minutes - of training on their condition. "They’re there because they want to learn," Peacock said.

"There are now levers to pull at the clinical trial design phase to incorporate endpoints that matter, potentially external comparators that matter to payers, even if they're not going to be submitted for regulatory approval. Bring that perspective forward."

Manufacturers waiting for institutional alignment between regulatory and reimbursement frameworks will wait indefinitely. The bridge must be built from the manufacturer's side, and it must begin in trial design.

Evidence Architecture Is Now a Pre-Launch Competitive Moat

The evidence package that clears regulatory review and the evidence package that secures appropriate payer access have different authors, different timelines, and increasingly different contents. Treating trial design as a regulatory exercise produces evidence that arrives at the payer's desk already insufficient.

Adam Hathorn, Head, National Accounts, US Market Access, Sanofi, put the commercial timeline in concrete terms: "Our customers look at products that are coming to market really early on. It could be 12 months, could be 18 months, sometimes even before if it's a disease state that will be a newer disease state to them." That window means payer evidence needs are functionally a Phase 2 design input. The unmet need narrative, the demographic alignment to specific payer populations, and the shape of the forthcoming value story must be legible to payers before pivotal data exists—not assembled from it afterward.

This is where the concept of a living evidence ecosystem becomes operationally meaningful rather than aspirational. Lott argued that "the evidence should be dynamic and it should be evergreen and it should be responsive to the changes in market demands or access constraints that are popping up in real time... payer narratives can be dynamically developed over time and things that were once discrete in time become really living things." The static value dossier—assembled at launch, updated annually, submitted as a finished document—is the wrong unit of analysis. In a market where the IRA, PBM structural reform, and competitive biosimilar entry can shift a payer's formulary calculus within a single quarter, evidence that cannot adapt is evidence that decays.

Wright reinforced what specificity actually requires in practice: the data package lands better when it uses numbers the payer already uses, reflecting their own population rather than a generalized trial cohort. That reframes evidence from a broadcast document into a co-created instrument—one built with the payer's own denominators and defensible in their own internal review processes.

Hathorn cited simultaneous active variables that make static planning untenable: PBM reform shifting from a rebate model to a net sales model by 2028, IRA impacts shaping payer forecasting through 2027 and 2028, and Medicaid channel exposure from pending legislation. These are not future scenarios to monitor. They are current inputs to evidence strategy. Organizations that can generate payer-population-specific analyses inside the pre-launch window are structurally advantaged over those presenting generic value dossiers, and the distinction compounds over time.

Cell and Gene Therapy for Common Diseases Breaks the Model

The industry's CGT pipeline is no longer confined to rare diseases with small, well-defined patient populations. Common conditions are now in scope, and that shift breaks the evidence model that has supported drug access for decades.

Lott stated the methodological challenge without softening it: "We're not talking about rare diseases now. We're moving into an era of CGT for common diseases... we have to move beyond the average treatment effect that we're estimating as part of a routine clinical trial to these individual treatment effects.  That's the holy grail." This is a category shift, not an incremental refinement. The average treatment effect estimated from a controlled trial cannot answer the question a payer must now ask: which specific patient, in my specific population, will benefit enough from a one-time, high-cost therapy to justify this expenditure—particularly if they may switch plans before long-term benefit materializes? Actuarial caution in the absence of individual-level evidence is not irrational. It is the predictable consequence of asking population-level methods to support individual-level coverage decisions.

Likewise, generalizing clinical trial results to payer-relevant populations is another emerging opportunity. Lott described a capability that would have been implausible three years ago: "You would never come to a payer and be like, hey guess what, I got an external control on this that's really reflective of your patients. Or by the way, that's actually using your data, right? And these are your patients and this is how it's stacking up. And it's not perfect, but it's a step further than what we were used to." The "not perfect" acknowledgment matters. The question is whether it is directionally accurate enough to support coverage decisions, and whether the relationship infrastructure exists to have that conversation honestly.

Hathorn confirmed that payer-data partnerships create value extending beyond the manufacturer-payer relationship:

"When you have a partnership with a payer and you are using their data, it is super powerful... it also helps them to justify when they're talking to their customers."

Payers face downstream accountability to employers, unions, and plan members for every formulary decision. Evidence built on their own population data gives them a defensible rationale for their own customers, creating a mutual interest alignment that rebate negotiations alone cannot produce.

That alignment depends on a foundation Hathorn described in operational terms: "Not all your data is going to be perfect. And so you have to acknowledge that and be upfront about that... the more trust you have and the more understanding that you're both trying to achieve the same end goal, then that can really smooth out a lot of the friction." Transparency about data limitations is not a concession. It is the mechanism by which imperfect but directionally valuable evidence earns enough credibility to influence decisions. In a domain where individual treatment effect models carry irreducible uncertainty, the relationship equity to be believed despite that uncertainty is itself a form of competitive infrastructure.

The Internal Prerequisite Most Organizations Haven't Met

The external evidence challenge has an internal prerequisite that most large pharma organizations have not cleared: their own commercial analytics and HEOR teams are producing divergent answers from the same data. Before the industry can build living evidence ecosystems for payer customers, it must first achieve internal coherence.

Lott named the organizational failure precisely: "At a lot of pharmaceutical companies, your commercial data science and analytics team is separate from your HEOR or your real-world evidence team... I've seen agentic AI act as a single source of truth and a harmonization mechanism that really democratizes how we derive insights from data so that really everyone's on the same page inside of an organization." The internal silo is not primarily a technology problem—the databases often overlap. It is a query and governance problem. Agentic AI that normalizes how questions are asked and answered across functions creates the organizational foundation on which all external evidence communication depends. Without it, manufacturers risk presenting payer customers with analyses that contradict their own internal modeling.

Wright surfaced a second internal constraint operating simultaneously: portfolio triage at the field level. National account teams carrying multiple brands must allocate limited customer-facing time against competing internal priorities, and the evidence asset that matters most to a regional payer may be irrelevant to a PBM managing national formulary decisions. Hathorn was direct about the consequence: "You have to be able to say back to the brand, like, you know, I'm sorry, this is not that big of a priority for my customer. We'll do what we can do to establish the access that you need, but we have other priorities that we need to work on."

Lott also flagged a policy variable that belongs inside scenario planning but is frequently absent from it. CMS Coverage with Evidence Development decisions—historically applied to devices—are emerging as an anticipated mechanism for high-cost therapeutics, potentially generating post-approval evidence requirements resembling clinical trial protocols in scope and burden. Market access teams modeling only regulatory approval and commercial formulary access are missing a third evidence obligation that is not hypothetical.

From Deliverable to Infrastructure

The panelists described, without explicitly naming it, a category shift in what "evidence" means for market access. Evidence is moving from a deliverable—the static value dossier assembled at approval—to an infrastructure: a continuously operating system that links trial data to real-world data, generates payer-population-specific analyses on demand, and updates as policy and competitive conditions shift.

Lott pointed to a research model that illustrates where this is heading. At Stanford's Center for Digital Health, a team published work in Nature using networks of agentic AI to run drug development processes end-to-end, from modeling through candidate identification. The implication for market access is not that AI replaces account teams. It is that hybrid teams—human account directors paired with AI agents handling data synthesis, scenario modeling, and population-level analysis—are already technically feasible and will become operationally standard. "I want to see hybrid teams of humans and agents as the years progress," Lott said. "Some people are already doing it."

Hathorn offered a commercially candid recalibration of where real-world evidence actually sits in payer decisions: "I don't know that it would outweigh it. I think it complements it... you could be talking to a PBM or a GPO of a PBM and, you know, it's pretty much financial. But you could be talking to a regional payer and it's really impactful." The assumption that superior evidence translates uniformly into superior access is wrong. Evidence infrastructure investments must be calibrated to the account mix—weighted toward payer types where population-level data and trust relationships can move formulary decisions, and paired with financial contracting where they cannot.

The diagnostic question for market access leadership is not whether the organization has a value dossier. It is whether the organization can link pivotal trial data to real-world claims at the patient level after trial exit, generate a payer-population-specific external control analysis within the pre-launch planning cycle using the payer's own data denominators, and produce the same answer from HEOR and commercial analytics when querying the same underlying database. For organizations approaching cell and gene therapy in common disease markets, a no on any of these is not a capability gap to close post-launch. It is the access risk that is already accumulating.

This content reflects the personal views of the speakers and is not intended to represent the official position of their respective companies.

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

"Trust is built over time," Schlessinger said. "One small slip can make it vanish."

That asymmetry — slow to build, fast to lose — has a structural implication that the session's full argument demands: brand teams cannot manage trust. They can only create the conditions in which trust becomes possible. Those conditions are internal alignment, field force capability, real-world evidence infrastructure, and operational programs that remove friction between prescription and patient access.

The organizations that understand this are not building trust campaigns. They are building systems. In oncology, where a community physician managing 30 tumor types needs a succinct, scientifically credible answer in under five minutes, and where a rural patient over 65 may never reach a center of excellence, the difference between a campaign and a system is the difference between a patient who gets the standard of care and one who doesn't. That outcome is determined by decisions made in cross-functional strategy meetings, field training programs, and logistics contracts — not in the brand messaging document.

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

"Making sure that you tailor your governance to the appropriate risk level — just like every other business decision or tech investment you would make — is critical to ensuring that you don't get caught up in a morass of doing the same process for something that's super risky as for something that everyone would understand is not as risky."

A rep copilot surfacing pre-call planning information inside an existing Veeva workflow is not the same governance problem as an AI system synthesizing clinical trial data into brand strategy recommendations. Treating them identically wastes compliance resources and builds organizational resistance to the governance function itself.

Joshi formalized this into a three-tier framework at Takeda. Tier one covers low-risk, high-return tools — rep copilots, omnichannel copilots, market research assistants — where experimentation within existing ecosystems carries limited regulatory exposure. Tier two covers marketing automation and MLR processes, where legal and regulatory involvement becomes necessary. Tier three is the category every pharma company is racing toward: an AI system capable of synthesizing clinical trial data, conference presentations, market research, and transactional records into disease area strategy. "This is the most high, high risk area," Joshi said. "We don't have a real answer over there." The framework is useful precisely because it makes that uncertainty explicit rather than treating all three tiers as equivalent unknowns.

BMS has operationalized the time dimension of this calibration. "Gone are the days when you had unlimited funding to run pilots you wanted to run," Tantsyura said. "We condensed the timelines — before it used to be unlimited time; now you have to show the results within the three-month time period.

Leadership now is expecting that before we even start working on anything, we have very clearly defined value — not just slides, but demos — showing every two weeks what progress has been made." A hard pilot window with biweekly accountability replaces open-ended exploration. The three-month constraint is not primarily a cost control mechanism. It is a forcing function that surfaces adoption problems and compliance friction early enough to address them without sunk-cost pressure.

One governance actor most technology conversations still overlook is procurement. "Procurement is also having a seat at the table," Joshi said. "Procurement along with legal has been really driving conversation much earlier on in the journey of AI, so that you don't have to do expensive retrofitting of all this from a regulatory standpoint. That has been the big learning." Procurement's earlier entry into AI investment decisions is preventing downstream regulatory costs — but it also introduces a new friction point at the front of the pipeline that organizations haven't fully mapped into their governance timelines.

The Capability No One Named But Everyone Described

Dr. Salinas posed the question that crystallizes the regulatory specificity of the pharma context: pharma operates in a very different space, with strict regulatory oversight — how are organizations responsibly deploying AI while ensuring compliance and maintaining audit visibility? The session produced multiple frameworks in response. What it did not produce — because no panelist named it explicitly — is the capability that connects all of them.

The measurement dimension points toward it. "It's no longer about Gen AI insights," Liu said. "We need to measure also the activities that are taken upon those insights and then what's the outcome of that. That's the next level we're trying to get into." The people dimension confirms it. "Do we have the leadership alignment? Do we have the team operationally scalable? This is about your deployment, driving the change management and thinking through the end-to-end pull-through," Liu said. Both observations describe organizations that must hold multiple operational modes simultaneously, not transition from one to the next.

The capability is governance ambidexterity: the organizational capacity to run parallel governance tracks at different speeds and scrutiny levels for different risk tiers, without collapsing them into a single process. Tantsyura confirmed BMS is already doing this — federated governance for traditional AI, centralized cross-functional review for agentic AI, both live at the same time. Joshi described the "ambidextrous organization" in terms of operations versus experimentation. The evidence suggests the same structural demand applies to governance itself.

Liu's measurement evolution — tracking not just AI outputs but actions taken and outcomes produced — requires governance infrastructure that spans from content generation through field execution through business impact, across multiple compliance regimes simultaneously. That is a different organizational design problem than the one pharma solved when it first built centralized AI review boards.

The organizations advancing fastest are not the ones with the most mature governance model. They are the ones that can operate multiple governance models in parallel — applying proportionate scrutiny through risk-tiering, maintaining federated guardrails for known-risk categories, and restarting centralized review for emerging ones — without treating each new technology generation as a crisis that suspends everything else. The diagnostic question for any leadership team is not whether your governance is mature. It is whether your governance architecture can absorb the next generation before this one has finished scaling — and whether you have already started building the answer.

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

Text

Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.

Strategic Focus Starts with Choice

The prescription Riordan and Stamm offer — limit focus to one to three critical launch drivers — sounds simple in a conference presentation and is operationally brutal in practice. Not because identifying the top drivers is analytically difficult. Because maintaining that focus through a twelve-to-eighteen-month planning cycle, against continuous internal pressure to re-expand scope, requires a form of organizational discipline that most pharma companies have not built and many have not attempted.

"The organizations that win launches," Riordan argued, "are the ones that pick the one or two or three things that are truly critical and resist the pressure to treat everything as equally important." The resistance is the hard part. Analytical prioritization is a workshop exercise. Organizational prioritization is a sustained political act.

What the survey of pharma executives finding about HCP gravitational pull actually reveals is that most organizations are not prioritizing at all. They are listing. A launch plan that identifies fifteen strategic priorities has not made strategic choices. It has documented everything the organization intends to do and labeled it strategy. The discipline Riordan and Stamm are prescribing is the discipline to look at that list and remove twelve items from it, knowing that the functions responsible for those items will push back.

What really matters the most is permission. Someone with sufficient authority must be willing to say that a given activity is not happening, and must be willing to hold that position when someone asks again. That kind of protected prioritization is culturally foreign in large pharma organizations, where cross-functional consensus is both a regulatory necessity and a deeply embedded operating norm. Stamm observed that the launches in Inizio's benchmark that most consistently outperformed were distinguished not by superior resources or more sophisticated analytics, but by clearer decision rights. Someone was actually empowered to hold the line on scope.

Governance That Makes Saying No Stick

Prioritization without governance has a half-life measured in weeks. The governance structures Riordan and Stamm advocate are not oversight mechanisms. They are decision-forcing mechanisms. The distinction matters. Oversight governance asks whether the launch is on track. Decision-forcing governance asks what should stop, given what the team now knows. The second question is structurally harder, because stopping an activity that has already been resourced and socialized requires overriding prior decisions, which in most organizations requires consensus that rarely forms fast enough to be useful.

Stamm's implementation framework incorporates agile rhythms not as a software methodology import but as a structural solution to this problem. Regular, time-bounded review cycles require teams to make explicit choices about what continues and what does not. The agile cadence creates a forcing function: instead of activities persisting by default until someone makes the case to stop them, they require active renewal. The burden of proof inverts. That inversion is modest in description and significant in practice. It is the organizational mechanism that keeps prioritization from decaying back into comprehensiveness.

What deserves naming directly, is why organizations resist these structures. Decision-forcing governance redistributes power. Comprehensive planning environments favor consensus-builders: people who can align large cross-functional groups, accommodate diverse stakeholder interests, and produce plans that reflect everyone's input. Prioritized planning environments favor decision-makers: people willing to hold a position under pressure and absorb the organizational friction of saying no. Those are different skills, rewarded differently in different cultures. The shift from one operating mode to the other is a cultural change that governance changes can support but cannot substitute for.

The practical diagnostic is simple enough that any launch team can apply it immediately. Count the stated priorities in your current launch plan. If the number exceeds three, you have not prioritized. You have listed. The operational difference between a list and a strategy is what you have chosen to sacrifice. Most launch plans contain the former and describe it as the latter.

Every organization that has ever underperformed a launch had a strategy. Most of them had too many. Importantly, these findings are based on proprietary benchmark analysis, and should be interpreted as directional insights rather than definitive conclusions.

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.

Reaching physicians who have never seen a case

HCP strategy in rare disease splits into two problems that require different solutions. The first is the thought leader tier - the 50 or so specialists nationally who diagnose and treat the majority of cases. The second is the long tail: thousands of community physicians who may see one or two patients a year, if that.

For the thought leader tier, Smith's team has focused on smaller regional congresses alongside the major national meetings. "To build trust, we need to show up at those smaller, more regional congresses to engage physicians," he said. The team has also explored fellowship-style programs targeting younger physicians early in their training, before they develop the habit of missing a rare disease diagnosis. Expert-on-demand networks connect community physicians with regional specialists in real time, so a provider who suspects ATTR but lacks confidence can reach someone who can help - or can refer to an amyloid center of excellence with a warm handoff rather than a cold one.

For the long tail, Moran has landed on a combination that would have seemed counterintuitive a few years ago. "I don't think five years ago I would have been like, hey, we should maybe light up those phones again," she said of virtual sales reps. "Wow, it actually is hugely effective, especially when you're trying to find a needle in a haystack." Predictive analytics and suggestion engines help field teams anticipate which community physicians are likely to encounter a rare disease patient in the next few weeks. Doximity, CME programs, targeted digital content - the channel mix is bespoke by physician, not uniform across the list.

Connected TV has moved from experiment to standard. UCB is now running DTC connected TV in rare disease, reaching 50% of the patient population at scale. Smith's team uses NPI tracking to measure how many target physicians are being reached through the same devices. "People laugh when we're saying you're going to do TV for a rare disease," Smith said. "Like, we're not buying Jeopardy, but we're buying custom audiences that we know have high affinity and high audience quality." The reach numbers in rare, precisely because the target population is small, make it easier to demonstrate impact - not harder.

AI as infrastructure, not oracle

The panel's discussion of AI was notably unsentimental. A recent study found AI misdiagnosed conditions in 53% of cases. In rare disease, where the training data is thin and the presentations are heterogeneous, that number is almost certainly higher.

The more productive question isn't whether AI can diagnose rare disease patients. It's whether AI can help surface suspect patients for physician review - and how to scope that function so it doesn't create more noise than signal. Smith's team has learned to limit EHR algorithm runs to patients a provider will see in the next two weeks or month, rather than running against an entire system. "If you go to Penn and they run it against their entire EHR system, there's going to be patients that are deceased, patients that no longer go to that provider," he said. A list of 1,000 flagged patients paralyzes a physician. A list of 12 creates action.

On the patient side, Moran flagged a trust problem that precedes the accuracy problem. Roughly 60% of patients are worried that AI will override their physician's judgment. Deep fakes and misinformation have eroded confidence in digital health tools broadly. "This trust element is going to be a trend that we continue to see rise over the next couple of years," she said.

Peacock offered a counterintuitive data point from MyHealthTeam's own experience. When his team launched AI-assisted tools in patient communities, expecting a flood of questions, they got near-silence. Patients didn't know what to ask. What worked was showing the six most common questions - essentially prompting patients to recognize their own concerns in someone else's words. The same principle applies to how pharma should think about feeding large language models: patient quotes and lived-experience language are what LLMs are currently starving for, and organizations that include authentic patient voice in their published content are seeing referral traffic from AI overviews increase, not decrease.

The playbook that emerges from this panel isn't a set of tactics. It's a set of commitments: to find patients the healthcare system has lost, to build physician confidence where training never existed, and to treat access as a relationship that starts before diagnosis and continues long after the prescription is written. In a disease with 120 patients, every one of those commitments is visible. There is no hiding behind aggregate numbers.

To get you highlights of Pharma USA 2026 faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

Discover more on this topic at Pharma Customer Engagement USA 2026 (October 27-28, Philadelphia) - where commercial, marketing, medical, data and AI pioneers converge. Explore the agenda here.