Based on my modelling, the pharmaceutical industry loses around £16 billion in launch revenue every year. Not in R&D. Not in clinical trials. Not in regulatory approval. In what I call the last mile: the gap between a drug reaching the market and that drug actually reaching the patient. And based on fifteen years working on drug launches, I believe the majority of that loss is preventable.
34% of drug launches miss their year one forecasts. Some analyses put that figure closer to 60%. The first year sets the trajectory. Products that miss early tend to keep missing. Products that outperform early tend to keep outperforming.
At the same time, the commercial window available to recover that investment is narrower than it has ever been. Average patent exclusivity runs to eight to twelve years post‑launch. The window before competing products arrive has fallen to around four years in many therapy areas. Development costs average roughly $2.3 billion per drug. The numbers demand that every year of that window performs. The pressure is not the problem. The problem is that launch teams are still making decisions without the full picture.
Advisory Boards Tell You What. They Do Not Tell You Why.
Thousands of advisory boards and webinars run every year in pharma. They sit at the heart of launch planning and lifecycle strategy.
They produce something genuinely valuable: expert opinion. Physicians, payers, and KOLs tell teams what they think, what they are seeing in their clinics, and what concerns them about a new therapy. That is real signal. But opinion tells you what. It does not tell you why. Why does a drug achieve 60% uptake in one region and 20% in another, when the physician base and clinical profile look identical? Why does adherence collapse in one postcode and hold strong three miles away? Why do two physicians in the same focus group report completely different patient experiences with the same drug?
The answer is almost never in the medicine. It is in the environment around the patient. The local healthcare infrastructure, access barriers, transportation constraints, socioeconomic pressures, cultural norms, language. These are the structural forces that shape whether a prescription becomes a treatment. They sit outside the advisory board transcript. And without them, the intelligence is incomplete. This is the gap that costs launches their trajectory.
Treating Every Conversation as a Signal
At Adelo, I start from a different premise: every conversation a pharma team has with an advisor, a physician, or a KOL is a data point. The question is whether you capture it, structure it, and connect it to everything else you know about the environment those advisors are operating in.
Our platform captures advisory board discussions, webinars, and focus group conversations digitally and structures them automatically. That gives you the WHAT: what physicians are seeing, what concerns payers have, what patterns are emerging across markets in real time. We then layer that against the WHY: postcode‑level social determinants of health data, population trends, migration patterns, demographic signals, scientific literature, and environmental data. When those two layers combine, the picture changes entirely.
A physician reporting that patients are discontinuing treatment is no longer just a sentiment data point. When you map that comment against local deprivation indices, transportation access scores, and employment patterns, the discontinuation becomes explainable, predictable, and addressable. The strategy sharpens. The playbook changes. The intervention becomes specific rather than generic.
This is what I mean by launch intelligence. Not transcripts. Not raw dashboards. A geo‑mapped view of where your launch will succeed, where it will stall, and what the structural reasons are in each case. Adelo exists to build that layer. It turns the advisory boards and webinars you already run into last‑mile intelligence you can act on.
What the Intelligence Layer Reveals: A Worked Example
To understand what high‑definition patient population intelligence looks like in practice, it helps to work through a specific disease and geography. Hepatitis B in London is a useful illustration, because the gap between what standard launch planning sees and what the data actually reveals is stark. London’s hepatitis B rate runs far higher than the rest of England. Most chronic HBV infections are in people of ethnic groups other than White British. These are not marginal populations. They are the population. And yet most launch planning treats them as a line in a market access slide rather than the primary strategic focus.
When you layer SDOH data, migration patterns, census demographics, and community infrastructure data against that clinical profile, specific patient populations emerge at postcode level, each requiring a different engagement architecture. Take Somali‑born residents concentrated across Hackney and Tower Hamlets: E1, E5, E8, E17. High deprivation scores. High rates of overcrowded housing, a known transmission risk factor. Significant gaps in GP registration and prior hepatitis screening. Administrative barriers including language, immigration status concerns, and distrust of institutional healthcare systems mean this population is largely absent from standard screening programmes.
The engagement playbook here is not a GP letter and a clinic appointment. Research consistently shows that imams and mosque leaders in Somali communities carry substantially more health communication authority than clinical channels. A linkage‑to‑care model built through mosque networks, delivered in Somali, with on‑site testing available in a trusted setting, reaches a population that a field force deployment would entirely miss. The channel is trust. And trust is held in the community, not the clinic.
This is what high‑definition looks like in practice. Not “undiagnosed hepatitis B patients.” But a specific community, in specific postcodes, with specific barriers, and a specific playbook designed around how decisions and trust actually operate in that environment. The disease changes across therapy areas. The geography changes. The community infrastructure changes. But the methodology is consistent: layer the WHAT from advisory conversations and webinars against the WHY in the local environment, and the playbook becomes specific enough to actually work. That is the difference between a strategy built on what physicians say and one built on why adoption actually happens.
Low‑Definition vs. High‑Definition Patient Populations
Most pharma companies define patient populations clinically. Inclusion criteria. Disease stage. Comorbidities. “All undiagnosed hepatitis B patients who have not been treated.” That is a patient population in the traditional sense. But it is not precise. It is not actionable. And in a narrowing commercial window, it is not enough. High‑definition looks different. Not “undiagnosed hepatitis B patients.” But “Somali‑born residents in E8 Hackney, with low transportation access, no prior hepatitis screening, precarious employment, and language barriers to healthcare access.”
That is specific. That is something a clinician, a field team, and a payer can all act on simultaneously. When a physician sees a patient matching that profile, they know what they are looking at. When a field team targets those neighbourhoods, they understand the context. When a payer designs a patient support programme, they are designing for the actual barriers those patients face, not a notional average. The difference between those two definitions is not semantic. It is the difference between a launch that stalls and one that finds the population it was designed to serve.
The Last Mile Is Structural
There is a reason launches fail in the last mile rather than in the clinical data or the top‑line marketing strategy. The last mile is where the environment takes over. Social determinants of health shape a large share of health outcomes. Poor housing links to poor health. Food insecurity links to chronic disease. Transportation barriers link to missed care. These are not correlations to acknowledge in a market access document. They are lived realities that determine whether a prescription becomes a treatment course, and whether that treatment course delivers the outcomes the clinical trial promised.
Evidence on geolocated information behaviour also shows that local data drives a fundamentally different response from clinicians. When you show a physician data specific to their own catchment area and their own patient population, something shifts. The information is no longer abstract. It is their problem to solve. Physicians are more influenced by geographically close prior adopters when making clinical decisions. National‑level data is processed differently from neighbourhood‑level data. One is background. One is actionable. Generic launch strategy does not account for any of this. High‑definition patient population data, mapped to the local environment and grounded in what those physicians told you in advisory boards and webinars, does.
It Is Not Too Late to Change the Vector
For teams already a few months into a launch and tracking below forecast, the data is sobering. Very few launches that miss early trajectory targets manage to recover. That is not a reason to accept the trajectory. It is a reason to act faster and with more precision.
My modelling puts the annual industry loss from launch underperformance at around £16 billion. If Adelo prevents just 5% of that, the figure preserved runs to approximately £800 million annually. That is not theoretical. It is the direct result of understanding, earlier, which geographies are stalling, which patient populations are not converting, and what the structural reasons are.
A geo‑mapped launch intelligence snapshot can surface that picture in weeks. Not to rewrite the strategy from scratch. But to sharpen the playbook: redirect field force deployment, adjust patient support programme design, refocus HCP engagement in the postcodes where structural barriers are driving the underperformance. The window is narrow. But it is open. Knowing where the drag is coming from is the difference between adjusting the trajectory and watching it compound.
The Adelo Architecture
Advisory board discussions, webinar content, and focus group conversations captured and structured digitally. That is the WHAT layer: what physicians are observing, what questions they are asking, what patterns are visible across markets in real time.
Layered against postcode‑level SDOH intelligence, scientific literature, demographic and migration data, and behavioural signals from activity and digital health sources. That is the WHY layer: the structural and environmental forces shaping adoption in each geography.
The product we are building at Adelo is the software version of a process I have already run for years by hand: taking advisory boards, webinars, and focus groups, tying them to SDOH and geospatial evidence, and turning that into practical playbooks. I blueprinted the platform from that experience so teams can do in weeks what used to take months. The output is a geo‑mapped launch report and a strategic playbook showing where adoption will succeed, where it will stall, where structural barriers exist, and where targeted intervention will improve performance. Not transcripts. Not just another dashboard. A clear view of where the launch is going and what to do about it.
As Na‑Ri Oh, Global Head of Commercial Product Strategy at Gilead, has said of this style of approach: “This has been responsible for delivering more than 90% of revenue for Hepcludex since launch.” Adelo is built to make that kind of last‑mile intelligence available to every launch team that already runs advisory boards and webinars and wants them to do more than generate another slide deck.
It Scales
Once you have identified the structural barriers in one borough of London, you have a playbook. That playbook works in a ward in Toronto. It works in a neighbourhood in Paris. It works in a district in Sydney. Not because the populations are identical, but because the methodology transfers. The framework is the same: identify the high‑definition patient population, locate them at neighbourhood level, understand the barriers, engage with precision, measure and refine.
The first time you run this kind of analysis it might take months. By the fifth market, it takes days. That is the difference between a consulting project and a scalable operating model. One gives you an answer. The other gives you a machine for generating launch intelligence across markets, earlier in the planning cycle, and faster than the launch calendar allows for traditional advisory methods. The blockbuster era relied on efficacy, investment, and being early in class. That era is gone. What replaced it is precision. And in a world where the last mile is where launches fail, precision is not optional. It is the strategy.