Executive summary

Solving the identity crisis at the heart of evidence-based strategy

In an era where AI promises speed and scale, market access consultants still face one stubborn bottleneck: knowing who to trust.

Every year, millions of scientific papers are published, yet the author information that underpins them is deeply flawed. Inconsistent names, duplicate profiles, and fragmented identities make it hard to distinguish credible thought leaders from incidental contributors. For consultants tasked with turning evidence into strategy, this is a nightmare. Mistaken attribution can skew KOL mapping, distort literature reviews, and mislead AI systems built to support high-stakes decisions.

This whitepaper unpacks one of the most underappreciated problems in scientific research: author disambiguation. And it shows why solving it is essential for delivering trusted, explainable, and high-impact AI in market access.

Drawing on our work at Knowledgeable, we explain:

• Why traditional systems fail, and how that quietly sabotages strategy.
• How author disambiguation actually works, from practical workflows to AI integration.
• The measurable benefits for consultants and clients: from cleaner literature reviews to stronger stakeholder maps.
• What comes next, as this capability powers predictive insight and a more strategic future for evidence-based consulting.

For anyone building, buying, or relying on strategic intelligence platforms, this is no longer a nice-to-have. It’s the foundation for clarity, credibility, and competitive advantage in a world of overwhelming scientific noise.

Because in market access, knowing who said it is the first step in knowing what to do next.

1. The overlooked challenge of attribution in scientific research

In the age of information abundance, scientific output is growing at an exponential pace. In 2023 alone, more than 3.5 million peer-reviewed articles were published globally, spanning an ever-expanding range of diseases, technologies, and therapeutic approaches. This volume presents a profound opportunity for insight, but only if that information can be properly attributed, interpreted, and acted upon.

And that’s where one of the most persistent, and under-appreciated, problems in scientific research rears its head: author attribution.

The problem with names

Unlike consumer-facing platforms, scientific databases were not built with modern entity resolution in mind. As a result, author metadata is often messy, inconsistent, and misleading:

• An author publishing under "J. Smith" at one institution may later appear as "John R. Smith" or "J.R. Smith" at another.
• A common name like “L Zhang” may correspond to dozens of distinct individuals across different therapeutic areas and geographies.
• Spelling variations, missing initials, inconsistent affiliations, and transliteration issues all conspire to fragment a single author’s identity. Or worse, collapse multiple people into one.

This makes it remarkably difficult to determine who actually wrote what, and by extension, who is truly shaping the research narrative in a given field.

Why this matters more than ever

For researchers and consultants alike, the implications are serious:

• KOL mapping becomes noisy and inaccurate, with stakeholder influence misrepresented or entirely missed.
• Scientific trends are misread, as the wrong experts are credited with key findings.
• AI outputs become untrustworthy, hallucinating insights based on incorrectly merged or duplicated author profiles.

This problem is compounded by the very systems we rely on to make sense of the literature. Most databases and research tools are built on unstructured or semi-structured metadata, treating author names as plain text rather than as entities to be verified, cleaned, and linked.

“We were building a stakeholder map for an oncology product and kept hitting the same name - four times. It turned out they were four different people. We almost built a whole narrative around a KOL who didn’t exist.”

— Senior Consultant, Global Market Access Agency

The cost of attribution error

In high-stakes environments like market access, where consultants are tasked with connecting evidence to strategy and strategy to outcomes, this is a risk multiplier.

• Strategic decisions are built on shaky evidence.
• Stakeholder engagement plans target the wrong experts.
• Literature reviews balloon in size due to duplication, or miss the mark entirely due to omission.

What’s more, as AI tools become increasingly integrated into daily workflows, the quality of input data becomes critical. Language models like GPT or Gemini don’t inherently know that “L. Zhang” in rheumatology isn’t the same “L. Zhang” in ophthalmology. Without disambiguation, these systems produce plausible-sounding, but semantically incorrect summaries and insights.

TL;DR

We don’t have a problem accessing information. We have a problem trusting it. Attribution error is the silent saboteur of literature analysis, eroding the credibility of search results, stakeholder maps, and AI-generated outputs alike.

Fixing this requires more than clever filters or manual review. It demands a dedicated process of author disambiguation: using structured logic, natural language processing, and domain expertise to verify identities, cluster publications, and accurately map the people behind the science.

2. Why it matters for AI, search, and strategic thinking

Author disambiguation isn’t just about fixing messy data, it’s all about elevating the quality of decisions made by both humans and machines. In market access consulting, where strategy is built on evidence, who generates that evidence is often just as important as what it says.

And yet, many systems (especially those powered by generic AI) still treat all voices equally.

When the AI doesn’t know who’s speaking

Large language models (LLMs) such as GPT, Claude, and Gemini are trained to generate coherent language based on statistical patterns in the data. But without reliable metadata about authorship, LLMs cannot distinguish between an influential thought leader and an incidental contributor.

This leads to three compounding problems:

i) Wrong insights, confidently delivered

AI summarisation may accurately extract text from publications, but, without knowing the authority behind the content, it often highlights secondary or speculative findings while underweighting key, high-impact results.

In market access, this risks building strategies on incomplete, unbalanced, or even misleading summaries.

ii) KOL discovery becomes unreliable

Author disambiguation is fundamental to accurate stakeholder mapping. Without it, AI may merge unrelated author profiles, inflate influence scores, or fail to surface relevant voices altogether. Particularly in crowded therapeutic spaces or when dealing with common surnames.

iii) Weakened trust and traceability

In a consulting environment where every insight must be defendable, AI outputs without verifiable attribution undermine credibility. It becomes difficult to answer client questions like:

“Where did this come from?”

“Is this evidence from a credible expert?”

“Why was this included over that?”

“We had a model generate a promising summary on trial outcomes… until we realised it was quoting a paper by someone completely outside our therapeutic area. It sounded right but was strategically useless.” - Director of Evidence Strategy, Market Access Consultancy

Why it matters most in market access

Market access professionals need to do more than just process literature, they need to build strategy from it. That means the evidence used must be:

• Relevant
• Trustworthy
• Prioritised correctly
• Clearly attributable

Author disambiguation ensures these criteria are met by providing AI (and humans) with the foundational context required to:

• Assign influence
• Map networks of expertise
• Understand publication impact over time
• Tie insights to real-world decision-makers

The result: Sharper insight generation, more reliable strategic outputs, and less time wasted reviewing irrelevant or duplicated information.

How this changes the game

With disambiguated author data:

• AI outputs are cleaner, smarter, and more relevant, because they are grounded in the actual influence behind the research.
• Search results become more precise, prioritising high-impact contributions over generic keyword matches.
• KOL maps become strategic assets, not noisy guesses.
• Consultants can defend and explain every output, restoring confidence in AI as a partner, not a risk.

TL;DR

AI can read a paper, but without knowing who wrote it(or how much that matters) it often gets the message wrong.

Author disambiguation gives AI the context it needs to summarise smarter, search sharper, and deliver insights consultants can trust.

3. How author disambiguation works

In scientific research, names can deceive. Two authors can share the same name. One author can publish under multiple aliases. And when you're working across thousands of papers and multiple therapeutic areas, knowing who actually wrote what becomes a serious challenge.

At Knowledgeable, we’ve solved this problem by disambiguating authors at scale: giving consultants and AI systems a reliable, unified view of who the true contributors are, and how much weight their work carries.

What it looks like in practice

Let’s say you’re running a landscape review on JAK inhibitors in dermatology. You search for recent clinical trials and publications to identify emerging voices in the space.

• Without author disambiguation, you're flooded with papers from "Y. Chen" and "M. Patel" but have no idea which are leaders in the field and which are unrelated researchers in immunology, ophthalmology, or cardiology.
• You spot a trial result that looks important, but the author has no linked history. Are they credible? Have they published before? Are they even in this field?
• A KOL heatmap flags "J Smith" as highly active but it turns out that's actually three different people, publishing in three different regions.

With disambiguation, these issues disappear.

• You see clear author profiles, showing only the relevant contributions.
• You can track publication history, therapy focus, and strategic relevance over time.
• You instantly know who’s rising, who’s influential, and who’s peripheral.
• And when the AI summarises findings, it prioritises the voices that matter most, not just the ones with the most hits.

Why this matters for consultants

Whether you're building a KOL map, doing a targeted literature review, or preparing for a proposal, speed and accuracy are everything. And if you can’t trust the identity behind the insight, you risk making the wrong call.

Disambiguation ensures that:

• You know who’s who: no more duplicate names or ghost authors.
• You can defend your choices: every insight is backed by the right source.
• You work faster: less second-guessing, less manual checking.
• AI works smarter: because it understands whose work carries weight.

“It’s like going from a phonebook to LinkedIn. Same names, but now you can actually see the person behind them, and whether they’re worth listening to.” - Engagement Strategist, EU Market Access Firm

Strategic leverage

When consultants have confidence in attribution, they work smarter:

• KOL strategies become sharper: You engage the right stakeholders with tailored evidence.
• Proposal research becomes faster: You instantly surface the right voices to cite.
• Landscape reviews become leaner: You focus only on high-impact findings, not background noise.

And critically, every insight is traceable. So you can always show your working.

TL;DR

In market access, who says something is often just as important as what they say.

Disambiguation makes that clear. Giving consultants the confidence to move fast, prioritise the right voices, and deliver insights that hold up under scrutiny.

4. What this unlocks: Value for consultants and clients

Author disambiguation is the backbone of author intelligence, helping consultants think faster, work smarter, and deliver more strategic value.

When you know who wrote what, you can finally trust what to prioritise.

In complex, high-stakes domains like market access, data without context is just noise. But when the identity and influence behind that data is understood, the result is a system that strategically prioritizes information, rather than just organizes it.

Below, we break down exactly what author disambiguation enables across daily workflows, strategic planning, and AI-powered insights.

Ranked literature by author impact

With disambiguated author profiles, Knowledgeable can automatically rank and filter literature based on author influence, not just keyword relevance.

Our platform takes into account:

• Publication volume and recency
• Trial leadership roles
• Journal impact factors

This means consultants don’t waste time reviewing marginal papers, they start with the evidence that’s most likely to shape decisions.

“Instead of reading 20 papers to find the best 3, we now start with the best 3.” - Market Access Lead, EU Consultancy

Better evidence weighting

Not all evidence is created equal. And not all authors are equally authoritative.

Our AI now factors in who authored a study, not just what the study says. This allows for:

• Smarter summarisation that gives weight to primary investigators and field leaders
• Higher confidence in outputs used for HTAs, TPPs, and value messages
• Reduced risk of over-representing outlier or exploratory data

In short, insights are now backed not just by information, but by proven expertise.

Stronger KOL and stakeholder mapping

With clear author identities, the platform can build co-author networks and influence graphs that reflect the actual structure of scientific collaboration.

Use cases include:

• Identifying regional KOLs who drive payer influence in specific indications
• Mapping institutional affiliations to uncover collaboration patterns
• Linking authors to trial registries and policy contributions

All of which lead to more accurate, context-rich stakeholder maps, with fewer false positives.

Predictive KOL discovery or ‘Rising Stars’

Because Knowledgeable tracks longitudinal author activity, we can identify emerging thought leaders based on rising:

• Publication frequency
• Topic relevance
• Co-authorships

This allows consultancies to spot new voices early, often before competitors know they exist, and bring them into strategy or engagement plans.

Author trajectory & impact over time

Every author profile includes a timeline of influence, making it easy to understand:

• When an expert was most active
• Which therapeutic areas they contributed to
• How their influence evolved alongside the product or therapy class

This insight is essential when preparing HTA dossiers, designing advisory boards, or prioritising clinical collaborators.

Cleaner AI summarisation

AI summarisation becomes more trustworthy and strategic when grounded in author identity.

• Outputs can prioritise primary authors over secondary contributors
• Systems can deprioritise outdated or low-impact content
• Summaries now include attribution context, increasing transparency

The result is a narrative that feels less like generic prose and more like a consultant who understands the field.

Trust and traceability

Every insight within Knowledgeable is traceable to:

• A verified author identity
• A specific publication or study
• A strategic rationale for inclusion

That means teams can defend their findings under scrutiny whether in a regulatory review, a client workshop, or a senior stakeholder meeting.

TL;DR

Disambiguated authorship turns raw data into strategic intelligence.

It powers better AI, sharper KOL mapping, stronger evidence prioritisation, and, makes every decision faster, clearer, and easier to defend.

5. Where this goes next: Continuous refinement

The power of author disambiguation doesn’t stop at accuracy, it’s what it enables next that really moves the needle.

In the scientific and regulatory environment, the ability to refine insight over time is just as critical as getting it right the first time. Author disambiguation, once solved, becomes the foundation for living intelligence, a system that gets smarter, sharper, and more context-aware with every project.

At Knowledgeable, we're not treating disambiguation as a static problem to solve once. It’s a dynamic capability. Continuously improved through new data, better modelling, and constant feedback from real-world use.

What continuous refinement looks like

With each new paper ingested, each publication cluster validated, and each consultant interaction logged, the system becomes more accurate, more confident, and more tailored to the context in which it’s being used.

This means:

• Improved author resolution in crowded fields (e.g. oncology, inflammation, cardiology), where name duplication is high and signal-to-noise is low.
• Stronger temporal mapping of author impact over time: seeing how a KOL’s influence grows, wanes, or pivots between therapy areas.
• More accurate network mapping, as co-authorship, institutional affiliations, and semantic themes are refined to better understand influence and relevance.
• Greater adaptability across use cases, from stakeholder profiling to publication prioritisation to value message validation.

For consultants: Less effort, more clarity

Continuous improvement in disambiguation means less second-guessing, fewer rabbit holes, and faster time to insight.

• You won’t need to manually verify whether an author in your stakeholder map is the expert or just shares a name with one.
• You’ll spend less time digging and more time thinking.
• And you’ll trust that the outputs you’re building are based on real, strategic voices—not statistical noise.

As the system improves, so does your ability to deliver value faster, with more confidence, and with less mental overhead.

For clients: Deeper insight, stronger trust

Clients aren’t paying for pages of data. They’re paying for strategic clarity, and that clarity depends on evidence that’s credible, relevant, and defensible.

Ongoing refinement of author intelligence allows you to deliver:

• Clear, transparent sourcing of every insight.
• Strategic rationale built on who said it, not just what was said.
• Faster responses to client questions about source quality or KOL alignment.

In other words, it lets you operate not just as a service provider, but as a trusted thinking partner.

TL;DR

Disambiguation isn’t a one-time fix, it’s a long-term advantage.

The more you use the system, the smarter it gets: delivering clearer insights, stronger author intelligence, and less friction in turning data into decisions.