Moving from reputation to reach: data-driven decision-making for Medical Affairs publication targets

Too often, familiarity and reputation guide target selection for scientific communications. Quantifiable asset performance metrics show the way to more effective publication targeting.

Compass Points: The Future of Medical Affairs is a series exploring the strategic challenges facing Medical Affairs teams in today’s communication landscape—and the tools that will help them get it right.

In the life sciences industries, submitting scientific communications for publication is the culmination of substantial investment. Assets such as research articles and conference abstracts provide valuable context to scientific and clinical data. This work underscores the data-driven mindset of modern Medical Affairs and publications teams.

Yet the selection of publication targets is often driven by qualitative, instead of quantitative, measures. Human bias easily informs journal and conference selection. When decisions result from long-established practice, from familiarity, or from reputation, they lack the fidelity of publication targeting based on defined metrics such as reach.

Publication performance metrics captured from integrated data sources help overcome these limitations. Data-driven choices about scientific communications publication targets are facilitated by measurement tools, such as Compass by Dimensions, characterized by user-defined benchmarking capabilities and publication performance tracking over time.

Incomplete data promotes biased decisions for scientific communications publication targets

For Medical Affairs and publications teams, selecting publication targets is a key element of evidence planning and communication to a variety of stakeholder audiences. Choosing the right publication target means that product-defining data are available to the intended readership, in accordance with strategically-defined time points in the product’s lifecycle. Adding to the complexity, data types, intended readership, and key product messaging will evolve over time. 

Furthermore, target selection encompasses a range of content types. This requires understanding which formats are appropriate for a given journal or professional meeting. Teams must decide where to publish peer-reviewed articles, reviews, communications, and other long-form structures, and where to submit primary and encore conference abstracts. Such decisions may still rely on only a limited number of traditional metrics; known examples include journal impact factor, average number of referencing citations per published work, or visits to a publication’s webpage. 

However, when availability of publication performance data is limited in this way, instead of thorough, metrics-grounded decision-making, teams turn to qualitative influences. Though provocative, the statement “Better the devil you know” characterizes a persistent, biased, and even emotional selection based on long familiarity with a journal, conference, or editorial team. Despite its ambiguous definition, the reputation of a given journal or meeting in the industry may strongly bias the choice of publication target. As described below, author preferences can also play a role.

Real-world example: which conference to choose?

A new pharmaceutical drug product is in its first year of launch in multiple EMEA markets. Further commercial launches are planned for this region over the next two years. While the therapeutic area is established within the global organization, the product’s treatment modality is new. In this critical lifecycle stage, the Senior Director, Scientific Communications is responsible for the region’s conference publication strategy.

All the elements are in place: robust Launch Excellence structures, the Integrated Evidence Plan, established relationships with Key External Experts (KEES). Up to now, however, the abundance and variety of relevant local-market and EMEA-regional conferences have been difficult to navigate. The KEES are highly engaged, sharing their own recommendations and interests for conferences to target. They expect that these interests will be taken well into consideration during conference decision-making.

How should scientific communications teams structure conference data submission? Going forward, how can our Senior Director colleague make data-driven decisions, instead of selections based on the company’s past conference attendance, or on stakeholder preference?

Without knowledge of reach, critical timelines and audiences are underprioritized

As explored in the first installment of this Compass Points blog series, reach is a quantitative measure of information propagating across channels or networks; for example, in news, social media, podcasting, or clinical commentary. It is a key performance metric for scientific communications.

When publications professionals lack the ability to select publishers, journals, and conferences based on reach, biased decision-making will play a greater role, often leading to delayed decision-making. This comes with risks: extended timelines correlate with increased project costs.

This is magnified when a first submission target becomes unfeasible due to gratuitous edit requests, rejection, or other factors, and the publications team must decide their subsequent target. Elsewhere, lack of reach data may also blind teams to valuable time-dependent opportunities such as early-bird submission mechanisms, or journals’ thematic special editions.

Meaningful reach metrics can therefore support critical project timelines, as well as greater understanding of publication audiences. Today’s audiences are more segmented than ever. Along with parameters such as geography or profession, the readership may represent an intended publication audience, a strategically targeted audience, or a novel audience.

Without comprehensive performance data such as reach metrics, it is more difficult to assess whether a critical audience has connected with a publication. The importance is in the name; without knowledge of reach, Medical Affairs and publications teams lack quantitative confirmation that audiences they seek are, in fact, seeing their research and development outputs.

Additionally, journal articles and conference abstracts may have unrecognized audiences of strategic interest to the Medical Affairs organization. Such groups are varied, and the medical strategy will define their role as (potential) stakeholders. Underappreciated audiences could include physicians in other therapeutic areas, nurses, pharmacists, allied healthcare professionals such as medical technicians, government and regulatory personnel, and patients and patient advocacy. It may be challenging to uncover these relevant groups without publication performance data such as reach. Missing these audiences limits publication influence. 

Metrics-grounded target selection supports Medical Affairs across the product lifecycle

In the life sciences industries, product lifecycle spans laboratory evaluation and clinical development, through regulatory approval and commercialization, followed by ongoing product support through post-marketing surveillance, real-world evidence generation, and curation of deeper business insights. As Medical Affairs’ strategic remit continues to expand across the lifecycle, publications teams are responsible for a growing scope of outputs.

With this increase in quantity comes increased complexity. As pharmaceutical drug products and medical technologies move through their product lifecycles, the scientific communications strategies supporting them must necessarily evolve. This occurs as the product profile matures, as further scientific and clinical data become available, as target audiences or geographies shift. Advanced preparation for these shifts allows the publication team to adapt efficiently. That advanced preparation derives in part from reliable publication performance data captured from trusted sources.

Whether maintaining existing publication targets or exploring new opportunities, the ability to track performance data over time promotes efficient decisions about data submission. The parameters that a given Medical Affairs team will prioritize will vary, but useful readouts include a journal’s length of peer review (from submission to publication), number of citations per published work per time period, and quantitative information about reach, engagement, and impact.

In this example, if length of review is steadily increasing, if newer publications are securing fewer citations than historical examples, and if quantitative altmetrics are decreased, it may be time to adjust the target. Clearly-defined, quantitative metrics promote efficient adaptation and serve the product lifecycle.

Such scientific communications performance data supports Medical Affairs in other strategic settings, too. Valuably, information about publication reach, engagement, and impact is available beyond the organization’s current products. This can reinforce market awareness and team readiness when moving into a new product modality or therapeutic area. As performance data are not limited to owned assets, key metrics may be tracked for competitor outputs. Understanding competitor publishing preferences for their long-form works and conference abstracts provides meaningful competitive intelligence insights to Medical Affairs and cross-functional colleagues.

When choosing publication targets, data-driven decision-making is capable decision-making

Now more than ever, performance metrics influence decisions about where, when, and how to publish in the life sciences industries. Scientific communications professionals see the importance of publication targeting based on benchmarks, and on performance data that is trackable over time. The ambiguities and delays of biased, emotionally-driven choices give way to traceability and efficiency.

Meanwhile, senior leadership are counting on metrics-grounded publication targeting that quantifies its value both for the readership and for the business. Using built-for-purpose measurement tools, the ability for Medical Affairs and publications teams to get this right underscores their strategic role as business partners within the organization.

Compass was designed to support data-driven decision-making for publications targeting. Built on Dimensions and Altmetrics data, Compass combines publication and altmetrics into a single collaborative workflow, simplifying how Medical Affairs teams benchmark, track, and manage publication impact and reach.

Compass by Dimensions is developed by Digital Science, an AI-focused technology company that transforms fragmented data into unified knowledge assets, leveraging AI and Knowledge Graphs to deliver structured, actionable intelligence for high-value discovery and innovation. By combining unparalleled data depth and breadth with enterprise-ready AI technology, we help leaders confidently accelerate product life cycles and secure a decisive market lead.

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From Vision to Velocity: Architecting a Functional AI Strategy for Medical Affairs

How are organizations architecting functional Artificial Intelligence (AI) strategies that will shape the next phase of AI in Medical Affairs?

Earlier this year, Digital Science brought together Medical Affairs leaders to explore how they’re approaching AI, and why they need to embed sustainable AI capabilities in day-to-day workflows. 

The conversation covered six key topics:

  1. Building trust and driving user adoption
  2. Managing the volume and quality of scientific insights
  3. Bridging the gap and demonstrating ROI 
  4. Governance, safety and regulatory alignment
  5. Systematic literature review and methodological rigour
  6. Emerging use cases

The journey from AI vision to operational velocity in Medical Affairs is one of deliberate, incremental progress rather than a single leap. The organizations making the most tangible progress are those pairing ambitious use cases with disciplined governance, keeping humans in the loop, insisting on provenance, and investing in the change management that turns adoption from an aspiration into a habit.

As AI capabilities mature and expand into new areas, from MSL training to patient journey research, the role of Medical Affairs will continue to evolve. Success will depend not on any single tool or model, but on the strategic architecture connecting people, processes, data and technology. By defining clear use cases, aligning stakeholders, protecting scientific integrity and demonstrating measurable value, Medical Affairs teams can indeed move from vision to velocity. And, therefore, lead their organizations in shaping how AI is applied responsibly in the service of science and patients.

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How a lab came undone

The disinformation tax is the compounding cost of relying on findings that are false but not yet corrected, or corrected but not yet unlearned.

To understand this, we look at former Cornell researcher Brian Wansink, who published hundreds of studies on why we eat the way we do. But his research came under scrutiny, and the flags started — eventually touching 45 of his papers.

Here we track one piece of that tax: the citations his flagged papers kept collecting after a problem was already on the record.

Each vertical line is one of his flagged papers, in the order they were published. Grey and blue ticks are citations — blue when the citation comes from Wansink himself.

↓ Scroll to begin
accumulated
0
total citations
the tax
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citations after a flag
from within
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self-citations after a flag
PubPeer
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Corrections
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Exp. concern
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Retractions
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Where it lands

The record kept spreading, even after the flags

0
total citations to the 45 flagged papers
0
of them made after the paper was flagged
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self-citations after the flag — the lab stopped citing itself

Lines are ordered by publication date; a line's colour changes to the flag it received — green PubPeer, orange correction, purple expression of concern, red retraction — and a $ marks papers linked to a funding grant. The vertical scale is stretched across 2012–2022, where most flags fall. Citation ticks and all counters are year-resolution. Sources: Dimensions.ai and the Retraction Watch database. This page reports the public record and alleges no wrongdoing beyond the recorded editorial actions.

See the detailed interactive ledger →

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Reducing the Hidden Administrative Cost of Open Access

International Open Access Week 2026 runs from October 19 to 25, with this year’s theme, “The Cost of Knowledge,” asking a pointed question: why does access to knowledge keep getting more expensive, and who actually benefits?

Much of the conversation around this theme, understandably, centers on money: subscription costs, article processing charges, and the growing consolidation of scholarly publishing under a small number of commercial players. But there is a second cost sitting underneath the financial one that rarely gets its own headline: the cost of time. The hours researchers, librarians, and administrators spend on the manual work that openness still demands of them: depositing papers, chasing compliance, re-entering the same metadata across multiple systems, and so on. And this is the cost Symplectic Elements, part of the Digital Science portfolio, has spent over 15 years working to reduce.

What a decade of research tells us

The State of Open Data 2025 report, marking 10 years of research into researcher attitudes toward openness, gives a clear picture of where this labor cost lands hardest.

Sixty-nine percent of researchers still say they receive too little credit for sharing their data. That figure has improved since 2020, when it stood at 78%, but a clear majority of researchers still feel under-recognized for their work. 

Awareness of open principles has increased meaningfully. For example, awareness of the FAIR principles, which call for data to be Findable, Accessible, Interoperable, and Reusable, rose from 15% in 2018 to 40% in 2025, reflecting a significant shift in how researchers understand open data. Yet understanding these principles does not, on its own, ensure that publications are deposited or funder policies are followed.

Without the workflow support to put them into practice, open principles become another expectation placed on already stretched researchers and administrators. Meanwhile, institutions are navigating a growing array of mandates from funders, governments, and internal policies, each with its own requirements. The resulting complexity falls directly on the people responsible for meeting them.

A history rooted in reducing that burden

This is not a new problem for the sector, and Symplectic Elements’ own history reflects a long-running effort to address it. In 2009, Elements became one of the first commercially available research information management systems to be open-access-aware, connecting directly to institutional repositories, beginning with DSpace and later extending to EPrints, and Figshare, which would go on to become central to how deposited research is shared and discovered.

In 2011, as open access mandates expanded across Europe and Australia, the Repository Tools module in Symplectic Elements broadened that repository integration further. Then in 2012, Crossref was introduced as a data source, giving institutions access to Creative Commons CC0 metadata they could reuse freely, removing another manual step from an already labor-intensive process. By 2015, OA Monitor had launched within Elements, turning compliance tracking from a spreadsheet-based task into a clear, and easy to use interface that institutions could act on. 

Where this stands today

Symplectic Elements’ OA Monitor has continued to evolve alongside the complexity it was built to manage. Researchers can deposit their work with the push of a button, while intelligent data harvesting works continuously in the background, matching outputs and populating records without requiring manual input.

The shift this represents is substantial, as reflected in 20 years of institutional experience with Elements. Before this kind of automation existed, institutional teams often spent years manually validating and maintaining the research profiles of thousands of faculty members, checking records one by one for accuracy. The ability to automatically associate and claim dozens or hundreds of activities through a single researcher ID removed that burden almost entirely. At an institutional level, the effect shows up as less manual checking, fewer inconsistencies, and research outputs appearing in institutional systems sooner, giving institutions a stronger foundation of accurate, visible research data to build on.

Because institutions rarely operate under a single policy, OA Monitor supports multiple, independently configurable policies tracked in one place. Rather than surfacing compliance gaps after the fact, it provides real-time insight that allows institutions to identify and address those gaps as they emerge.

This visibility carries weight beyond compliance reporting. Understanding who is doing the work, and how much of it, is a meaningful step toward narrowing the recognition gap that the State of Open Data findings continue to highlight.

An ecosystem built to close the loop

None of these functions operate in isolation: this is where Figshare plays a defining role. Once a deposit clears compliance within Elements, Figshare is where that work becomes discoverable and citable, closing the loop of the credit gap felt by researchers.

Loughborough University’s experience shows what that looks like in practice. Having used Figshare as a data repository since 2014, the university restructured it in 2018 into a single, all-in-one repository covering data, journal papers, theses, and media, migrating away from a separate DSpace system. The rationale was direct: meeting open access requirements, keeping research publicly accessible, and fulfilling REF conditions. Consolidating into one system also brought cost savings for IT Services and allowed staff who had previously worked separately on either publications or data to work as a single, more flexible team. The bidirectional integration between Figshare and Elements was built specifically to support REF 2021 Open Access policies and reporting, and the full migration took about a year from start to finish.

Elements also connects to other institutional repositories including DSpace and EPrints, to publisher APIs and open databases such as DOAJ and PubMed Central, and to researcher identifiers including ORCID, Crossref, and Dimensions. Deposit, discovery, and credit only function as a system when working from an integrated infrastructure.

While institutions cannot set publisher pricing or reshape the economics of scholarly publishing this Open Access Week, the labor cost sitting behind “The Cost of Knowledge” theme is one they can act on directly, through the systems and workflows that make openness something researchers can comply with rather than struggle against.

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Citations are only part of the picture. Life Sciences needs the whole story.

Citations are a lagging indicator, but conversations start on day 1. Only alternative metrics can tell Medical Affairs teams if research is landing in real-time.

In the age of AI, research intelligence has to begin with trusted data. This series, Inside Our Data, explores the foundational data infrastructure that makes trust possible. 

Medical Affairs teams are increasingly under pressure to act on emerging evidence and consistently demonstrate value to stakeholders, but this is impossible with citations alone. Citations can take years to manifest and capture only one aspect of a publication’s impact: its resonance in the world of academia. 

To capture a holistic view of a publication’s performance and find new opportunities in publication strategy, teams have to integrate alternative metrics, often called altmetrics, to understand where research is gaining influence today. Altmetrics encompasses broader, more horizontal signals that bubble up across analog and digital third spaces, such as policy documents, social media, broadcast media and other peer-to-peer communication forums. When it comes to capturing a robust picture of a publication’s influence in real time, there’s no substitute. Unlike citations, altmetrics can help teams find direction and set measurable goals from the beginning of the publication cycle. 

That teams are expected to do a lot with a little isn’t new. What has changed in recent years is that budget holders increasingly understand citation-based metrics alone aren’t enough. Leadership teams have come to recognize that the conversation about research is now happening in more places, and on a faster timeline, and there’s no reason to think this trend will slow or reverse. 

That means now is the time for teams to work out their altmetrics set-up so they’re best placed to capitalize on relevant community activity, maximize a publication’s impact, and demonstrate that value upwards.

Tracking real-world impact with altmetrics

In the last few decades, advances in technology have made it much easier to share, converse about, and learn from research emerging all over the world. As journals moved online, so too did Life Sciences communities; Key Opinion Leaders (KOLs) and Healthcare Practitioners (HCPs) alike found community and conversation that helped them learn about new research and potentially inform their therapeutic behavior. 

At the same time, the definition of KOL got wider. Beyond the traditional clinician-researcher, a distinct category of communication specialists has developed in recent years. These might be clinicians who focus on translating and communicating research rather than producing it, for example. Patient advocates have taken on a similar role, particularly in oncology, where major congresses now build in dedicated spaces for patient participation. In rare disease areas, that role often falls to parents of patients, who can become de facto experts in fields where the total body of published research may only amount to a handful of papers a year.

Knowing who is saying what about research provides invaluable insights which are critical for maximizing both the academic and real-life impact of a publication, and citation data alone is incapable of capturing the volume or nature of that community and conversation. But identifying and tracking these different populations is an ongoing process, and it varies by therapeutic area—teams need to know both who they’re looking for and what they’re looking for. To date, this was a task too complex and time-consuming to perform reliably, and this meaningfully hindered how Medical Affairs teams were able to perform. 

This is what led Digital Science to invest in Altmetric, a platform which uses academic, non-academic, governmental, and professional sources to build a more complete picture of research impact. Its technology tracks, collates, and disambiguates shares and mentions of articles, books, reports, grey literature, and more, making it possible to track specific KOLs and analyze sentiment within specific communities. Data on researchers—job function, professional expertise, social media activity, location, career history—is blended with social and attention data to build a fuller picture of a given KOL, including how and where they’re active outside formal publication. Altmetric continues to expand its scope, having recently added seminars and readership information to its datasets. 

The result is a clearer picture of who is reading what research and what they think about it, so teams can tailor publication plans as new opportunities arise.

Alternate timelines: where citations and altmetrics diverge

We know that research can have an extremely long shelf life. In fact, the challenge faced by Medical Affairs teams over the last 20 years—tracking both citations and altmetrics—results at least in part because these two measurements work on vastly different timescales. 

Citations can emerge years after publication, but they form a critical part of impact measurement and planning for later publications. Citations are the best way to demonstrate high-level academic impact and they grant legitimacy to the research and the teams who carry it out—whether they’re published a year on or more. 

Contrast this with altmetrics, which can emerge from day 1 and can ebb and flow dramatically over the life of a publication—and where each such instance can provide teams the opportunity to capitalize if they can move fast enough. Social media conversation can begin on the day of publication; podcasts often emerge at least a year later, and policy and guidelines citations typically materialize two to three years after publication. Where conversation is ongoing, sentiment can shift through time, creating opportunities for teams to engage with communities and refine strategy. Reliably tracking these instances manually is essentially impossible, but this low-frequency activity is the best way to understand real world impact. 

These two measurements capture both sides of the same coin, and teams need both to know exactly how research is performing. But checking for citations weekly might turn up no results for a year or longer. And if a team only checks altmetric monitoring once per week, or overlooks a rising KOL, they could miss a critical flash in the pan. 

To solve this problem, Altmetric tracks thousands of online mentions plus clinical guidelines and policy citations across social media, mainstream media, policy, patents and more all in one platform, and Altmetric Explorer centralizes that data so teams can easily and consistently monitor both citation and altmetrics for signals that could help shape strategy. This way, teams can pivot as conversation naturally evolves through time—despite the different timelines such conversation can take to develop.

Measuring real-life impact and demonstrating value with Digital Science

Medical Affairs teams are under immense pressure to act quickly—and to be able to explain their decisions and learnings as they go. They need to understand the real-life impact research is having, and how it is best measured, to make informed decisions.

To make this possible, Altmetric provides:

  • 280M+ Altmetric data points within the Dimensions ecosystem
  • real-time attention records for individual scholarly outputs
  • academic and mainstream source coverage
  • clear attention and engagement signals 

Altmetric data is continuously refined and curated by Digital Science teams to ensure fit and relevance. Digital Science has existed for nearly 20 years, so its data aggregation and curation processes are time-tested, ensuring the Altmetric data teams have come to rely on is still market-leading in breadth and completeness. 

This is how the Altmetric score has taken on industry-wide importance. This score has existed for over a decade, and the interoperability of the dataset has proven value across research areas, making the Altmetric score a reliable, trustworthy indicator of performance. This is useful not only for benchmarking across competitors or research areas, like teams can do with Digital Science’s new platform, Compass, but also to help with upward reporting.

By putting a number on research attention—a single, clear metric to track and compare—teams can easily demonstrate value to stakeholders. This is more specific than broad social listening—which is characterized by fuzz and non-specific noise—and instead captures real, relevant sentiment which ensures teams can actually compare apples to apples with clean, consistent data. 

This is important because raw attention numbers can only go so far on their own: oncology and pandemic-era research naturally draw disproportionate attention compared with rare disease research, so that number alone doesn’t tell a team whether their result in another therapeutic area is actually good. Being able to compare a consistent measurement, the Altmetric score, against publications within the same therapeutic class is what makes the number meaningful.

In 2025, Digital Science built sentiment analysis into its ecosystem. Adding sentiment analysis on top provides an unprecedented qualitative layer to this measurement. Teams can measure real-world impact, and how it changes, through time, adjusting activities to better target certain KOLs or communities from day 1. This sentiment tracking is focused on scientific and clinical attitudes that form towards research, and is tied to the specific publication rather than overall brand or company sentiment. This distinguishes it from the broader social listening that pharma companies often run at the brand level. 

See where research is gaining influence before citations catch up

Citations take years to materialize and only capture a slice of the impact research has. Stakeholders recognize there’s more to performance measurement than citations, but there hasn’t been a suitable proxy for a holistic view of impact until now.

Altmetrics is the other piece of the puzzle, reflecting the real-world, real-life impacts of a publication in the conversations of KOLs, HCPs and policymakers, and in the lives of patients. To maximize publication performance and demonstrate value to stakeholders, Medical Affairs teams need both. 

This is what Digital Science’s data ecosystem is built for: connecting the academic record with real-world attention so that Medical Affairs teams get one continuous view of a publication’s impact. Altmetric captures this influence layer by tracking millions of data points to give teams a real-time view of the conversation surrounding given research, and it’s one part of a wider toolset that Digital Science provides to enrich the entire research lifecycle. This layer provides crucial context for the burgeoning use of agentic AI in Medical Affairs; it also allows teams to make informed use of emerging evidence and act quickly—all with the confidence they’ll be able to explain their decisions and prove their value upwards.

Digital Science provides one of the world’s broadest collections of connected research intelligence—combining Dimensions, Altmetric, and IFI CLAIMS to help enterprise organizations with strategic decision-making, innovation, and AI-enabled workflows with intelligence that can be explained, audited, and defended. Request a demo to learn more about how Digital Science could revolutionize your team’s publication planning.

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Moving from reputation to reach: data-driven decision-making for Medical Affairs publication targets

Too often, familiarity and reputation guide target selection for scientific communications. Quantifiable asset performance metrics show the...

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