Every strategic decision starts with insights. Insights provide direction, back up strategic thinking, and inform confident decision-making – but these decisions will only ever be as good as the insights that inspire them. Whether it stems from selective engagement, hierarchical groupthink, or human assumptions, bias remains one of the most persistent risks in pharma insight gathering.
“The lengthy, risky, and costly nature of pharmaceutical research and development (R&D) makes it vulnerable to biased decision-making.” –Benjamin Weber et al., via National Library of Medicine
In this article, we discuss the issue of bias in pharma insights and explore how teams can effectively remove bias from the insight-gathering process.
Where does bias come from?
Bias can occur in a number of forms throughout the insight-gathering process. In the current geopolitical landscape, it’s tempting to think of bias as something intentional – a conscious effort to skew results a certain way, exclude a particular group, or rubber-stamp a specific line of thinking. In fact, bias is often much more subtle, much less voluntary, and much harder to detect – but with no less impact on launch success.
Unconscious bias
Harvard University defines unconscious bias as both “unintended, subtle, and subconscious associations learned through past experiences” and “thoughts that happen to all of us, and that we are unaware of on a conscious level.” We are all subject to these unconscious biases. In the insight-gathering process, they can have an impact at both the input and output stages. At the input stage, our biases may influence the kinds of questions we ask during a virtual advisory board, as we involuntarily seek answers that align with our initial hypotheses. During the output phase, these biases may lead us to overvalue data and insights that uphold our opinions and expectations.
“Optimism bias occurs when decision makers are overly optimistic about the outcome of planned actions, including overestimation of the frequency and size of positive events and underestimation of the frequency and size of negative ones.”–Sandra Visser, ASCPT, SVP, Head of Therapeutics Development, Alltrna
Overdependence on familiar voices
Bias isn’t just about upholding preconceived ideas—it’s also about looking at a subject through too narrow a lens or failing to consider the full picture. These limitations often arise during the KOL engagement phase, where pharma teams tend to rely on the same small cadre of experts for insights. Typically, these are individuals with the longest list of congress speaker engagements or the most publications on PubMed.
While these respected experts offer valuable perspectives, they represent only one part of a much larger network—one that may also include patients, advocates, HCPs, and KOLs who do not speak or publish via traditional channels. Engaging these lesser-known experts is crucial to understanding the full picture of a disease community.
Hierarchy and groupthink: when the loudest voices drown out insight
The life sciences are intrinsically hierarchical. While such structures can help establish a clear chain of command and ensure accountability, they can also prevent valuable opinions from being heard. This ties into the ‘babble hypothesis’—the theory that we tend to pay more attention to those who talk most and loudest.
In an advisory board setting, these dynamics may lead some advisors to remain silent for fear of upsetting the hierarchy or contradicting a more senior colleague. As a result, the insights may appear to reflect the full spectrum of expert opinions when they actually echo the views of a select few.
The impact of bias
In short, bias in insight gathering diverts teams from the pursuit of objective truth. Instead, they may align around a version of reality that reinforces established thinking or reflects only a narrow range of influential voices.
“60% of medical affairs leaders say bias can be an issue when reporting on or sharing the results of insight-gathering activities.” –Reuters Events Pharma
While insights should support established knowledge, they must also challenge assumptions and present new perspectives that enable agile shifts in launch strategy. If insights are biased, they may be incomplete or misleading—more misinformation than meaningful intelligence.
How to remove bias from insight gathering
Society continues to grapple with how to eliminate bias from human thinking. While this may never be fully possible, we can minimize bias in the insight-gathering process—and technology plays a critical role.
Diversifying expert opinion through network analytics
When identifying experts for advisory boards or other pre-launch engagements, network analytics can uncover the “invisible college” of experts operating within a disease community, along with their connections and influence. Rather than relying on the same small group, teams can use these tools to engage a broader, more representative group of influencers.
Anonymizing input and capturing insights with asynchronous virtual engagement
Many issues related to hierarchy and the babble hypothesis can be mitigated through asynchronous virtual engagement platforms. These tools allow advisors to respond anonymously, increasing the likelihood they’ll share honest, independent opinions—even those that contradict more senior colleagues. Some platforms also offer translation capabilities, enabling insights from a more globally diverse set of experts.
Establishing objective truth through AI-powered insights reporting
AI can remove human subjectivity from data analysis. AI-powered insights reporting evaluates all available data to identify patterns and connections—not just those your team may expect or unconsciously prioritize. This ensures a more complete, unbiased picture of the market landscape.
Conclusion
Within3 can help you minimize bias throughout the insight-gathering process. Stop relying on incomplete data and start engaging with the full picture—book a demo with Within3 today.
