Exposure is not the same as consideration
It is tempting to view communication as a relatively linear journey: an HCP encounters a message, engages with it and, potentially, develops greater awareness or consideration of the treatment. Real-life prescribing decisions are rarely that straightforward.
Engagement metrics can tell us whether an audience encountered or interacted with a message. They do not necessarily explain whether that message changed how an HCP thinks about a treatment, resolved a concern or increased confidence in considering it.
Research into prescribing behavior consistently points towards a much broader decision environment. A systematic review of factors influencing prescribing decisions identified clinical condition, Patient preferences, physician characteristics, cost and pharmaceutical industry influence among the factors associated with prescribing decisions. More recent research continues to describe prescribing as a complex behavior shaped by influences at physician, Patient, practice, industry and wider system levels.
For Pharma, this means looking beyond whether an audience simply saw or engaged with a message. The more useful insight comes from understanding what HCP conversations reveal about how a treatment is being perceived and discussed.
Prescribing consideration is shaped by more than the product
When discussing whether a medicine may be appropriate for the patients they see, HCPs may consider factors such as the clinical evidence, current treatment options, tolerability, contraindications, administration, monitoring, local guidelines and practical considerations.
These considerations do not exist independently. Evidence from research into new-drug adoption has highlighted the importance of factors including treatment failure, adverse-effect profiles, pharmaceutical representatives, hospital consultants, Patient requests and practical experience. The same research found that personal clinical experience could play an important role in subsequent adoption.
This creates a significant challenge for brand teams. A campaign may communicate a scientifically compelling product benefit, but an HCP’s consideration of that benefit can still depend on whether it addresses the questions they actually have. A message can be accurate without being persuasive. It can be noticed without being meaningful, and its relevance can vary across HCPs and clinical contexts.
The missing layer is often found in HCP voice
This is where real-life HCP dialogue becomes particularly valuable.
HCPs do not necessarily express their prescribing considerations in the language used by brand teams. They may discuss a treatment indirectly, compare it with another therapy, raise concerns about a particular Patient population or describe a previous experience without explicitly stating, “This has increased my intent-to-prescribe.”
For example, an HCP might say: “I’d want to see more evidence in this Patient group.” Another might describe greater familiarity with a treatment following experience in practice. A third might focus on administration or access.
Traditional metrics can struggle to capture these distinctions because they are designed to measure specific interactions: impressions, clicks, reach, engagement, awareness, or stated responses.
Unstructured conversational data operates differently. It can contain context behind those responses, including language, themes, sentiment and associations that can help identify the reasons HCPs express for their views.
Turning unstructured conversation into prescribing signals
The opportunity is not to treat every HCP comment as a prediction of future prescribing. That would overstate what conversational data can tell us.
Instead, the opportunity is to identify the signals that sit closer to prescribing consideration: themes such as perceived clinical relevance, treatment concerns, differentiation, treatment experience, guideline context, and unmet needs.
This requires moving beyond simply counting mentions of a brand.
Natural language processing and domain-specific AI can help identify recurring themes across large volumes of conversational data while incorporating contextual information from those discussions. Instead of asking only how frequently a medicine is discussed, strategists can investigate how a medicine is being discussed, which themes commonly occur alongside those discussions, and where HCP perceptions appear to align or diverge from intended brand messaging.
What traditional campaign metrics may miss
Consider two campaigns with identical reach and engagement. On paper, they might appear equally successful. Imagine that HCP conversations associated with one campaign contain more positive discussion of the medicine’s potential role in an appropriate Patient population, while conversations associated with the other continue to include concerns about tolerability and differentiation. The campaign dashboards may look similar. The strategic implications are very different.
This is why the space between message exposure and prescribing consideration deserves greater attention. Understanding that space can help teams explore associations between recurring themes and aspects of treatment perception, such as awareness, comprehension, credibility, clinical relevance, experience or access. It can also reveal when the intended brand story is not the story HCPs are actually discussing.
From HCP voice to actionable intelligence with DrugVoice
This is the opportunity Talking Medicines is addressing with DrugVoice. DrugVoice transforms unstructured HCP and Patient dialogue into structured, actionable intelligence using domain-specific data science, AI and Natural Language Processing. Rather than relying solely on surface-level campaign metrics, it enables strategists to explore how HCPs are actually talking about medicines, brands and therapy areas. It is in making previously difficult-to-use data more accessible and interpretable.
The future is understanding the conversation before the prescription
Prescribing consideration is built through a combination of evidence, experience, context and confidence. Messaging is one part of that equation, but its impact depends on how it interacts with everything else an HCP already knows and believes.
For Pharma, that means the next generation of campaign intelligence cannot stop at exposure or engagement. The more useful question is what happens inside the conversation that follows. By listening to authentic HCP voice and structuring the unstructured, brand teams can begin to explore the factors shaping treatment perceptions and assess how closely their messages align with the issues and themes emerging in clinical conversation.
The goal is not to claim that conversation alone determines prescribing. It is to uncover the signals that help explain the journey from message exposure to consideration.
Discover what brand messaging resonates with HCPs and their intent-to-prescribe with DrugVoice. Read our most recent use case here, or feel free to get in touch to learn more.
References
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review
- Influences on prescribing decision-making among non-medical prescribers in the United Kingdom: systematic review
- Interactions between physicians and the pharmaceutical industry generally and sales representatives specifically and their association with physicians’ attitudes and prescribing habits: a systematic review
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review