Medical information analytics can shape pre-launch strategy by extracting intelligence from existing MIR data — on analogous therapies, previous launches in the same disease area, or early compassionate use and early access programme inquiries — before the first formal inquiry on the new product is ever received. MIR Analytics from WPP Enterprise Solutions is purpose-built for this kind of forward-looking intelligence application, enabling MI teams to move from reactive launch participants to active contributors to launch readiness.

The strategic logic is straightforward: HCPs ask remarkably similar questions across products in the same therapeutic area. Dosing complexity, administration concerns, patient selection criteria, drug-drug interactions, and off-label scenarios follow predictable patterns within a disease class. By reading MIR data from analogous products at scale, MIR Analytics can predict — with evidence-based precision — what HCPs will ask when the new product launches, and how urgently those questions will need to be addressed.

How MIR Analytics supports pre-launch strategy across five activities:

Medical Information Request data plays a uniquely important role in evidence gap identification because it captures a signal that no other data source generates: the direct articulation by HCPs and patients of where the current label or clinical evidence base fails to answer their clinical questions. When an HCP contacts medical information with a question that cannot be answered from the current label, that is not just an inquiry — it is an evidence gap signal. When multiple HCPs ask similar questions repeatedly, that pattern constitutes a structured, real-world evidence gap dataset that Labelling and Regulatory teams can act on.

MIR Analytics from WPP Enterprise Solutions converts these signals into actionable intelligence for labelling and regulatory strategy, transforming the MI function from a downstream operational service into a proactive contributor to label evolution planning.

The evidence gap identification flow:

MIR Analytics supports labelling and regulatory strategy through four specific capabilities:

• Signal Detection for unmet evidence needs — monitors for repeated inquiry patterns around specific clinical scenarios, patient populations, dosing regimens, or safety topics that the current label does not fully address. The frequency and clinical context of these patterns form the evidential foundation for label evolution conversations.
• Clusters highlighting HCP uncertainty zones — identifies where inquiry volume concentrates around specific topics, revealing the areas of greatest clinical uncertainty and providing Regulatory teams with a data-led map of where label updates would deliver the most immediate HCP value.
• Cross-Functional Insights for Labelling and Regulatory stakeholders — delivers curated evidence-gap intelligence formatted specifically for label review teams and regulatory affairs professionals, with clear rationale, inquiry evidence, and strategic context.
• Report Generation for structured evidence-gap briefings — produces formal documents presenting evidence gaps in a structured, audit-ready format suitable for regulatory submission support, label review committees, or cross-functional strategy sessions.

At launch, Medical Information Requests are the fastest available real-world feedback channel in the pharmaceutical organisation. Unlike clinical trial data, field medical reports, or sales analytics — all of which have inherent reporting lags — MIR data arrives in real time, directly from the point of clinical practice. When an HCP calls medical information with a question about a product they have just started prescribing, that question is an unfiltered, unsolicited signal about how the product is being understood, used, and experienced in practice. At scale, these signals map the complete landscape of adoption barriers before any other data source can.

MIR Analytics from WPP Enterprise Solutions captures this landscape as a live signal feed — continuously reading every incoming MIR and translating it into structured adoption barrier intelligence that launch teams can act on in real time.

The four adoption barrier types that MIR Analytics identifies at launch:

How MIR Analytics enables rapid response to adoption barriers at launch:

• Signal Detection on early friction themes — identifies rising, quiet, and off-taxonomy barrier signals as they emerge, enabling the launch team to respond before a single barrier pattern has grown large enough to materially impact uptake.
• Clusters grouping barriers by type — groups related inquiries into coherent barrier themes, making it possible to distinguish between a reimbursement problem, a handling confusion, a clinical question, and a safety perception concern — each of which requires a different organisational response.
• Cross-Functional Insights with immediate routing — access barriers go to Market Access and Commercial; dosing confusions go to Medical Affairs and field training teams; safety-adjacent signals go to Safety; clinical understanding gaps go to MSL and Medical Communications. No manual triage required.
• Action Generator for specific, rapid responses — for each barrier type detected, the Action Generator proposes concrete content, education, or engagement responses — shortening the gap between barrier detection and barrier resolution.