Purpose-built for Medical Information

The Intelligence & Insights Inside Your MI Data, Finally at the Speed Your Organization Needs.

Medical Information Requests contain some of the most strategically valuable, and most underutilized, intelligence & insights in a life sciences company. MIR Analytics is purpose-built to change that. 

Request a Demo

Medical Information Hears What No Other Function Can

Consider what Medical Information receives.
A physician asking about a dosing. A pharmacist seeking advice about product handling under real-world conditions. A patient describing an issue in their own words. A caregiver asking what to do to get the best for their loved one.

These interactions are unsolicited. They’re not the result of planned field visits, guided advisory boards, or structured market research. They reflect the immediate, unfiltered informational needs of the people who are actually prescribing, dispensing, administering, and living with your therapies. That distinction matters.

While colleagues in other functions usually engage with key opinion leaders who specialise in defined areas, MI hears from the full clinical breadth: practicing physicians navigating real patient scenarios, community pharmacists encountering day-to-day product issues, patients and caregivers describing genuine experience. It’s a channel that reaches stakeholders most other functions do not — and it captures the kind of grassroots, unprompted insights that cannot easily be generated any other way.

Analysed properly, that data reveals patterns that are highly relevant to functions across the organisation:

  • Evidence gaps that inform labelling strategy
  • Real-world usage behaviours that shape scientific messaging
  • Safety-adjacent signals that warrant cross-functional review
  • Unmet needs that can guide R&D decisions long before they become obvious through other channels

The strategic case for MI insight has never been stronger. The gap between the data that exists and the value actually being extracted from it has also never been more visible.

The Gap Between What MI Captures and What Organisations Actually Use

Many MI teams are managing that gap through a combination of effort, expertise, and process that was never designed to scale.

Standard reporting focuses on what is easiest to measure: inquiry volumes, response turnaround times, channel distribution, and category breakdowns. These are legitimate operational metrics. They do not, however, answer the more important question: what are all of these requests actually telling us about product experience, stakeholder needs, and what may be coming next?

Answering that question typically requires significant manual work — reviewing request narratives, cross-referencing disparate data sources, working within classification structures that were built to organise requests efficiently, not to discover meaning within them. The result is a reporting process that is retrospective by design, producing a picture of what happened last month rather than what is forming right now.

The problem compounds at the boundaries of the MI function. Even when insights are identified, packaging and sharing them with Safety, Medical Affairs, R&D, Commercial, Labelling, and Publications teams in a form those functions can act on is its own challenge. Valuable intelligence often reaches the rest of the organisation too slowly, too selectively, or too late for it to influence decisions at the right moment.

Ask Yourself:

  • How much expert MI time is currently spent assembling, sorting, and reformatting data, rather than interpreting what it means?
  • If a new issue were quietly forming across different HCP types, patient groups, and geographies, would your current approach surface it before it became obvious?
  • Are you only finding what your existing classification structure was designed to capture?
  • How consistently does strategic MI intelligence reach Safety, R&D, Commercial, and Labelling teams with enough lead time to act on it?
  • Are the KPIs MI reports to leadership accurately reflecting the function's strategic contribution, or primarily its operational performance?

If those questions are familiar, it may be time to look at MIR Analytics.

An AI Layer Built Specifically for Medical Information

MIR Analytics sits alongside your existing MI platform. It reads and analyses MIR content at scale — not just metadata and volume, but the actual substance of requests. It identifies themes, clusters, and emerging patterns across your data. It surfaces signals that manual review and standard dashboards are not equipped to find. And it generates insight-led outputs quickly, making that intelligence accessible to the people across your organisation who need it.
MIR Analytics does not replace your MI system. It does not manage inquiry workflows, automate response generation, or operate as a contact centre tool. Its sole focus is: helping you extract substantially more strategic value from the data your MI function is already collecting — and ensuring that value reaches the teams across your organisation who can act on it.

What MIR Analytics Changes

From Fixed Reporting to Ongoing Intelligence

Traditional MI reporting cycles describe what happened. MIR Analytics shifts the orientation toward what is happening and what is likely to happen — identifying themes as they develop, surfacing early signals before they reach significant volume, and enabling MI teams to brief internal stakeholders based on current intelligence rather than the previous period’s retrospective.

Beyond Taxonomy, Beyond Volume

Fixed classification structures are valuable for organising and tracking requests. They are less well suited to discovering what those requests contain. MIR Analytics reads MIR content directly — identifying patterns across categories, surfacing themes that fall outside existing classification structures entirely, and detecting the kind of quiet, recurring signals that are easy to miss when analysis is built around predefined buckets and volume counts.

Strategic Visibility Across Functions

MI’s insight value does not stop at the boundaries of the MI function. MIR Analytics is built to help that intelligence travel: to Safety teams assessing emerging signal patterns, to R&D teams mapping evidence gaps and unmet needs, to Commercial teams understanding real-world product experience, and to Sci Comm teams tracking what stakeholders are asking that current evidence does not yet fully address.

Multiple Capabilities. One Integrated Analytics Layer.

Eight powerful capabilities designed to transform your MI data into strategic intelligence.

Headlines

At any moment, across any timeframe, surface the top 5, 10, or 20 most important insights from your MI data. Not just a list of themes, but a coherent narrative that tells you what your data is actually saying — and why it matters right now.

Signal Detection

The most strategically important signals in MI data are rarely the loudest. Signal Detection identifies what standard analytics cannot: entirely new in-world topics, unexpected off-world requests that fall outside any known category, themes rising quietly in low volume, patterns approaching a previously seen peak or trough, and sudden spikes on established topics. Early, unprompted, and without requiring you to know what to look for.

Cross-Functional Insights

Automatically identifies the MI insights most relevant to the specific needs of different internal functions, together with clear rationale explaining why each MIR matches that need — speaking directly to their priorities rather than delivering a generic summary.

Action Generator

Translates what your MI data is revealing into actions your organisation should explore. Bridges the gap between insight identification and the decisions those insights should drive.

Clusters

Groups MIRs automatically into natural themes, driven by machine intelligence, with the flexibility to define your own topics and subtopics. Structure the analysis around what matters most to your team — not just what your existing taxonomy was built to find.

Topics / Areas of Interest

An AI-defined taxonomy applied consistently across all MIRs, sitting alongside your existing classification structure to provide a stable, reliable analytical foundation. Ensures consistency across teams and markets, improves the accuracy and comparability of outputs, and makes it meaningful to track how themes develop over time regardless of how requests were originally categorised.

Report Generation

Generate high-level executive summaries, tailored to a specific audience or question. The right format, for the right stakeholder, without the manual effort.

Answer / AI Chat

Ask questions of your MI data in plain language and receive immediate, evidence-based answers. No waiting for the next report cycle. Queries can be saved and revisited over time, building a knowledge trail that makes recurring analysis faster and allows teams to track how the picture changes.

The Right Intelligence. At Every Stage of the Product Lifecycle.

MI’s intelligence value is not uniform across the product lifecycle. It evolves — and the opportunity to act on it varies at each stage.

Pre-Launch

Maps the questions that will define readiness. Informs SRD development, shapes medical communications strategy, and pressure-tests the launch narrative before the first external inquiry arrives.

Launch

A live, real-time signal feed. MI hears friction as it forms — from access barriers to early off-label interest. These signals often appear in MI before they surface anywhere else. Speed to insight is directly linked to speed of response.

Growth & Maturity

Reveals where education gaps persist, where adoption barriers are concentrated, and how stakeholder concerns are shifting. Guides content strategy, field enablement, and patient support initiatives.

Label Expansion & New Data

Shows what stakeholders are trying to understand next, and where evidence or communication clarity is currently missing. Directly relevant to labelling strategy, RWE, and regulatory preparation.

MIR Analytics accelerates the path from raw inquiry data to structured intelligence at every one of these stages — ensuring that the insight MI is uniquely positioned to generate reaches the teams that need it in time to influence decisions.

One Signal Source Is a Clue. Three Is Confidence.

No intelligence channel is fully informative in isolation.

MI

Hears unprompted questions across the full stakeholder spectrum

Safety

Captures what is being reported as potential adverse events or product quality issues

Field Medical

Surfaces what is being raised in deeper scientific conversations with specialist prescribers and KOLs

Individually, each channel provides a partial picture. Combined and cross-referenced, they enable the kind of validated, multi-source intelligence that supports confident and timely decision-making across the organisation.

MIR Analytics is built to support that triangulation. It clusters MI intelligence and packages it in a form that is straightforward to cross-reference with all the other insights your organisation brings in. When MI, Safety, and Field Medical are all seeing the same pattern from different vantage points, the case for action is considerably stronger — and considerably faster to make.

From a Function That Responds to Questions — To One That Helps Anticipate Them

The argument for MI’s strategic value is well understood within the function. The challenge, for many teams, is demonstrating it consistently, visibly, and at the speed internal stakeholders need.

When valuable insight is buried in manual processes, trapped in static monthly reports, or difficult to package for cross-functional audiences, the opportunity to influence decisions at the right moment narrows. MI ends up being recognised for how efficiently it answers questions, rather than for what those questions reveal.

MIR Analytics is designed to change that dynamic. By reducing the time spent on manual data preparation, surfacing insights earlier in their development, and making it easier to communicate structured intelligence to the functions that need it, MIR Analytics helps MI move from a function that responds to questions to one that helps the organisation anticipate them.

That shift is not just a change in tooling. It is a change in how MI is seen, used, and valued across the organisation.

What It Looks Like in Practice:

  • Faster identification of emerging themes, weak signals, and off-taxonomy issues across the full MIR dataset
  • Significantly less expert time spent on data assembly, sorting, and retrospective report preparation
  • Tailored, insight-led outputs for colleagues in allied functions
  • MI intelligence that reaches cross-functional teams before the window to act has closed
  • A clearer, more consistent, and more credible demonstration of MI's contribution across the product lifecycle

MI’s value is not measured only by how well it answers the questions it receives — but also what it does with what those questions reveal.

See MIR Analytics in Action

If your MI team is spending significant time on manual insight extraction — if important signals may be developing below the threshold of your current reporting — or if you want to build the foundation for MI to function as a genuine strategic intelligence asset within your organisation, we would welcome the opportunity to show you what MIR Analytics can do.

Request a Demo
MIR Analytics is part of the VML Health Platforms suite, purpose-built for the Medical Information function of pharmaceutical, biotech, and MedTech organisations.

Click here to find out more about VML Enterprise Solutions