What medical affairs analytics is, and why most teams still count calls
Medical affairs analytics is the work of turning MSL interactions, field insights, medical information requests, publications and real-world data into evidence of what medical affairs changed: which knowledge gaps closed, which insights shaped strategy and which evidence plans paid off. Done well, it replaces a monthly count of MSL calls with a short set of execution, insight and outcome measures that leadership trusts.
Most teams are not there yet. The data sits in a CRM medical module, a medical information system, a publications tracker, congress spreadsheets and a KOL list maintained by hand. Insights arrive as free text that nobody codes the same way twice. So the easiest number to report, interactions per MSL, becomes the number that defines the function, even though almost nobody thinks it captures the value.
This guide sets out the medical affairs metrics worth tracking, the data sources behind them, what a medical affairs dashboard should show to each audience, how to turn free-text MSL insights into something you can analyse, a realistic build sequence and the compliance lines you must not cross. It is written for heads of medical affairs, medical excellence and field medical operations at mid-sized pharma and biotech companies who need the measurement in place without a two-year transformation programme.
The measurement gap, in numbers
Which medical affairs metrics should you track?
IQVIA's 2025 guidance on medical affairs impact metrics groups measures into three categories: execution metrics such as MSL calls, perception metrics drawn from stakeholder feedback, and outcome metrics that show a change in the healthcare ecosystem or patient care. That framing is sound. What teams lack is a concrete list and a definition for each, so here is the set we start from. Pick no more than three or four per tier for the first release.
| Tier | Metric | Definition that survives an audit | Main source |
|---|---|---|---|
| Execution | Reach of priority experts | Share of the agreed priority KOL list with at least one scientific exchange in the period | CRM medical interactions joined to the KOL master |
| Execution | Frequency against plan | Interactions per priority expert compared with the engagement plan for their tier | CRM and the medical plan |
| Execution | Medical information response time | Median days from unsolicited request to response, by channel | Medical information system |
| Insight | Actionable insights submitted | Insights coded to a strategic question, per MSL per month | CRM insight records |
| Insight | Insight to action rate | Share of insight themes reviewed by the medical strategy team that led to a recorded decision or action | Insight review log |
| Insight | Theme coverage | Strategic questions in the medical plan that received insights from at least two regions | Coded insights and the medical plan |
| Perception | Expert feedback score | Average rating from structured post-interaction or annual expert surveys | Survey tool |
| Perception | Internal partner feedback | Rating from brand, HEOR and clinical development leads on the usefulness of medical input | Internal survey |
| Outcome | Evidence gap closure | Gaps in the integrated evidence plan with a study, publication or data release completed | Evidence plan tracker |
| Outcome | Publication and congress reach | Accepted publications and presentations against plan, with citations or downloads where available | Publications tracker |
| Outcome | Practice signal | Movement in guideline adoption, testing rates or appropriate prescribing in regions with focused medical activity, read as a signal and never as attribution | Claims or real-world data |
The survey data explains why the insight tier matters most. In the 2025 MSL Society survey, 70 percent of respondents said MSL KPIs should focus more on the quality of KOL and HCP relationships, and 67 percent on the quality of actionable insights. Yet only about half of organisations track insights at all. Closing that gap is mostly a data problem, which is good news: it can be solved without changing what MSLs do in the field.
The data behind a medical affairs dashboard
CRM medical interactions
MSL interactions, topics discussed and follow-ups logged in Veeva, Salesforce or another CRM. The backbone of every execution metric.
- Interaction date, type and channel
- Expert, institution and territory
- Topic and product tags
Field insights
Free-text observations MSLs record after scientific exchanges. The richest source and the least structured one.
- Insight text and date
- Linked strategic question, if coded
- Region and therapy area
Expert and institution master
A clean list of KOLs, HCPs and institutions with tiers and affiliations, usually built from a reference data vendor plus your own priority lists.
- Stable expert identifiers
- Tier and engagement plan
- Institution hierarchy
Medical information requests
Unsolicited requests from HCPs and patients, with response times and topics, from your medical information system or vendor.
- Request topic and channel
- Received and responded dates
- Product and region
Publications, congresses and evidence plans
Publication plans, congress activity and the integrated evidence plan, which often live in spreadsheets or a publication planning tool.
- Planned against accepted
- Evidence gaps and status
- Owners and due dates
Real-world data and claims
De-identified claims or longitudinal data used to watch treatment patterns, testing rates and guideline uptake in the areas medical affairs focuses on.
- Licensed and de-identified
- Read at region level
- Signal, not attribution
What a medical affairs dashboard should show, by audience
MSL
An MSL needs a working view, not a scorecard: which priority experts are due a conversation, which follow-ups are open and what colleagues in other regions are hearing on the same questions.
Field medical lead
Field medical directors need reach and frequency against plan by region, the volume and quality of insights by MSL, and early warning where coverage is thin.
Head of medical
Leadership needs a one-page view that ties activity to the medical plan: which strategic questions are being answered, which evidence gaps closed and what decisions came from field insight this quarter.
Cross-functional partners
Brand, HEOR and clinical development benefit from aggregated, de-identified insight themes. They should never see individual expert interactions or anything that turns medical exchange into a sales lead.
Turning free-text MSL insights into analytics
Insights are where medical affairs analytics succeeds or fails. An insight that reads "several oncologists unsure about sequencing after progression" is valuable once, in one inbox. Coded against a strategic question, a therapy area and a stakeholder type, it becomes a data point you can count, trend and compare across regions.
A workable approach has three parts. First, a short taxonomy agreed with the medical strategy team: the strategic questions from the medical plan, plus a handful of standard themes such as unmet need, treatment practice, safety perception, access barrier and competitor data. Second, coding at the point of entry where the CRM allows a picklist, with a language model suggesting codes for the free text and a medical excellence reviewer confirming them. Third, a monthly insight review where themes are discussed and the decision or action is logged, because the insight to action rate cannot be measured without that log.
A worked example, with illustrative numbers. Take a field medical team of 30 MSLs who each submit an average of 8 insights a month. That is 240 insights a month, or 2,880 a year. Reading them by hand is not realistic, so most are never read twice. With coding in place, a medical excellence lead can see that 60 of last month's 240 insights concerned the same diagnostic testing question, that 45 of those 60 came from two regions, and that the theme had been raised for three months in a row without a recorded action. That one view justifies the analytics work, because it turns field effort into a decision the leadership team can act on.
Language models do this coding well when the taxonomy is short and every suggestion is reviewed. They do it badly when asked to invent the categories themselves, or when their suggestions go straight into reporting with no human check. Keep insight text inside your own environment and your CRM vendor's approved tools, and never send identifiable patient details to any model.
How to build medical affairs analytics without a two-year programme
Agree the questions and the metric definitions
Start with the medical plan. Pick three or four metrics per tier, write a definition for each, and agree who owns it. This is a few workshops, not a project.
Build the expert master
Match KOLs and institutions across CRM, reference data, publications and congress lists so one expert is one record. Most of the effort sits here, and every later metric depends on it.
Load interactions and medical information data
Automate the extracts from CRM and the medical information system into a governed store, with checks for missing or late files.
Code the insights
Apply the taxonomy to current and historical insights, with model suggestions reviewed by medical excellence, and set up the monthly insight review log.
Release the first dashboards
Ship the MSL working view and the field medical lead view first, reconcile them with what managers already report, then add the leadership page.
Add perception and outcome measures
Bring in survey results, the evidence plan and real-world data signals once execution and insight reporting is stable and trusted.
Compliance lines medical affairs analytics must not cross
Medical affairs is non-promotional, and analytics built for it must stay that way. Four guardrails cover most of the risk.
- Keep medical and commercial data apart. MSL interactions and insights should not feed sales targeting, call plans or incentive calculations. Cross-functional partners see aggregated themes only, after medical review.
- Do not measure MSLs on product sales. Tying field medical metrics to prescribing in their region invites exactly the regulatory question you do not want. Read real-world data as a signal about medical need, not as attribution of revenue.
- Reconcile with transparency reporting. Consulting, speaker and advisory board payments to experts are reportable under the US Open Payments programme. If your analytics shows expert engagement, it should agree with what your aggregate spend process reports. Our guide to Sunshine Act reporting covers that data flow.
- Protect patient information. Insights and medical information requests sometimes contain patient details or adverse events. Route those through your safety process, keep analytics on de-identified data and work under your Business Associate Agreement where protected health information is involved.
None of this is legal advice. Your compliance and legal teams should approve the metric set and the access model before the first dashboard goes live.
Build, buy or bring in a partner?
Your CRM's reports
CRM medical modules include standard activity reporting. If interactions are your only source and you have fewer than a dozen MSLs, those reports may be enough for now.
A packaged platform
Several data and analytics vendors sell medical affairs analytics products with their own KOL data and insight tools. Useful if their data model fits yours and you accept their definitions.
Build with a partner
When insights, evidence plans and real-world data must be joined on your own expert master, a build in your warehouse and BI tool gives you definitions you own and data you keep.
Common mistakes, and how Greenwolf builds medical affairs analytics
Four mistakes undo most medical affairs analytics projects:
- Interactions per MSL as the headline number. It is easy to count and, by the field's own assessment, weak proof of value.
- Dashboards before the expert master. Duplicate KOL records inflate reach and break every comparison across regions.
- A model that invents the insight categories. Agree the taxonomy with medical strategy first, then use AI to apply it.
- Expert-level detail shown to brand teams. Share aggregated themes after medical review, never individual exchanges.
It is also not always time to build. A team of a few MSLs with one product and no insight process should fix the insight capture habit first; analytics comes later.
When it is time, we build the layer underneath the dashboard: the expert and institution master, automated loads from CRM and medical information systems, coded insights and the reports each audience needs, inside your own cloud, warehouse and BI tool. Our founder came up through pharma commercial operations consulting at ZS, so we know where field data breaks and how medical and commercial data must be kept apart.
The same foundations power our pharma commercial analytics consulting, with a firewall between the two. If your CRM is the first problem, start with Veeva CRM data integration; if you are weighing expert reference data, read Veeva OpenData vs IQVIA OneKey. For AI tools aimed at field medical teams, see our roundup of AI tools for medical affairs teams, and for our wider life sciences practice, our specialty pharmacy analytics hub.
We work under NDA from the first call, build in your environment rather than ours, use de-identified data wherever the work allows, and handle protected health information only under your Business Associate Agreement.
Medical affairs analytics questions
What is medical affairs analytics?
What KPIs should MSLs be measured on?
How do you measure the quality of MSL insights?
What should a medical affairs dashboard include?
Can AI analyse MSL insights?
Can medical affairs data be shared with commercial teams?
How long does it take to set up medical affairs analytics?
Tell us what your medical team reports today
Describe your CRM, your insight process and the report leadership asks for each quarter. We will come back with the metric set we would start with and what the data needs first. Please do not send any patient information.
Part of our Pharma Commercial and Patient Analytics hub. Start with our specialty pharmacy analytics.