Measuring HCP Sentiment: Surveys, Field Feedback, and Media Signals

Engagement data tells you what an HCP did with your content. Sentiment is about why. A neurologist who never clicks your banners might be indifferent, or might already be a loyal prescriber who has no need for more information, or might distrust the safety profile. Those three people need completely different treatment, and engagement data alone cannot tell them apart. This piece is part of the HCP engagement series.

The main methods for measuring HCP sentiment

Surveys and ATU studies: the only direct measure

An ATU study is a repeated survey of prescribers in the brand's specialties, typically fielded through a physician panel at a fixed cadence (quarterly or twice a year is common). It asks about unaided and aided awareness, how the brand rates on key attributes against competitors, whether the HCP has prescribed it, and what they intend to do.

Three things usually go wrong. The sample does not match the target list, because panels over-represent HCPs who like taking surveys. Questions change between waves, so the trend is broken. And results get reported as if stated intent equals behavior, when intent to prescribe tends to run well ahead of actual prescribing.

The fixes are simple to describe and tedious to do. Weight the sample to the target list by specialty and tier where possible. Freeze the core questions and add new ones at the end. Report intent as a trend, not a forecast. Survey honoraria paid to HCPs are usually handled by the research vendor and, where the manufacturer does not know respondent identities, may fall outside Open Payments reporting; check with compliance.

Field feedback: useful, but filtered

Reps and MSLs hear objections first. When a competitor publishes new data, the field knows within days. That speed is valuable. But call notes have two built-in biases. They come only from HCPs who see reps, which excludes a growing share of prescribers in many specialties. And they pass through a person who may be inclined to report positive reactions.

Structured capture helps more than better training. A short picklist after each call ("main objection raised: efficacy, safety, access, cost, none") produces data you can count. Free-text notes are better for qualitative review than for trend lines. Medical affairs feedback from MSLs must stay within the scientific exchange boundary; commercial teams should see summarized insights through approved channels, not raw MSL notes.

Media and content signals: behavior as a proxy

Content engagement can be a decent proxy for interest. If HCPs in a segment read the safety data page at twice the rate of the efficacy page, that is information. Creative tests that rotate claims can show which message earns more attention among on-list NPIs. The HCP engagement metrics guide explains what each signal can support.

The limitation is obvious once you say it out loud. Reading the safety page might mean concern, or due diligence before prescribing, or a resident checking for an exam. Behavior is ambiguous about attitude. Use content signals to generate questions, then use surveys to answer them.

Social listening: narrow use cases

Some physicians discuss trials and treatment choices publicly, especially in oncology, cardiology, and a few other active specialties. Listening tools can track that conversation. For most brands the volume is small, it is hard to confirm who is actually a clinician, and the conversation skews toward a vocal minority. If your team collects and reviews this content, involve pharmacovigilance: mentions of adverse events may trigger reporting obligations under company procedures.

How to combine sentiment sources without double counting

1. Pick a small set of sentiment questions that matter for decisions (for example: do target HCPs believe the efficacy claim? what is the leading objection?).
2. Assign each question a primary method. The survey answers belief questions; field feedback answers objection frequency; content signals answer topic interest.
3. Use the other methods only to corroborate or challenge the primary one.
4. Report sentiment by segment, not overall. An average across segments hides the fact that one group is cooling while another warms. The HCP segmentation models article covers how attitudinal segments are built from this kind of data.
5. Connect it back to behavior. If sentiment in a segment improves and prescribing does not, the barrier may be access, not belief. The low HCP engagement diagnostic walks through how to separate those causes.

Practical takeaway

Write down the two sentiment questions that would change your plan if the answer moved, and check whether your current ATU questionnaire actually asks them in a consistent way. If it does not, fix the questionnaire before the next wave. A cleaner question asked consistently for two years is worth more than a richer survey that changes every time.

Editorial note. Analysis and frameworks are the author's own and do not represent Acxiom or any current or former employer, client, or named platform. Examples labeled hypothetical or illustrative are not results from real campaigns. Nothing here is legal, regulatory, or medical advice.