How Pharma Measures the ROI of EHR Advertising
How to measure EHR advertising ROI: Rx lift studies, control groups, what counts as cost and return, common biases, and credible reporting.
The short answer
Pharma companies measure EHR advertising ROI by linking exposed HCPs to prescription data and comparing their prescribing with similar unexposed HCPs, which estimates incremental prescriptions. That lift is then valued using net revenue per prescription and compared with total cost. Credible studies define the control group before launch, account for HCPs who were already high prescribers, and separate EHR effects from other channels.
EHR vendors often present impressive ROI figures. Some are sound. Others compare exposed HCPs with a weak control group and call the difference lift. Knowing how ROI should be measured lets you tell them apart.
The ROI formula
ROI = (incremental prescriptions x net value per prescription - cost) / cost
Each part needs care:
- Incremental prescriptions are prescriptions that would not have happened without the media. Not all prescriptions written by exposed HCPs.
- Net value per prescription should use net revenue after rebates and discounts, ideally adjusted for persistence (how long patients stay on therapy).
- Cost includes media, vendor fees, measurement, and creative adaptation.
The article on attributed vs. incremental prescriptions explains why the first bullet matters most.
Study designs
| Design | How it works | Strength |
|---|---|---|
| Pre-post | Compare exposed HCPs before and after | Weak; ignores market trends |
| Matched control | Compare exposed HCPs with similar unexposed HCPs | Good if matching is strong |
| Randomized holdout | Randomly withhold messages from part of the list | Strongest, if feasible |
| Geographic test | Compare regions with and without | Useful when HCP-level holdouts are hard |
The matched-control studies and holdout test design articles cover the methods.
Matching matters most
Exposed HCPs in EHR studies are, by definition, users of certain platforms who saw the message, often because they treat relevant patients. A good control group matches on:
- Specialty and practice type.
- Prior prescribing of the brand and the class.
- Patient volume in the condition.
- Geography and payer mix.
- Exposure to other channels.
If the control is just "HCPs not on the platform," the comparison may mostly measure differences between practices.
Separating EHR from other channels
Exposed HCPs often see your programmatic, email, and field too. Options:
- Compare HCPs exposed to EHR plus other channels with HCPs exposed only to other channels.
- Use a multi-touch or model-based approach across channels, with caution.
- Run a holdout on EHR alone while other channels continue.
Questions for vendor-run studies
- How was the control group selected, and on what variables were they matched?
- Was the design set before launch?
- What data source links exposure to prescriptions, and what are match rates?
- Is the result shown with a confidence interval?
- How did you handle HCPs exposed to other channels?
Reporting ROI credibly
- Report a range, not a single number.
- Show the comparison against other channels measured the same way.
- State assumptions about net value per prescription.
- Note limitations: match rates, study length, and channel overlap.
The pharma programmatic measurement framework shows how to place EHR results alongside other channels.
Common mistakes
- Using gross price per prescription.
- Counting all prescriptions from exposed HCPs as return.
- Accepting a study designed after the campaign ended.
- Comparing EHR ROI with another channel measured in a different way.
Practical takeaway
Before your next EHR buy, agree the control-group design and the value-per-prescription figure with finance and the vendor in writing. ROI numbers built on terms agreed in advance are the ones leadership will trust.
Frequently asked questions
What is a good ROI for EHR advertising?
There is no reliable universal benchmark. ROI depends on the brand's value per prescription, the size of the target list, and the study design. Compare with your own other channels measured the same way.
Which platforms connect EHR exposure to prescriptions?
Many EHR media vendors offer outcome studies, and independent measurement companies can link exposure to de-identified prescription data. Prefer designs with a defined control group.
Why do vendor ROI studies look so strong?
Often because exposed HCPs were already more engaged or higher prescribers than the comparison group. Check how the control group was matched.
Sources
- Veeva, Crossix
- American Statistical Association, Statement on p-Values
- HHS, Guidance Regarding Methods for De-identification of PHI
External guidance and platform documentation change. Links were current at publication; check them again before relying on them for a decision.
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.
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