Diagnostic Data in Precision Medicine Launches: A Commercial Guide

Most launch plans are built backwards from prescriptions. Forecasted patients, share assumptions, a target list ranked by historical prescribing in the category. For a therapy that needs a biomarker result first, that model has a hole in the middle of it. The prescriber may be ready, the payer may cover it, and the patient still never gets the drug because the test was never ordered, was ordered too narrowly, or came back after first-line treatment had already started.

This guide is about filling that hole. It covers what diagnostic data actually shows, how it changes HCP targeting and launch sequencing, how to measure against it, and what to ask before you sign with anyone who sells it. The other articles in this series go further on each piece, and I link to them where they fit.

Why testing is the bottleneck for targeted therapies

A targeted therapy is approved for a defined population: patients with a specific mutation, fusion, expression level, or other marker. FDA often pairs these approvals with a companion diagnostic, a test that supplies the information needed to use the drug safely and effectively. That pairing changes the commercial math. Your addressable market is not "patients with the disease." It is "patients with the disease who were tested, with the right test, at the right time, and whose result reached the treating physician."

Each of those conditions leaks. Some patients are never tested. Some get a single-gene test that does not include your marker. Some get the right panel, but the result arrives after treatment has begun. Some get tested at a reference lab, and the result sits in a portal the treating oncologist does not check. Published research in several cancers has found testing rates below what guidelines recommend, though the size of the gap varies by tumor type, setting, and year, so check the current literature for your indication. The biomarker testing gap and how HCP media can help close it is its own article in this series.

The practical point for a launch team is simple. If the test is the constraint, messaging the prescriber about efficacy does not move volume much on its own. You also need to reach whoever controls the test order, the sample, and the result: often the treating physician, sometimes a pathologist, sometimes a tumor board or an oncology nurse who coordinates care.

What diagnostic and lab data shows

"Diagnostic data" covers several different things, and vendors in the category package them differently. Before you compare offers, separate the signals.

None of these is patient-level targeting. Commercial use of lab data generally relies on de-identified data under HIPAA, either through the Safe Harbor method or an expert determination, and the output you work with should be aggregated to an HCP, account, or geography. The article on patient identification with diagnostic data and how to keep it compliant covers where the lines are.

How diagnostic data changes HCP targeting

A standard launch target list is a ranking of prescribers by category volume, usually from claims, cut into deciles. For a biomarker-gated drug, that list over-weights high-volume prescribers who may not test well and under-weights smaller practices that test thoroughly. Adding testing behavior gives you a second axis.

A simple two-by-two works as a starting point:

• High category volume, high testing: your core targets at launch. Message the drug and the data.
• High volume, low testing: the biggest opportunity and the hardest. Lead with testing education, not product.
• Low volume, high testing: often academic or specialist sites. Smaller reach, high relevance, often influential.
• Low volume, low testing: lowest priority for paid media, but worth watching for change.

The mechanics of joining lab data to an NPI list, building tiers, and setting media triggers are in how to integrate diagnostic testing data into HCP targeting. Two warnings up front. First, the ordering NPI on a lab requisition is not always the physician who will prescribe; in some settings it is a pathologist, a fellow, or a group billing number. Second, your list is only as good as the lab coverage behind it, which leads to accuracy.

Accuracy, coverage, and what to ask a diagnostic data vendor

The most common question buyers ask about any diagnostic data company is some version of "how do you make sure this is accurate?" The honest answer from any vendor should start with coverage: which labs are in the data, which are not, and what share of national testing volume that represents for your test type. A vendor with strong reference lab coverage and weak hospital lab coverage will make academic medical centers look like they test less than they do.

The rest of the questions follow from there: how ordering and treating physicians are attributed, how long between a test and its appearance in the data, how de-identification is done and certified, and whether the vendor has checked its counts against any known reference. How to judge the accuracy of lab and diagnostic data for targeting turns these into a structured review. For general provenance questions that apply to any health audience vendor, the health data provenance question list is a useful companion.

Diagnostic data is one source among several. Claims, EHR, specialty pharmacy, professional network, and identity providers each see a different slice of the same HCP. How to compare HCP targeting data providers for campaign measurement lays out the categories with pros and cons.

Sequencing a launch around the testing step

Precision medicine launches tend to work better in phases that follow the test, not the calendar alone.

1. 12 to 18 months before approval: map testing. Which HCPs and accounts test for the marker today, with what test types, and where results come back. This is disease education territory; keep anything promotional out until approval and run all materials through MLR.
2. 6 to 12 months before approval: unbranded testing education aimed at high-volume, low-testing HCPs and at pathology and lab audiences. Set up holdout groups now so you have a pre-period.
3. Launch to month 6: branded HCP media to high-testing prescribers, with triggers tied to testing signals where the data latency allows it. Field and media coordinated on the same tier definitions.
4. Month 6 onward: shift budget based on testing uplift and early prescription data. Retire tiers that are not moving.

The broader media plan structure, from pre-launch through year one, is in the drug launch media plan article. If you are at a company that has never done this before, launching a first precision medicine at a small biotech gives a pared-down version with what to outsource.

Measuring what the launch is supposed to change

If testing is the bottleneck, testing is a primary outcome, not a nice-to-have. A measurement plan for a precision medicine launch should track at least three levels:

• Exposure: share of the target list reached, by tier, with frequency. See how to measure HCP target-list reach for the denominator issues.
• Testing behavior: change in testing rate or test type among exposed HCPs versus a holdout or matched group.
• Prescribing: new patient starts, once claims or prescription data for the brand matures.

The trap is reading testing uplift from a source that covers exposed and unexposed HCPs unevenly. If your exposed group skews toward community practices that send tests to well-covered reference labs, and your control group skews toward hospitals with in-house labs that are poorly covered, you will see a "lift" that is mostly a coverage artifact. Check balance on lab coverage before you read the result. The holdout test design article covers the setup.

Budget, contracts, and plumbing

Diagnostic data is rarely the largest line in a launch budget, but the contract terms can surprise you. Licenses are priced in several ways: annual flat fee, per NPI, per CPM when activated in media, or per project. How HCP data products are priced explains the models with hypothetical numbers. Onboarding, refresh, platform, and measurement fees often sit outside the headline price; hidden costs in HCP targeting data contracts has a checklist to use before signing.

Then the data has to go somewhere. Testing tiers need to land in CRM for the field, in a CDP or warehouse for analytics, and in a DSP or HCP platform for media, with the same NPI keys and the same refresh cadence. When those drift, the field and media teams target different lists without knowing it. Integrating HCP data into CRM, CDP, and DSP without breaking scale covers the patterns.

Practical takeaway

Before you finalize the launch target list, build one table: your top 200 target HCPs by category volume, with a column for whether they appear as testers for your marker in whatever diagnostic data you can access, and a column for the lab coverage behind that answer. The gaps in that table tell you where testing education has to come before product messaging, and where your data is too thin to make the call.

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.