Analytics

NBRx vs NRx vs New Patient Starts: Which New Patient Number Your Specialty Brand Should Trust

How NRx, NBRx and new patient starts differ, why they disagree for specialty brands, and the data rules that make one new patient number hold up.

Nov 20265 min readAnalytics

Three new patient numbers, each answering a different question

Ask a specialty brand how many new patients it added last month and the answer depends on who is in the room. Brand quotes new prescriptions (NRx) or new-to-brand prescriptions (NBRx) from syndicated data. Patient services quotes referrals or enrollments from the hub. The specialty pharmacy team quotes first fills shipped. Each number can be calculated correctly and still disagree with the others, sometimes in opposite directions in the same month.

The three measure different things. NRx counts prescriptions with a new prescription number, which includes a doctor rewriting a script for a patient already on therapy, for example after a dose change or when the old prescription runs out of refills. NBRx counts prescriptions for patients who had no fill of the brand within a lookback window, so it includes patients new to the market, patients switching from another product, add-ons and patients restarting after a long gap. A new patient start, built from specialty pharmacy dispense data, counts the first time a patient actually received drug. Only the last one says a patient began therapy.

For a retail brand the gap between these numbers is often small enough to ignore. For a specialty brand dispensed through a limited network of specialty pharmacies, with prior authorization, bridge supply and long time to therapy, the gap can be large and it moves forecasts, incentive pay and launch decisions. This article explains each measure, where they part ways and the rules that make one new patient number hold up. It builds on our guides to defining an active patient and launch analytics.

Where the numbers part ways in specialty

  • A new script is not a new patient

    Dose changes, new strengths and rewritten prescriptions all create a new prescription number. NRx rises while the number of people on therapy stays the same.

    • Titration counted as new demand
    • Refill limits create new scripts
    • Pharmacy transfers can look new
  • The lookback decides who is new

    NBRx treats a patient as new to brand if there was no fill inside the lookback window. Change the window and restarts move in or out of the count.

    • Restarts read as new patients
    • Lookback rarely shown on the slide
    • Different windows across vendors
  • Coverage is not complete

    Syndicated prescription data depends on which pharmacies report to the data provider. In a limited distribution network, one pharmacy not reporting can change the trend.

    • Projected volumes, not counted ones
    • Network changes shift the trend
    • Free drug fills usually absent
  • A referral is not a start

    Hub referrals and enrollments count intent. Patients lost to benefits verification, prior authorization denials or cost never start, and some patients start without passing through the hub.

    • Abandoned referrals counted
    • Direct-to-pharmacy starts missed
    • Bridge shipments counted as paid starts

What a new patient number that holds up looks like

One named headline measure
Patients linked before anything is counted
Written rules for new, restart and switch
Syndicated and dispense data shown side by side
Incentives tied to the right measure

How to put it in place

List every new patient number in use

Collect the new patient figures in the QBR, the forecast, the incentive plan and the patient services report, with the source and definition behind each.

Link patients across sources

Attach hub referrals, specialty pharmacy dispenses and free drug shipments to one patient ID or token, and check a sample of pharmacy transfers by hand.

Agree the definitions

Brand, patient services, finance and analytics agree the headline measure, the lookback window and how restarts, switches and bridge starts are counted, in writing.

Reconcile one month by hand

Walk from referrals to starts to NBRx for a single month and explain each gap: abandonment, restarts, rewritten scripts, missing pharmacy coverage.

Automate and label

Build the measures from the same linked data each week, label every chart with its definition and as-of date, and alert when the gap between measures moves.

Questions leaders ask about NBRx, NRx and new patient starts

What is the difference between NBRx and NRx?
NRx counts prescriptions with a new prescription number, including new scripts for patients already on the brand. NBRx counts prescriptions for patients with no fill of the brand inside a lookback window, so it is closer to new patients but still includes switches, add-ons and restarts.
Is NBRx the same as new patient starts?
No. NBRx comes from prescription data and depends on the lookback window and pharmacy coverage. A new patient start is the first fill a patient actually received, which for specialty brands is best measured from linked specialty pharmacy dispense data.
Why does our NBRx trend differ from specialty pharmacy first fills?
Common causes are pharmacies in the network that do not report to the syndicated provider, restarts counted as new because of the lookback window, free drug and bridge fills missing from syndicated data, and the time lag between prescription and first shipment.
Which measure should a specialty launch track?
Track starts from linked dispense data as the headline patient number, referrals and time to therapy for the funnel, and NBRx for market share and switching. Showing them together, with the gaps explained, is more useful than choosing one and hiding the rest.
Should incentive compensation use NBRx?
It can, if the plan says so and the team understands what it includes. Where the gap between NBRx and actual starts is large, review it before payouts so field credit reflects patients who started therapy.

Compare your new patient numbers with us

Send us the new patient figures from your last QBR and where each came from. We will show where definitions, linking or coverage explain the gaps. More on our work at specialty pharma analytics and automation.

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