Pharmaceutical companies are not using the best marketing data available. Sure, they spend tens of millions of dollars a year
on data, and billions more on marketing campaigns, but they are surprisingly vague about the prescribing habits of the physicians
those campaigns target.
Anonymous patient-level data (APLD) could bring a new level of precision to pharma marketing. APLD can differentiate between
physicians who are writing mainly refill prescriptions, and those writing mostly new-to-therapy prescriptions. Many companies
would reshape their marketing and sales campaigns based on these data alone. Managers understand that physicians who are writing
new prescriptions deserve more attention from sales reps. But even though data can single out physicians with high marketing
upsides, most pharma companies are doing without such high-value data.
Why is pharma ignoring information that could increase returns? First, acquiring and utilizing the new data is seldom easy.
It takes more than money to buy the right information. Not every APLD vendor will have the same data sets available, so companies
must begin by shopping for the right vendor. Second, neither the APLD data products nor many of the companies offering them
have matured. This creates a complex, shifting vendor landscape, in which it is difficult to sort out competing claims and
Another barrier to entry is the cost of switching from present data sources. Currently, pharma companies rely on prescription
data such as IMS' Exponent/NPA and NDC's Source Prescriber to identify high-prescribing physicians. While this data says nothing
about which physicians have the most potential prescribing upside (or downside), it is firmly entrenched within most pharma
companies. IMS, in particular, does not provide APLD yet with the depth and breadth of other vendors, but its data are used
to calculate sales force compensation. And if the cost of switching is high, so is purchasing multiple databases.
Finally, the organization as a whole must change to take full advantage of APLD data. Changes in the way physicians are targeted
will lead to changes in the way sales representatives are trained, and probably in how they gather and digest information
before calling on doctors.
What is APLD?
APLD is healthcare-utilization data that can be linked, in a HIPAA-compliant manner, to individual patients longitudinally.
That is, they are data that can track a patient's healthcare utilization over time. This data is captured through similar
sources as standard prescription data are, but with two major differences: the number of data fields captured is increased
(in some cases), and proprietary linking algorithms that utilize multiple fields (birthdate, zip code, sex) are applied to
create a unique encrypted identifier for each patient.
APLD itself is not new. Small data sets have been used for longitudinal analyses in the outpatient market for several years
(for example, PMSI, CHAMP, LifeLink). And Solucient (formerly HCIA-Sachs) has made large APLD sets available for hospital
inpatients. But only recently have large APLD sets been applied to drug use in the outpatient setting. This latest development
presents pharma with opportunities to use APLD to improve ROI.
Targeting the Right Physicians
APLD can improve sales and marketing ROI by delivering differentiated data on physician-prescribing data (see Figure 1). In
this hypothetical example, traditional Rx data for a chronic outpatient drug treatment is compared with that using APLD. Based
on traditional data, a pharma company would focus resources on Dr. Smith, Dr. Jones, and Dr. Brown, the high-prescribing physicians.
However, APLD reveal that Dr. Jenkins and Dr. White may be more important than Dr. Smith and Dr. Jones because they prescribe
drugs to more "new-to-therapy" patients. Physicians generally are reluctant to switch treatments when medications are working,
so new-to-therapy patients have a higher value to pharma companies than renewing patients. This is particularly critical for
companies launching new products in crowded markets. Sometimes, new-to-therapy patients represent the only chance to capture
Figure 1Targeting the Right Physicians