Track Patients, Not Prescriptions - Pharmaceutical Executive


Track Patients, Not Prescriptions
Why pharma companies reject anonymous patient-level data, and why they can't do without it for long.

Pharmaceutical Executive

Figure 2 Quantifying the Untreated
Tailoring the Message In Figure 2, two physicians may appear to have the same affinity for product X. Unless the sales rep takes time to personally learn a physician's prescribing patterns, he would deliver essentially the same message to each physician. However, APLD reveals that Dr. Smith seems biased against product X for younger patients, while Dr. Jones may be biased against it for patients with cardiovascular co-morbidities. Armed in advance with this knowledge, sales reps can tailor messages to different physician segments.

Improving New Product Planning Enhancing forecast accuracy with APLD can improve ROI in new product planning. With traditional data sources, there is no accurate way to size the overall market for diseases when drug penetration within the target patient population is limited. (Consider the case of macular degeneration.) The same problem occurs when existing drugs are used for multiple indications. Chart audits, epidemiology studies such as the National Health and Nutrition Examination Survey (NHANES), and physician surveys, such as the National Drug Therapeutic Index (NDTI) and the Physicians Drug and Diagnosis Audit (PDDA), can provide trends, but their sample sizes are often too small or biased to provide accurate information for any but the most common diseases.

In addition, the most commonly used tools, NDTI and PDDA, only can project number of office visits. They cannot estimate unique patients reliably. Because APLD vendors link diagnosis codes to unique patients for 20 to 25 percent of office visits, they can accurately quantify the number of patients with a given disease (or at least the number of people making at least one office visit each year, which in most cases is the relevant population). A forecast based on a better estimate of unique patients can be 50 to 500 percent more accurate, which is sufficient to change the level of resources allocated to a product.

Patient Segmentation Even when prescription data can be used to size the overall market, APLD can drive more accurate patient segmentation. For example, assume a company has a fourth-to-market, me-too drug for a chronic disease. The marketers know that male patients over 60 represent the drug's best opportunity to gain market share. To quantify the size of this market segment with traditional data, researchers must build a complex model combining epidemiology and physician-survey data. Even then, the accuracy of the model will be questionable. But because APLD can track unique-patient drug use over time and link it to the patient's age and sex, a fairly accurate number can be generated. Once again, the improvements in forecast accuracy can change go/no-go and resource allocation decisions.

Quantifying Untreated Patients APLD captured through the physician office can link the diagnosis code to drug use. This allows drug companies not only to differentiate utilization of their drugs by indication better—how much calcium channel blocker is prescribed for hypertension as opposed to angina—but also to accurately quantify untreated patient populations and identify the demographics and concomitant conditions of the patient pool.

Figure 3 Tailoring Messages
While these vendors only capture 20 to 25 percent of physicians currently, the data set is rich enough for marketing purposes—to identify and quantify market segments at national and regional levels, and make more robust resource-allocation decisions. The most powerful applications of this data, however, would occur if vendors were able to expand their data access and achieve this level of data on over 50 percent of physicians. Achieving this critical mass will enable drug companies to use the information to influence individual-prescriber behavior by tailoring messages to individual physicians, based on the number and type of untreated patients in their practices (see Figure 3).


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