Pharmaceutical companies spend billions of dollars annually to support the activities of sales representatives who call on doctors, a practice known as detailing. Much of the investment is directed at marketing to physicians who prescribe higher volumes of drugs.
However, an analysis of prescription data by Lakshman Krishnamurthi (Professor of Marketing at the Kellogg School of Management) and Tulikaa Bhatia (Assistant Professor of Supply Chain Management and Marketing Sciences at Rutgers Business School and Kellogg alumnus) suggests pharmaceutical companies should focus on a more refined target: heavy prescribers who treat higher percentages of new patients.
Detailing expenditures are most effective when they are focused on the physicians likeliest to write initial prescriptions, Krishnamurthi says. Patients are seldom switched to a different drug within a specific drug class, even if they switch doctors. This means that the first prescription is crucial; unless the first drug fails to work or causes severe side effects, a patient will probably stay on it. Thus, heavy prescribers who are not taking new patients may be less attractive targets than younger physicians working to build their practices. Krishnamurthi explains, “This is where pharmaceutical companies need to fine-tune their strategy.”
Pharmaceutical companies are at a critical juncture, says Krishnamurthi. New product launches are down, and busy physicians have less time for sales calls. Such business challenges have made it increasingly important for pharmaceutical companies to raise the effectiveness of their marketing expenditures.
Research shows that detailing has a significant effect on physicians’ prescription choices. However, Krishnamurthi notes many studies ignore the role of other influences on prescribing behavior, such as patient characteristics (gender, age, race), co-morbidities (other illnesses affecting the patient), payer type (government or private insurer), and patient treatment history. These influences are important because there is no single “best” drug for a condition; the efficacy of medications can vary from patient to patient.
To get a more holistic view of prescribing behavior, Krishnamurthi and Bhatia analyzed longitudinal data that linked prescribing data on individual physicians, brand-level marketing activity, and the patient and insurance information missing from earlier research. Their analysis showed that patient treatment history was the single most influential driver of a physician’s prescription choice, the first paper to document this relationship. In fact, they found that the likelihood that a physician would choose the last drug prescribed for a patient was greater than 90 percent. “There is enormous stickiness in prescriptions,” Krishnamurthi says.
Recognizing the importance of the initial prescription, the authors focused on modeling the factors that influence it. Krishnamurthi and Bhatia looked at two groups—1,000 heavy prescribers and a random sample of 5,000 physicians—who prescribed drugs in a specific chronic disease category, and they counted every new prescription written for patients. The authors took into account patient data and information from a physician survey that revealed doctors’ assessments of the safety and effectiveness of each drug. Direct-to-consumer advertising was negligible in the category and therefore omitted from the model.
The analysis confirmed Krishnamurthi’s hunch that detailing had more influence on the first prescription than on subsequent ones. A simulation showed that reallocating detailing resources to favor physicians with the highest percentage of new patients would increase incremental revenue, market share, and return on marketing investment. This strategy produced better results than focusing detailing efforts on the heaviest prescribers, which is currently common practice because of its simplicity.
“Salespeople can only make so many sales calls a day, month, or year,” Krishnamurthi says. “They should allocate their effort to the physicians most responsive to detailing.”
Patient Formulary Lists over Insurance
The results of the study reveal further differences among physicians. Heavy prescribers and specialists, two groups with more treatment experience, relied less on patient history to prescribe a drug. Heavy prescribers were also less sensitive to the type of insurance new patients held, including those receiving their first prescription. Krishnamurthi and Bhatia surmised that heavy prescribers were too busy to check each patient’s insurance and were instead guided by the typical patient formulary lists. On the other hand, physicians in the random sample were more responsive to patient insurance, perhaps because they were not as familiar with the drugs and typical formulary lists. Finally, all doctors were sensitive to the age and gender of patients and to their concurrent illnesses or conditions, as expected.
Krishnamurthi says patient prescription history probably has the greatest influence in prescriptions for patients with chronic diseases, such as diabetes and hypertension. Physicians have more freedom to choose a new drug when treating acute infections, such as influenza or pneumonia, he adds.
Krishnamurthi and Bhatia say their paper points to new areas of possible study. Research on patient acquisition strategies could be useful to marketers of drugs for chronic diseases, since new patients are the key to increased drug sales. Another area worth exploring is the effect of coupons and incentives on prescribing behavior.
Krishnamurthi notes that health care reform is not likely to change the dynamic of the model, but extending health care coverage to currently uninsured Americans might help drug marketers to put this research into practice. “If it increases the pool of new patients with chronic conditions, pharmaceutical companies will want to increase their detailing to grab these new patients,” he says.
Bhatia, Tulikaa, and Lakshman Krishnamurthi. 2009. Modeling key drivers of physician’s choice of prescription drugs: An integrated view incorporating patient and physician heterogeneity. Working paper.
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