While forcing Big Pharma to disclose the amount of money it’s putting in doctors’ pockets is important, it only solves part of the problem of the dicey relationship between pharmaceutical companies and physicians. There is a key piece of it that a national registry does not address — namely, the data mining that allows drug companies to know how many prescriptions a particular doctor is writing for its products:
Gathering prescription information on specific patients is a recent marketing development that is being used to refine information on physicians’ prescribing preferences. The identity of individual patients is of little importance to pharmaceutical companies. What they are really after is information about a specific physician’s prescription-writing habits.
Physicians’ names are not protected by privacy laws in the way that patient names are protected — and data-mining companies have long gathered information on the number of prescriptions a particular doctor writes for specific drugs. Although pharmacies keep data on physicians that are identified only by the physician’s number, these numbers are easily matched to physician names by comparing them with lists of names purchased from the American Medical Association (AMA) Physician’s Masterfile. The Physicians’ Masterfile is a database that contains demographic information on all U.S. physicians, whether or not they are members of the AMA. Licensing information from the Physician’s Masterfile, and other database product sales, netted more than $44 million for the AMA in 2005.
Physicians control the distribution of prescription drugs, so every bit of information on them is valuable to the pharmaceutical industry. (Although physician assistants and nurse practitioners prescribe as well, both of these groups tend to prescribe more rationally than physicians. Because physician assistants and nurse practitioners do not prescribe large amounts of expensive branded drugs, their prescribing habits are not usually tracked. That may change in the future). The industry uses this prescribing data to maximize the efficiency of “detailing”, their term for the promotion of drugs to doctors by pharmaceutical sales representatives (otherwise known as drug reps).
Prescribing data are used to rank physicians on a scale from one to ten based on how many prescriptions they write. Drug reps also use prescribing data to track how many of a physician’s patients receive specific drugs, how many prescriptions the physician writes for targeted and competing drugs, and how a physician’s prescriptions change over time. All this information helps drug reps tailor their marketing messages to the physicians.
New Hampshire was the first state to ban the sale of prescription data; it was promptly sued by IMS and Verispan. Other states considering similar bills include NY, NE, AZ, IL, KS, ME, MA, RI, TX, VT, WA, and WV. Data-mining companies argue that prescription data is used for public health purposes, but the use of these data by government and academic researchers is vanishingly small. Make no mistake about it: the purpose of prescribing data is to assist industry to influence physicians to prescribe the most expensive drugs.
I have a doctor friend who writes a lot of prescriptions for Pfizer drugs. Pfizer knows that because they track it, and the perks they offer are based on that knowledge. They aren’t just throwing out bones randomly to doctors because they have nice offices, there’s a very specific perks-for-scripts relationship at work. My friend is hired with some frequency to give “lectures” to, say, five doctor colleagues (at several thousand dollars a pop) on behalf of the pharmaceutical company and their particular drug. Even my friend acknowledges that it’s quite the racket.
There are many other ways to get money into the pockets of reliably prolific prescription writers, but the fact is that at present every time a doctor puts pen to paper he/she knows that the pharmaceutical company is watching. Without that kind of Big Brother awareness, doctors might be more cognizant of their patients’ needs first and less likely to be influenced by self-interest. Cutting the supply line of information is a critical first step.