The exponential increase in computing power and data storage capacity, coupled with the sharp decrease in data processing costs, have made possible an era of ‘big data’ that is transforming many aspects of life and commerce. In health care, this evolution is enabling access to information that was impossible to imagine in the era when I first began this work when most prescriptions and test orders were still written on little scraps of paper. As applied to academic detailing, this growing capacity opens up a veritable armory of double-edged swords.
Knowing what doctors are ordering: This information has always been an important advantage of the pharmaceutical industry, which routinely buys the detailed prescribing records of specific physicians from intermediaries such as IMS, who in turn purchase these records from nearly every pharmacy in the nation. In the hands of an agile pharmaceutical representative, knowing a doctor’s drug preferences can be a powerful tool in shaping a promotional message tailored to that person.
Many of us in have had mixed views about the use of such data. On the one hand, it can make possible a more precisely focused discussion about optimal ordering of tests and treatments that is based on a given practitioner’s actual behavior. On the other hand, the approach comes with several risks. One is the concern that clinicians may feel “spied upon” – a problem that doesn’t seem to come up much in industry visits. This in turn can divert the conversation to discussion of “Why are you visiting me?” rather than a conversation about optimal patient care.
Data feedback to clinicians also degenerates frequently into he said-she said debates that often come down to “My patients are different!” We welcome feedback from academic detailing programs on how this use of data has worked (or hasn’t) in their own settings.
What patients are (or aren’t) doing: The computerization of dispensing records opened an era of hitherto-difficult research on patient adherence to their medication regimens, with generally depressing findings of low adherence. The full import of this rampant epidemic of non-compliance is still not well understood by most prescribers. The rapid growth in mobile and wearable technologies that capture physical activity and other lifestyle choices provides another potential source of data on patient behavior, but the best applications of this information are even less well understood.
In principle, academic detailing programs embedded in health care organizations can provide feedback to clinicians on how much or how little their patients are taking medications as directed or complying with other medical advice, and – more important – what to do about it. Is this a useful component of the educational encounter? Again, we would welcome hearing how this use of big data to provide feedback on adherence or patient behavior does or doesn’t fit into the work of ongoing academic detailing programs.
In the coming years, we will see even greater access to terabytes of data on who is ordering what, and what patients are doing with their prescriptions and other treatments. Used well, this technological revolution can provide added power to programs designed to improve that clinical decision making.
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