MedAware’s script-error tool for docs could help marketers plenty, too, CEO says

Med mix-ups are a dangerous problem–and one Israel’s MedAware is working to address with an intelligent system that calls out would-be prescription errors based on doc-habit data and information from patients’ own medical records. In collecting that info, though, MedAware’s system also picked up key data on prescribing patterns that has the potential to be very valuable to pharma marketers.

The company has aggregated millions of prescriptions and full medical records—including blood tests, diagnoses, admissions, outpatient visits and more—to create mathematical models that describe which patients are likely to be prescribed which drugs, and at what point in their condition and treatment, cofounder and CEO Gidi Stein explained in an interview.

And while MedAware initially began aggregating prescription data in order to flag outliers, the company recognizes that’s info drugmakers are eager to get their hands on.

“We can provide much more” than the prescription data drugmakers regularly shell out big bucks for, he said as Israel was preparing to showcase its biopharma industry at the MIXiii BIOMED Conference, taking place later this month in Tel Aviv. “We see the full evolving picture of the patient—we see it on a timeline.”

That means seeing which med a patient started taking when symptoms first cropped up, which med he or she switched to after suffering side effects, and so on, Stein said. And with millions of cases logged, it can extrapolate on a large scale to understand the behavior of different types of patients over time.

MedAware found, for example, that beta blockers were being used by young, healthy people between the ages of 20 and 30, Stein said, “and we didn’t know why until we saw that these patients have migraines and headaches,” alerting the company to an off-label use.

So how can pharma marketers use that type of info? A couple of ways. Sales reps, for one, could tell doctors not to prescribe meds in specific instances where patients later stop taking them. Off-label information, on the other hand, could be “cause for another patient study” that could lead to a new indication, Stein said.
Drugmakers can also benefit simply from discovering which rival meds docs are prescribing for particular types of patients at particular times in their disease course—whether it’s happening because of perceptions in the market or confusion among similar products.

With the latter, there’s precedent for companies tweaking their product monikers when they too closely resemble someone else’s; Takeda and Lundbeck did just that last year with depression med Trintellix, whose name they switched from Brintellix after reports of mix-ups with AstraZeneca blood thinner Brilinta.

According to Stein, though, while prescription errors are very prevalent—of 4.5 billion annual U.S. scripts, 8 million contain life-threatening errors that translate into hundreds of thousands of casualties and injuries—name confusion isn’t responsible for very many of them.

“The most common are errors that occur because the physician is not aware of some clinical data that hides in the electronic medical records,” Stein said, such as a medical condition other than the one being treated.