The future of predictive analytics is practically written in stone. If any pragmatic organization sees the potential for its business intelligence to expand, the exploration of all tools capable of achieving any degree of marketing advantage is inevitable. Boundaries will be pushed, some broken, some disassembled and re-magined. Business analytics solutions depend on constantly questioning the nature of the data it collects and runs on.
Especially in modern medicine
If the right mile markers are reached – including proper investments in analytics technology – international consulting firm McKinsey & Co. projected a $300 billion annual increase in medical-related profits by 2021, $200 billion of which would be generated from predicted health care cost decreases. Possessing the capability to get ahead of an illness, injury, or surgical complication using predictive data mapping can return untold benefits to patients and doctors alike, benefits that have nothing to do with money and everything to do with the preservation of human life.
But combine predictive analytics solutions with the pharmaceutical industry, and the byproduct is the oft dreaded “matchback.”
What is “matchback”?
Matchback is a process by which pharmaceutical companies circumnavigate age-old patient data protections to optimize consumer-directed advertising strategies while keeping the identity of all parties involved under lock and key – sort of.
When a patient is either prescribed a medication or switches his or her medication, a record is created by the pharmacy that sells the patient in question said medication. Data brokers can purchase these records in bulk, but only if the information has been encrypted.
At the same time, drug manufacturers hire media platforms to distribute an advertising campaign to a target audience, of whom the media platform has already collected information like their name s, home addresses, or telephone numbers. Just like before, this information is encrypted, usually by a third-party IT firm, utilizing the same algorithmic method the data brokers used to code the pharmaceutical record.
The data brokers compare their encrypted records with the IT firm’s. When the brokers happen upon identical patterns from both sides of the aisle, it’s called a “matchback.” The pharmaceutical company can now fuel its marketing strategies and predictive analytics solutions with comprehensive data without ever exposing a patient’s personal information to the open air.
Oaths versus the bottom line
In the practice of medicine, there are certain foundational pillars even the average laymen can rely upon – the Hippocratic Oath of “do no harm,” for one. Arguably another tenet, the pact of doctor-patient confidentiality, may be even more recognizable.
But as big data merges with Big Pharma, have the limits of this restriction evolved with modern medical landscape? Should they? Is it morally reprehensible to fudge medical confidentiality for a better return on investment, or does matchback follow the letter of the law in an effort to provide pharmaceutical providers the same competitive edge predictive analytics solutions gives businesses outside the field of medicine?
A fuzzy consensus
The protection of a patient’s confidential prescription from public scrutiny is – and hopefully will forever be – an inalienable right in the U.S., regardless of HIPAA wording. Yes, the pervasive arm of predictive analytics marketing toes the line around the doctor-patient sacramental safeguard for the sake of advertising profit for an industry already worth more than $1 trillion. For that, plenty are skeptical.
However, if effective, user-tailored marketing is Big Pharma’s only crime, then any saving grace from this encrypted data line dance will have to come from increased public awareness, perhaps even a call for proper third-party regulation and oversight.
That may the hardest pill to swallow for the continued, long-term health of data analytics.