Big Data Could Solve The Prescription Drug Epidemic

Big data and Big Pharma are coming together to better understand and prevent prescription drug abuse.


Big data is becoming an essential part of nearly every industry. It’s changing the way we look at data collecting and processing, but it has the potential to save lives across the country. 

Prescription drug addiction is a growing epidemic. Until now, officials didn’t have the tools to collect the information they needed to stop it. With big data in their corner, it might be possible to solve the prescription drug epidemic that is sweeping our nation.

An Epidemic of Prescription Drug Abuse

It’s difficult to put an exact figure on the number of individuals in the United States who are addicted to painkillers or who have transitioned to heroin use as a direct result of that addiction.

Many of those affected don’t seek help and hide their addiction, but about 2.1 million adults in the United States are suffering from substance abuse related directly to prescription pain medication. If that sounds like an enormous amount of people, consider that 80% of the world’s pain medicine is consumed in the U.S.

Big Data Offers New Tools

Big data describes the application of predictive analytics to large swaths of data. Businesses like doctors’ offices and pharmacies gather large amounts of patient data every day — names, demographic information, diagnosis details, prescription information and a variety of other tidbits that might seem useless.

By taking this information and applying predictive analytic algorithms to it, medical professionals and pharmaceutical companies will be able to predict trends, target problem areas and even see patterns that could alert them to possible addictive behavior.

Big Data vs. Opioid Addiction

Big data has the potential to turn the prescription drug epidemic on its ear. By applying the algorithms that we discussed a moment ago, pharmaceutical companies can discover red flags that might be missed by a person examining the same data. Possible red flags might be:

  • Increased opioid prescriptions in a general area or for a particular demographic. This could include increased numbers of prescriptions being filled at a particular pharmacy in a problem area as well.
  • ‘Doctor shopping’ — patients switching between multiple physicians in a short period of time. When related to opioid addiction, this behavior often occurs when a patient is having difficulty obtaining prescriptions to which they’ve become addicted.
  • More opioids being resold illegally on the streets — when the drugs are easier to obtain, they will inevitably be resold more often.

By finding these red flags, it’s easier to determine where problem areas are. The Centers for Medicare & Medicaid Services (CMS), for example, puts these information points on a ‘heat map’ that allows them to specifically target areas where opioid prescriptions are becoming a problem.

Preventing Fraud With EPCS

Another big part of the big data powerhouse that is already having an impact on the opioid epidemic is the EPCS system. It stands for Electronic Prescriptions for Controlled Substances, and it requires that prescriptions for substances such as pain killers are submitted to pharmacies electronically. It also requires that participating physicians are approved to prescribe controlled substances.

It isn’t a perfect solution, especially since it isn’t widely implemented yet — less than six percent of professionals use it even though more than 80% of pharmacies are EPCS enabled — but it could significantly reduce the amount of fraudulent opioid prescriptions, thereby reducing the number of Americans who become addicted to such substances every year.

Big Data vs. the Human Mind

Big data might be one of the most effective tools in combating prescription fraud and opioid addiction, but it has one major flaw — it doesn’t take into account the sometimes unpredictable variables of the human mind. That is why researchers have begun applying big data to the data collected during drug abuse and addiction research.

By taking decades worth of drug abuse research data and applying the same predictive algorithms to it, big data can help researchers and pharmaceutical companies predict the kind of person who might be prone to opioid addiction, based on biological, societal and environmental factors. While it might not be able to target an exact person, it can provide a baseline for physicians so they know what to be on the lookout for.

The sheer amount of information that doctors across the country collect every single day is mind-boggling. Before now, collating and making sense of that information was an impossible task. Even now, HIPAA compliance might be a stumbling block.

The implementation of big data in the medical and pharmaceutical industries could change the way we look at medicine, but its biggest impact right now could be in the analysis and prevention of opioid addiction. The prescription painkiller epidemic is a real problem in the United States.

If we can use big data to help reduce the number of new addicts or find new ways to help those who are already suffering from addiction, aren’t we under an obligation to do so? Big data could potentially offer the tools we need to get a handle on an issue that has become endemic in the U.S.

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