Ending the Opioid Crisis: Could Big Data Help?

Implementing data-based solutions will take concerted will and effort from legislators and the healthcare industry at large

September 6, 2017
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No doubt you’ve heard opioid abuse is raging out of control in America. Look at the news sites each day and you’ll see at least one headline focused on the opioid crisis. The crisis has all the hallmarks of an epidemic, because it kills people indiscriminately and quickly. According to the Department of Health and Human Services, 78 people die everyday from opioid overdoses. And each day, 3,900 people take opioids without a prescription.

Unlike a disease, opioid abuse at a widespread level is a matter of choice. While the abusers and addicts may not feel they have much of a choice once they’re hooked, pharmacies, doctors, and drug companies have a choice when it comes to the dissemination of opioids.

This is a complex issue. Of course users have a choice to do their best not to get hooked. But what if real pain is what gets them started? Doctors prescribe opioids to help patients deal with all sorts of pain, and a great many patients do indeed need effective pain medication for severe pain.

But the data on pharmacy practice doesn’t lie: doctors prescribe enough opioids for every adult in America to possess a bottle of pills whenever they want to get their hands them. About 50 percent of women who end up in a methadone clinic, where they’re treated for opioid addiction, got there because they got hooked on opioids from prescriptions. And 20 percent of prescribed painkiller users share them. That’s how people without prescriptions are getting hooked.

No doubt there’s absolutely no need for that many pills floating around. Part of this speaks to the power of addiction. Once a user gets hooked, they’d rather scheme their way into getting more pills than go through withdrawals. Another part speaks to a lack of top-down data strategy and proper monitoring on the part of the healthcare industry. Yet another part speaks to the power of money.

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Big Data Strategy and Opioids

The strategy sounds simple enough: monitor data on how often patients are refilling prescriptions, then limit refills based on actual need. This is actually very complicated. It comes down to figuring out who’s lying and who’s not. Who’s telling the truth about their pain and their need for more pills? When subjectivity comes into the mix, it’s very tough to figure out a right or wrong answer.

To figure it out, doctors and pharmacies need access to widespread data on past results. They already have prescriptive information, i.e. the medically-accepted information on how long a specific condition will cause severe pain. But if they had accurate, up-to-date statistical information on how long it has taken success cases to recover in the past and cease taking opioids altogether, they could understand when a patient’s refill pattern is an anomaly and a red flag. Then, a patient at the pharmacy window asking for more opioids could be referred back to the doctor, who could diagnose whether the patient actually needs them.

Greg Horne, Canada’s National Healthcare Lead, points out that, in order for analytics to have any efficacy against the opioid crisis, all of the stakeholders involved in healthcare “must work together by sharing data and creating a flow of information.” In America, this type of organization is just not there. A lot of that has to do with money.

Money, Data, and the Pharmaceutical Industry

Drug companies have incentive to keep pumping out opioids, because the money from them keeps flowing in. According to McKinsey & Company, “Many pharmaceutical companies are wary about investing significantly in improving big-data analytical capabilities, partly because there are few examples of peers creating a lot of value from it.”

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Yet McKinsey Global Institute estimates big data strategies could pump $100 billion annually into the U.S. healthcare system. In terms of opioids, this makes sense, because prescription opioid abuse is part of $25 billion in excess healthcare costs annually.

Unlike the rest of America’s commercial world, the healthcare sector is adverse to risk. Why sink money into R&D involving big data and curbing opioid abuse when the risk may not pay off? Because people are dying, and that’s not good for business. So, McKinsey recommends a number of big data “prescriptions” for the pharmaceutical industry. Several of these are highly applicable to the opioid crisis:

  • Use smart tech: Smart pills and smart bottles can transmit data on patient drug use, while connected tech can monitor health issues and tell doctors when a patient actually has a problem that require more pills
  • Drop data silos and collaborate: At every level of healthcare, there’s a lot of data available, but it’s not shared between stakeholders; pharmaceutical companies, doctors, hospitals, healthcare providers, pharmacies, and the FDA must share data with the express purpose of ending the opioid crisis
  • Integrate data at each level: Applying data throughout the system would require a centralized database that adheres to confidentiality standards; blockchain could be a candidate for this
  • Use new research, technology and data on efficacy to come up with alternatives: There may be new drugs that would be effective substitutes for opioids, such as certain strains of marijuana

Implementing data-based solutions will take concerted will and effort from legislators and the healthcare industry at large. The pharmaceutical industry may be just as addicted to opioids as the drug abusers themselves. It’s up to the rest of us to stage an intervention.