Rise of the Anti-Opioid Algorithm

Jack Murtha
FEBRUARY 16, 2018
opioid data,algorithm opioid,aetna opioid,cerner opioid,healthcare analytics news

MY COUSIN DAVE WAVED HIS LIGHTER beneath a folded sheet of tinfoil, staring at the crushed blue pill as it melted into brown ooze along the crease. He dipped his head and inhaled the vapors through a hollowed pen pressed between his lips. The foil and pill snapped and fizzled as he slid the flame and his makeshift pipe down the line of chunky powder. Seconds later, he exhaled and sank into a serious opioid high.

Dave, a pseudonym to protect his identity, had become dependent on “blues,” slang for Roxicet, the prescription pain medication composed of oxycodone and acetaminophen. He smoked them every day, at first for fun and then to ward off the withdrawals that kept him from attending class and going to work. Several times, he came close to what he assumed were overdoses, smacking his own face to stay awake. Other addicts ransacked his family’s home for drugs, and a man once pointed a gun at him in a deal gone wrong.

Dave’s opioid abuse, with its undesirable effects, began about a decade ago, making him an early victim of the opioid crisis.

He also played a role in its rise. Dave needed a constant supply of pills, which sold for about $25 apiece on the black market. He began selling opioids and other prescription narcotics to fund his consumption. At first, he sold to other users, doctor-shopped, and stole the occasional blank prescription pad. Networking through the suburban underworld, he built more substantial connections. Soon he was taking weekly trips to a sketchy pain clinic, where gangsters took his cash and an elderly physician asked about fictional back pain and wrote a prescription for a Herculean bottle of opioids. When Dave left, he smoked some of the pills and sold the rest to smaller dealers.

Dave eventually got clean. But whether he could game the system today as he did then is unclear, if unlikely. If he were using now, a prescription drug monitoring program might nab him for doctor shopping, or a health insurer could notice the dirty doc’s peculiar behavior. Perhaps Dave’s prior medical history would have tipped off healthcare providers to his potential for opioid abuse before it ravaged his life.

Since the early days of the opioid epidemic, big data and analytics have rocketed in both power and prevalence. Stakeholders across healthcare, government, and academia are crunching numbers and merging previously disparate pieces of information to map and thwart its spread. Their efforts focus on everything from harm reduction and drug abuse prevention to scanning for alarming activity. Healthcare leaders and public health officials consider the use of data a key tactic in the larger fight.

The precise scope of the problem is blurred, but nearly everyone agrees it is overwhelming. Some expect opioids to kill half a million Americans over the next decade. In 2016, more than 42,000 people died as a result of opioid overdoses, 5 times more than in 1999, according to the CDC. The agency also claims that 2 million people in the United States misused or were addicted to prescription opioids in 2014. These numbers are expected to increase, a sign of a crisis so devastating that late last year, the president declared it a “public health emergency.” Healthcare professionals have labeled it “unprecedented.”

The effects that big data and tech will have on the opioid crisis remain to be seen. But campaigns have begun to shape how this country targets the issue, and more innovations are on the way. For hospitals and other healthcare organizations, it is crucial to see what is happening on the ground. Experts reached for this story said the industry needs total buy-in, and only by understanding these data-driven initiatives can hospitals prepare to strike partnerships or craft their own responses.

How Big Is the Opioid Crisis?

IN HIS OFFICE at the University of Virginia, economist Christopher Ruhm, PhD, was trying to solve a mystery. Over the years, he had shown that physical health improves when the economy stutters, but about 5 years ago, that dynamic was changing. In his quest to understand why, he cornered a suspect: a rise in poisoning mortalities. “I had no idea what poisoning deaths were,” he said. “I was thinking rat poisoning.”

It turned out that drug overdoses had caused more than 90% of poisoning deaths. The revelation surprised Ruhm, who decided to dig deeper. “But as I got into it,” he recalled, “I discovered that the data were more problematic than certainly I had known.” For example, roughly a quarter of overdose cases compiled by the CDC included no information on which drug caused the death. Ruhm settled on his next step: “Can we come up with better numbers?”

Computing his way through data sets, the investigator learned that drug-related deaths often involve multiple substances. In 2015, that was true about half the time, a statistic compounded by mortalities involving cocaine and fentanyl. “Drug cocktails” made ascribing deaths to a single culprit difficult, and the many possible narcotic combinations further complicated the picture.

Establishing baseline information is important, Ruhm said, because it provides the foundation from which healthcare providers, public health officials, and civic leaders act. If they do not know what exactly is killing people, they might stumble to provide relief. “Just understanding the dimensions of the problem—clearly, good data are critical for that,” Ruhm added. “We have to have good data to make good policy and, in this case, to make policies and interventions that could help to address the problem.”

Ruhm’s peers in social science are exploring policy options, how changes to public health insurance programs like Medicaid influence drug use, whether the greater availability of Naloxone (the medication that reverses the effects of an opioid overdose) encourages use, and to what extent physical changes to Oxycontin have pushed people to move to heroin. Ruhm, meanwhile, continues to analyze drug death data and advocate for more actionable data points, examining whether the economy or a change in opioid pricing and access is driving the epidemic.

What does it all mean for healthcare organizations and clinicians? They, too, must be on the lookout for bad data. And the more investigators like Ruhm toil behind the scenes, the better informed and prepared hospitals might be in the future—and that means patients could see better outcomes.

Become a contributor