“I was asking people in these online groups, ‘Have any of you been denied opioids due to sexual abuse history?’ And women were coming forward.”
In the past few years, through a series of acquisitions and government contracts, a single company called Appriss has come to dominate the management of these state prescription databases. While the registries themselves are somewhat balkanized—each one governed by its own quirks, requirements, and parameters—Appriss has helped to make them interoperable, merging them into something like a seamless, national prescription drug registry.
For all the seeming complexity of these inputs, what doctors see on their screen when they call up a patient’s NarxCare report is very simple: a bunch of data visualizations that describe the person’s prescription history, topped by a handful of three-digit scores that neatly purport to sum up the patient’s risk.
Even after Kathryn had read up on NarxCare, however, she was still left with a basic question: Why hadbeen flagged with such a high score? She wasn’t “doctor shopping.” The only other physician she saw was her psychiatrist. She did have a prescription for a benzodiazepine to treat post-traumatic stress disorder, and combining such drugs with opioids is a known risk factor for overdose.
As Kafkaesque as this problem might seem, critics say it's hardly an isolated glitch. A growing number of researchers believe that NarxCare and other screening tools like it are profoundly flawed. According to one study, 20 percent of the patients who are most likely to be flagged as doctor-shoppers actually have cancer, which often requires seeing multiple specialists.
The current anti-opioid climate has its roots in the overmarketing of Purdue Pharma’s OxyContin in the mid-1990s. Between 1999 and 2010, opioid prescribing in the US quadrupled—and overdose deaths rose in tandem. To many experts, this suggested an easy fix: If you decrease prescribing, then death rates will decline too.
That began to change as the opioid epidemic escalated and demand grew for a simple tool that could more accurately predict a patient's risk. One of the first of these measures, the Opioid Risk Tool , was published in 2005 by Lynn Webster, a former president of the American Academy of Pain Medicine, who now works in the pharmaceutical industry.
The ORT, however, was sometimes sharply skewed and limited by its data sources. For instance, Webster found a study showing that a history of sexual abuse in girls tripled their risk of addiction, so he duly included a question asking whether patients had experienced sexual abuse and codified it as a risk factor—for females. Why only them? Because no analogous study had been done on boys.
Appriss started out in the 1990s making software that automatically notifies crime victims and other “concerned citizens” when a specific incarcerated person is about to be released. Later it moved into health care.
But in practice, algorithms that originate with law enforcement have displayed a track record of running in the opposite direction. In 2016, for example, ProPublica analyzed how COMPAS, an algorithm designed to help courts identify which defendants are most likely to commit future crimes, was far more prone to incorrectly flag Black defendants as likely recidivists.
Early in the reporting of this piece, Appriss declined WIRED’s request for an interview. Later, in an emailed response to specific questions about its data sources, the company made a startling claim: In apparent contradiction to its own marketing material, Appriss said that NarxCare’s predictive risk algorithm makes no use of any data outside of state prescription drug registries.
Moreover, experts say, even the most simple, transparent aspects of algorithms like NarxCare—the tallying of red flags meant to signify “doctor-shopping” behavior—are deeply problematic, in that they’re liable to target patients with complex conditions.
The result of all that speed, and all that fear, says Kertesz, is that patients who have chronic pain but do not have addictions can end up cut off from medication that could help them. In extreme cases, that can even drive some chronic pain sufferers to turn to more dangerous illegal supplies, or to suicide.
After that, Schechtman says, the doctor became even more abrupt. “Due to that I cannot give you any type of IV pain medication,” she recalls him saying. When she asked why, she says he claimed that both IV drug use and child sexual abuse change the brain. “‘You’ll thank me someday, because due to what you went through as a child, you have a much higher risk of becoming an addict, and I cannot participate in that,’” she says she was told.
Schechtman eventually joined an advocacy group called the Don’t Punish Pain Rally. Together with other activists in the group, she discovered that the question about sexual abuse history in the ORT unfairly targeted women, but not men. “I dress nicely. I look nice. And I would be friendly,” he says. “And as soon as they get my driver's license, oh boy, they would change attitudes. I couldn't figure out why.”
But like the others, Ward has been unable to get his problem fixed. And since most states now require that physicians and pharmacists use these databases, millions are potentially affected. One survey of patients whose providers have checked these systems found that at least half reported being humiliated and 43 percent reported cuts in prescribing that increased pain and reduced quality of life.
Intrigued, she wanted to know more. So in the late 2010s, having become an assistant professor at Northeastern University, she decided to simulate the machine-learning model that generates NarxCare’s most algorithmically sophisticated measure, the Overdose Risk Score. “The problem with all of these algorithms, including the one I developed,” Kilby says, “is precision.” Kilby's complete data set included the files of roughly 7 million people who were insured by their employers between 2005 and 2012. But because opioid addiction is so rare in the general population, the training sample that the algorithm could use to make predictions was small: some 23,000 out of all those millions.
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