A Drug Addiction Risk Algorithm and Its Grim Toll on Chronic Pain Sufferers

Spread the love

The reason these cuts hadn’t worked, some experts believed, was that they had failed to target the patients at highest risk. Around 70 percent of adults have taken medical opioids—yet only 0.5 percent suffer from what is officially labeled “opioid use disorder,” more commonly called addiction. One study found that even within the age group at highest risk, teenagers and people in their early twenties, only one out of every 314 privately insured patients who had been prescribed opioids developed problems with them.

Researchers had known for years that some patients were at higher risk for addiction than others. Studies have shown, for instance, that the more adverse childhood experiences someone has had—like being abused or neglected or losing a parent—the greater their risk. Another big risk factor is mental illness, which affects at least 64 percent of all people with opioid use disorder. But while experts were aware of these hazards, they had no good way to quantify them.

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 (ORT), was published in 2005 by Lynn Webster, a former president of the American Academy of Pain Medicine, who now works in the pharmaceutical industry. (Webster has also previously received speaking fees from opioid manufacturers.)

To build the ORT, Webster began by searching for studies that quantified specific risk factors. Along with the literature on adverse childhood experiences, Webster found studies linking risk to both personal and family history of addiction—not just to opioids but to other drugs, including alcohol. He also found data on elevated risk from particular psychiatric disorders, including obsessive-compulsive disorder, bipolar disorder, schizophrenia, and major depression.

Gathering all this research together, Webster designed a short patient questionnaire meant to suss out whether someone possessed any of the known risk factors for addiction. Then he came up with a way of summing and weighting the answers to generate an overall score.

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. The gender bias that this introduced into the ORT was especially odd given that two-thirds of all addictions occur in men.

The ORT also didn’t take into account whether a patient had been prescribed opioids for long periods without becoming addicted.

Webster says he did not intend for his tool to be used to deny pain treatment—only to determine who should be watched more closely. As one of the first screeners available, however, it rapidly caught on with doctors and hospitals keen to stay on the right side of the opioid crisis. Today, it has been incorporated into multiple electronic health record systems, and it is often relied on by physicians anxious about overprescription. It’s “very, very broadly used in the US and five other countries,” Webster says.

In comparison to early opioid risk screeners like the ORT, NarxCare is more complex, more powerful, more rooted in law enforcement, and far less transparent.

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. After developing a series of databases for monitoring prescriptions, Appriss in 2014 acquired what was then the most commonly used algorithm for predicting who was most at risk for “misuse of controlled substances,” a program developed by the National Association of Boards of Pharmacy, and began to develop and expand it. Like many companies that supply software to track and predict opioid addiction, Appriss is largely funded, either directly or indirectly, by the Department of Justice.

NarxCare is one of many predictive algorithms that have proliferated across several domains of life in recent years. In medical settings, algorithms have been used to predict which patients are most likely to benefit from a particular treatment and to estimate the probability that a patient in the ICU will deteriorate or die if discharged.

In theory, creating such a tool to guide when and to whom opioids are prescribed could be helpful, possibly even to address medical inequities. Studies have shown, for instance, that Black patients are more likely to be denied medication for pain, and more likely to be perceived as drug-seeking. A more objective predictor could—again, in theory—help patients who are undermedicated get the treatment they need.

Leave a Reply