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Some experts believed that the reason for not having these reductions was that they were not targeted at the highest risk patients. About 70 percent of adults take medical opioids, but only 0.5% officially suffer from “opioid use disorder,” commonly referred to as addiction. According to one study, within the highest-risk age group, adolescents and those in their thirties were the only ones with 314 private opioid-prescribed 314 patients who had problems with them.
Researchers have known for years that some patients were at higher risk of becoming addicted than others. Research has shown, for example, that when someone has had more harmful childhood experiences — such as being abused or forgotten or losing their parents — they are at greater risk. Another major risk factor is mental illness, which affects at least 64% of all people with opioid disorder. But even though experts were aware of these risks, they had no good way of counting them.
This began to change as the opioid epidemic intensified and the demand for a simple tool that could more accurately predict patient risk increased. One of the first of these measures, the Opioid Risk Tool (ORT), was published in 2005 by Lynn Webster, a former president of the Pain Medicine of American Academy who now works in the pharmaceutical industry. (Webster has also previously received talk rates from opioid manufacturers.)
To build ORT, Webster began looking for research that quantified specific risk factors. Along with the literature on harmful childhood experiences, Webster found research linking the risks to the personal and family history of addiction — not only with opioids, but also with other drugs, including alcohol. He also found data on the high risk of certain psychiatric disorders, including obsessive-compulsive disorder, bipolar disorder, schizophrenia, and severe depression.
Gathering all of this research, Webster designed a short questionnaire for patients to find out if anyone had any of the known risk factors for addiction. He then devised a way to combine and weight the answers to create an overall score.
ORT, however, was sometimes distorted and limited by sharp data. For example, Webster found a study that showed that the history of sexual abuse of girls tripled the risk of addiction, introduced a question to ask whether patients had sexual abuse and coded it as a risk factor for women. Why them alone? Because no analogous study was performed on the boys. This led to a particularly strange gender bias in the ORT, given that two-thirds of all addictions occur in men.
ORT also did not consider opioids prescribed to a patient without becoming addicted.
Webster says he had no intention of using his tool to deny treatment for the pain — to determine who should be cared for. Being one of the first screenings available, however, he quickly came across doctors and hospitals that wanted to continue on the right side of the opioid crisis. It has now been introduced into multiple electronic health record systems, and is frequently used by physicians concerned with over-prescribing. Webster says it is “very widely used in the United States and five other countries.”
Compared to early opioid risk studies like ORT, NarxCare is more complex, more powerful, more rooted in law enforcement, and much more transparent.
Appriss began making software that automatically notifies victims of crime and other “concerned citizens” in the 1990s when a specific inmate is about to be released. Later, he went to health care. After developing some prescribing control databases, in 2014 Appriss was the most widely used algorithm for predicting the highest risk of “misuse of controlled substances”, a program developed by the National Association of Pharmaceutical Committees, and began to develop and expand. Like many companies that provide software to track and predict opioid dependence, it is largely funded, directly or indirectly, by the Department of Justice.
NarxCare is one of the predictive algorithms that has proliferated in many areas of life in recent years. In medical settings, algorithms have been used to predict patients who may benefit from a particular treatment and to calculate the probability of deterioration or death if a patient in the ICU is discharged.
In theory, creating this tool to target when and to whom opioids are prescribed could be helpful, perhaps even resolving medical differences. Studies have shown, for example, that black patients are denied pain medications and perceived as drug-seeking. A more objective predictor (again in theory) can help non-medical patients get the treatment they need.
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