Some experts believe that the reason these cuts did not work is that they failed to target the highest-risk patients. Approximately 70% of adults have taken medical opioids-but only 0.5% have the official label “opioid use disorder”, commonly referred to as addiction. A study found that even in the highest-risk age groups, teenagers and people in their early twenties, only 1 out of 314 privately insured patients who took opioids had problems.
Over the years, researchers have known that some patients are more addictive than others. For example, research shows that the worse a person’s childhood experiences—such as being abused, neglected, or losing their parents—the greater their risk. Another big risk factor is mental illness, which affects at least 64% of patients with opioid use disorder. However, although experts are aware of these hazards, they do not have a good way to quantify them.
With the escalation of the opioid epidemic and the growing demand for a simple tool that can more accurately predict patient risk, this situation is beginning to change. The first indicator of these measures is the Opioid Risk Tool (ORT), released in 2005 by Lynn Webster, the former chairman of the American Academy of Pain Medicine and now in the pharmaceutical industry. (Webster had previously received lecture fees from opioid manufacturers.)
In order to build ORT, Webster began to look for research to quantify specific risk factors. In addition to the literature on adverse childhood experiences, Webster also found that research links risk to personal and family addiction history—not only with opioids, but also with other drugs (including alcohol). He also found data on increased risks for certain mental illnesses, including obsessive-compulsive disorder, bipolar disorder, schizophrenia, and major depression.
Webster put all these studies together and designed a short patient questionnaire designed to determine whether someone has any known risk factors for addiction. Then he came up with a way to summarize and weight the answers to produce a total score.
However, ORT is sometimes severely distorted and restricted by its data sources. For example, Webster found that a study showed that girls’ history of sexual abuse tripled their risk of addiction, so he appropriately asked whether the patient had experienced sexual abuse and included it as a risk factor. ——For women. Why are they only? Because no similar research has been conducted on boys. Given that two-thirds of addictions occur in men, this gender bias that introduces ORT is particularly strange.
ORT also does not consider whether the patient is taking opioids for a long time without becoming addicted.
Webster said he did not intend to use his tools to refuse pain treatment-just to determine who should be watched more closely. However, as one of the first screening tools available, it quickly attracted doctors and hospitals keen to stand on the right side of the opioid crisis. Today, it has been incorporated into multiple electronic health record systems and is often relied on by doctors who are worried about over-prescribing. Webster says it is “very, very widely used in the United States and five other countries.”
Compared with early opioid risk screening tools such as ORT, NarxCare is more complex, more powerful, more rooted in law enforcement, and much less transparent.
Appriss started to make software in the 1990s, and when a particular prisoner is about to be released, the software will automatically notify victims of crime and other “related citizens.” Later it entered the field of healthcare. After developing a series of databases for monitoring prescriptions, Appriss obtained the most commonly used algorithm at the time in 2014 to predict who is most likely to “abuse controlled substances”. This is a program developed by the National Association of Pharmaceutical Councils , And start to develop and expand it. Like many companies that provide software to track and predict opioid addiction, Appriss is primarily funded directly or indirectly by the Department of Justice.
NarxCare is one of many forecasting algorithms that have proliferated in many areas of life in recent years. In the medical environment, algorithms have been used to predict which patients are most likely to benefit from a particular treatment, and to estimate the likelihood that patients in the ICU will worsen or die after they are discharged from the hospital.
In theory, creating such a tool to guide the timing and target of opioid prescriptions may be helpful, and may even solve the problem of medical inequality. For example, research shows that black patients are more likely to be refused painkillers and more likely to be considered seeking medical treatment. A more objective predictor can—again, in theory—help patients who are drug-deficient get the treatment they need.