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kestrellKes: This isn't just a random algorithm affecting a small subgroup of individuals: this algorithm is funded by the Department of Justice, and it accesses a large amount of what should be very private information, including criminal records and how far you travel for doctors and prescriptions. Aside from the various race, economic, and gender biases that might be affecting the algorithm, perhaps we should be concerned that the DOJ could use this algorithm and the information it makes use of to have a one-stop access to what you may consider to be your private health care information. Remember the other definition of "nark": someone who provides information about you to the government.
A Drug Addiction Risk Algorithm and Its Grim Toll on Chronic Pain Sufferers
WIRED
MAIA SZALAVITZ
08.11.2021 06:00 AM
https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/#main-content
Like most people, Kathryn had never heard of NarxCare, so she looked it up—and discovered a set of databases and algorithms that have come to play an increasingly central role in the United States’ response to its overdose crisis.
Over the past two decades, the US Department of Justice has poured hundreds of millions of dollars into developing and maintaining state-level prescription drug databases—electronic registries that track scripts for certain controlled substances in real time, giving authorities a set of eyes onto the pharmaceutical market. Every US state, save one, now has one of these prescription drug monitoring programs, or PDMPs. And the last holdout, Missouri, is just about to join the rest.
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. It has also gone well beyond merely collecting and retrieving records, developing machine-learning algorithms to generate “data insights” and indicating that it taps into huge reservoirs of data outside state drug registries to arrive at them.
NarxCare—the system that inspired Kathryn’s gynecologist to part ways with her—is Appriss’ flagship product for doctors, pharmacies, and hospitals: an “analytics tool and care management platform” that purports to instantly and automatically identify a patient’s risk of misusing opioids.
On the most basic level, when a doctor queries NarxCare about someone like Kathryn, the software mines state registries for red flags indicating that she has engaged in “drug shopping” behavior: It notes the number of pharmacies a patient has visited, the distances she’s traveled to receive health care, and the combinations of prescriptions she receives.
Beyond that, things get a little mysterious. NarxCare also offers states access to a complex machine-learning product that automatically assigns each patient a unique, comprehensive Overdose Risk Score. Only Appriss knows exactly how this score is derived, but according to the company’s promotional material, its predictive model not only draws from state drug registry data, but “may include medical claims data, electronic health records, EMS data, and criminal justice data.” At least eight states, including Texas, Florida, Ohio, and Michigan—where Kathryn lives—have signed up to incorporate this algorithm into their monitoring programs.
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.
Appriss is adamant that a NarxCare score is not meant to supplant a doctor’s diagnosis. But physicians ignore these numbers at their peril. Nearly every state now uses Appriss software to manage its prescription drug monitoring programs, and most legally require physicians and pharmacists to consult them when prescribing controlled substances, on penalty of losing their license. In some states, police and federal law enforcement officers can also access this highly sensitive medical information—in many cases without a warrant—to prosecute both doctors and patients.
In essence, Kathryn found, nearly all Americans have the equivalent of a secret credit score that rates the risk of prescribing controlled substances to them. And doctors have authorities looking over their shoulders as they weigh their own responses to those scores.
A Drug Addiction Risk Algorithm and Its Grim Toll on Chronic Pain Sufferers
WIRED
MAIA SZALAVITZ
08.11.2021 06:00 AM
https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/#main-content
Like most people, Kathryn had never heard of NarxCare, so she looked it up—and discovered a set of databases and algorithms that have come to play an increasingly central role in the United States’ response to its overdose crisis.
Over the past two decades, the US Department of Justice has poured hundreds of millions of dollars into developing and maintaining state-level prescription drug databases—electronic registries that track scripts for certain controlled substances in real time, giving authorities a set of eyes onto the pharmaceutical market. Every US state, save one, now has one of these prescription drug monitoring programs, or PDMPs. And the last holdout, Missouri, is just about to join the rest.
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. It has also gone well beyond merely collecting and retrieving records, developing machine-learning algorithms to generate “data insights” and indicating that it taps into huge reservoirs of data outside state drug registries to arrive at them.
NarxCare—the system that inspired Kathryn’s gynecologist to part ways with her—is Appriss’ flagship product for doctors, pharmacies, and hospitals: an “analytics tool and care management platform” that purports to instantly and automatically identify a patient’s risk of misusing opioids.
On the most basic level, when a doctor queries NarxCare about someone like Kathryn, the software mines state registries for red flags indicating that she has engaged in “drug shopping” behavior: It notes the number of pharmacies a patient has visited, the distances she’s traveled to receive health care, and the combinations of prescriptions she receives.
Beyond that, things get a little mysterious. NarxCare also offers states access to a complex machine-learning product that automatically assigns each patient a unique, comprehensive Overdose Risk Score. Only Appriss knows exactly how this score is derived, but according to the company’s promotional material, its predictive model not only draws from state drug registry data, but “may include medical claims data, electronic health records, EMS data, and criminal justice data.” At least eight states, including Texas, Florida, Ohio, and Michigan—where Kathryn lives—have signed up to incorporate this algorithm into their monitoring programs.
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.
Appriss is adamant that a NarxCare score is not meant to supplant a doctor’s diagnosis. But physicians ignore these numbers at their peril. Nearly every state now uses Appriss software to manage its prescription drug monitoring programs, and most legally require physicians and pharmacists to consult them when prescribing controlled substances, on penalty of losing their license. In some states, police and federal law enforcement officers can also access this highly sensitive medical information—in many cases without a warrant—to prosecute both doctors and patients.
In essence, Kathryn found, nearly all Americans have the equivalent of a secret credit score that rates the risk of prescribing controlled substances to them. And doctors have authorities looking over their shoulders as they weigh their own responses to those scores.
no subject
Date: 2021-09-10 01:32 pm (UTC)This seems the sort of thing that I should ask if my own GP is subject to.
Also, sending the Wired link to CBC Radio's tech-and-society show Spark.
no subject
Date: 2021-09-10 11:16 pm (UTC)no subject
Date: 2021-09-10 11:42 pm (UTC)no subject
Date: 2021-09-11 08:49 am (UTC)"Kilby’s research also identified an even more fundamental problem. Algorithms like hers tend to flag people who’ve accumulated a long list of risk factors in the course of a lifetime—even if they’ve taken opioids for years with no reported problems. Conversely, if the algorithm has little data on someone, it’s likely to label them low risk. But that person may actually be at higher risk than the long-term chronic pain patients who now get dinged most often."
This is so much worse than the TSA's no-fly list. This can ruin people's lives.
no subject
Date: 2021-09-11 08:50 am (UTC)"In other words, the algorithm essentially cannot do what it claims to do, which is determine whether writing or denying someone’s next prescription will alter their trajectory in terms of addiction. And this flaw, she says, affects all of the algorithms now known to be in use."
Translation: The tool doesn't work.
More from this writer
Date: 2021-09-11 11:38 am (UTC)That was a great article. Its author, Maia Szalavitz, goes deeper into addiction, its ineffective treatments and successful management, than most writers. She's commited to ignore the standard "war on drugs" narrative, looking at policies and neuroscience. She's written four books and a bunch of articles.
https://maiasz.com/other-writing/
Re: More from this writer
Date: 2021-09-11 12:23 pm (UTC)no subject
Date: 2021-09-11 12:25 pm (UTC)But instead, with each passing year, I also saw the rise of "Greed and selfishness is good for its own sake." And that's the ideology that seems to have won the race.
no subject
Date: 2021-09-11 02:42 pm (UTC)no subject
Date: 2021-09-11 05:38 pm (UTC)I can't remember the last time I watched a fictional T.V. series on a regular basis. My aide does, though. And now that I'm only aware of mainstream television through eavesdropping, I'm flabbergasted by how often the story is driven forward by a woman's screams, and furthermore, how much that failed to register back when I did watch TV out of habit. 😕