1/How to fix a problem in addiction treatment: a Train the Trainer model to expand #opioid use disorder treatment, tackling barriers: knowledge (providers & administrators), workload, innovation fatigue, telehealth, credentialing. In @SubstanceAbuseJpubmed.ncbi.nlm.nih.gov/32697170/
2/The key was to address this reality: there is considerable failure to reach patients with OUD with medication based therapies in @VeteransHealth (same as outside) with only a small % (often just 1/4 or less) obtaining treatment
3/The SCOUTT Initiative drew 13 staff from each VA network, with 4 leaders in mental health, substance use, primary care, pain services, and 7 multidisciplinary staff (pharmacists, nurses, physicians, therapists) - for special training- conference & community of practice
4/They implemented two LEVEL 1 clinics (addiction treatment via primary care, mental or pain settings) within 12 months and another within 24 months. Heavy emphasis on nurse collaboration
5/As this began, SCOUTT leaders anticipated PRECISELY those challenges our health care system fails to consider when implementing change: competing time demands, stigma of OUD, overworked front line staff, and lack of incentive for real change. That required more resources
6/Crucial for reporters: Reporting on "lack of treatment" or "prescribing opioids" is too narrow. The question to ask, for rehabilitation, pain, or addiction is this:
"what tangible steps should health systems and their payers take to implement change successfully".
7/Hats off to this guy (left side) who led the SCOUTT Initiative to expand OUD treatment in VA, @vahsrd investigator, clinician and nurturer-in-chief @AJ_Gordon#mensch #Tristan
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Truth💣 1/ The “NARXCare” opioid Rx risk algorithm is in all Prescription Monitoring Databases,ie ~1 bn Rx’s/year
NOW in @JournalGIM
✅evidence does not yet exist to support it as safe or protective
✅It has flourished due to lack of federal oversight link.springer.com/article/10.100…
2/The authors, led by Dr Michele Buonara, review the core argument as one in which this algorithm with low evidence to its favor
and high risk of harm
has gone unregulated
despite apparently fulfilling @US_FDA criteria that mandate it be regulated
3/Nearly all prescribers and national pharmacies now see the Bamboo Health, Inc proprietary “NARXcare” algorithm in a more prominent position *than the prescription history itself” when they view a prescription history.
1/Arguing for methadone deregulation, Dr. Ruth Potee notes that in an auditorium of 400 addiction specialists, almost NONE prescribe methadone (because they can't)
"Methadone is a miracle drug that no one has access to"
There are more people who offer Botox than offer methadone
Patient: “I can still do my activities”.
Doc: "No way, not really. I read the SPACE trial, and there is NO benefit (that would outweigh the opioids’ risk)”
"Shared decision-making" seems *doomed* here
1/I watch with concern as DEA prosecutions of MDs still seem to rely on “they prescribed more than I would” despite a 9-0 ruling of
Supreme Court last year
Sudden termination of opioids & progressive abandonment of 5-8 million patients is dangerous
1/Even on inpatient rounds, it is possible to introduce the idea that addiction isn’t (only) in the brain.
I contrast @NIAAAnews “brain disease” against a behavioral economics vide substance use as a pattern of behavior occurring in relation to environmental context
2/On teaching rounds we read aloud and discussed the @NIAAAnews brain-science model of addiction, pulling just a few lines off their website
3/then we read lines from Chapter 39 of “Evaluating the Brain Disease Model of Addiction” - this presents harmful substance use as a pattern of behavior based on assessment of competing rewards, delay or uncertainty of desired rewards, risks and costs - ie behavioral economics
1/In thinking about the OPAL opioid Trial (as 1st line treatment for back pain) - and other trials, I want to model an idea that I welcome others to shoot down or support
Comparing mean effects of opioid to placebo as 1st line treatment
2/studying the average effect for a treatment with very ⬆️ variability of “benefit” and “aversive” responses is confusing
it makes comparisons to placebo a bit of a mess.
Here is my hypothetical graph of a placebo’s average range of aversive impacts and beneficial impacts
3/With placebo - I suggest- whatever bad effects people feel (even if they are not truly “caused” by placebo) or benefits are either along some narrow range.