1/In this randomized trial of 421, a Housing First approach ended homelessness for 86%, vs 36% for usual care. This was among extremely vulnerable persons- mental health ER visits ⬇️. No difference in acute hospitalizations or overall ER. Check 🔽
2/Some key points: for people who argue that Housing First will save 💰 due to reduced health care use, the data from trials are not consistently showing that. People who are really ill are still in need of health & social care when housed. But sometimes you reduce ED use
3/With @MKushel we spoke to the moral, ethical & financial proposition here in @NEJM in 2016 : nejm.org/doi/full/10.10… But to end “this person’s homelessness” the offer of some housing subsidy is our current strongest intervention for persons with long term homelessness
4/I do know that for some, typically less disabled or less ill, more modest $ supports or targeted treatment _that they choose_ will be their way back. I worked with addiction researchers Milby & Schumacher, showing strengths & limits of such approaches link.springer.com/article/10.100…
5/But after 30 years of research ..”The only interventions we know that reduce homelessness more effectively than waiting and hoping involve some form of housing subsidy” (O’Flaherty). It’s not always easy to do RIGHT, but that is what we know so far) feantsaresearch.org/public/user/Ob…
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🧵1/Our @uabmedicine Grand Rounds will feature a diagnostic showdown between Dr Martin Rodriguez and ChatGPT4
I am scared here because I don’t want AI to win
2/the case features behavioral changes, swearing, cognitive decline, cough, progressive weakness over 3 years.
I wonder about infectious and rheumatic disorders. Maybe primary neurological
Aspirations after a cognitive change is possible
Dr Rodriguez opens. Not much to go on.
3/ChatGPT generated a lot of text read by Dr Kraemer but it is pretty good, with emphasis on neurological disorders followed by a disclaimer “please note that this does not substitute for professional medical advice”. Both want more information
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