Stanford apologizes after vaccine allocation leaves out medical residents
npr.org
npr.org
You got that right. The way they talk about "an algorithm did it" makes it seem as as if they think that somehow explains it, like there was only one algorithm possible handed down from god or something.
We'll be seeing more and more of this of course. "We can't be blamed, it was an algorithm! We can't be blamed for trusting in the algorithm, because everyone knows algorithms are objective, right?"
I'm not sure "laypeople" realize that, especially in this particular case, "algorithm" is just a fancy word for "formula". Right, you MADE the formula, and it was wrong.
- Senator Soaper
https://quoteinvestigator.com/2010/12/07/foul-computer/#:~:t....
No need to guess.
"It used an algorithm that assigned each person a crude risk score, taking into account factors such as age, job description and the number of coronavirus cases that had been detected in their hospital department. That resulted in personnel like environmental services workers, food service workers and older employees being shuttled to the front of the line.
Residents, who are early in their careers and tend to be young, rotate throughout the hospital to train with various teams of physicians, making them difficult to place in a designated unit."
https://www.nytimes.com/2020/12/18/world/covid-stanford-heal...
In addition to having a bug for residents without designated units (which again, how did they not test it on a sample set including residents?), it sounds like they maybe weighted age (or department?) too much compared to job description. It doesn't seem like an older physician that is working from home should be weighted higher than nearly anyone working every day in the hospital, regardless of age or if other people in their department working in the hospital got sick already.
It seems like amount of contact with patients should have been weighted far higher than anything else (patients for elective procedures are required to get a virus test first, so amount of contact with untested patients like in ER, or covid+ patients like those who need to care for them, even higher). But I'm not a doctor or medical ethicist. If Stanford has a reason for not doing that, they can, well, explain it, if they want to look better. It makes me figure it was just not done very carefully.
It seems to me that formula was just not very good. While it's technically an "algorithm", sure, I think most of us in the field would just call that a "formula" not an algorithm. I think they use the phrase "algorithm" precisely because to the layperson it seems more mysterious, more sophisticated, more complicated, harder to question, less obvious that was just something humans did, possibly not very carefully.
I can't fully define the new meaning, but it's like the YouTube video recommendation algorithm which is more like a whole system than a single algorithm in the original sense, the Facebook algorithm for ranking items on your feed. These big data, machine learning, opaque models.
With the SQL example:
I don't think a set of rules is disqualified from being called an algorithm if it's implemented using some other tool or process, because we do this all the time: when programming, we split up implementations into functions, or we could have used the standard library's sort function too -- I would still consider it an algorithm no matter how the "sort the results" step ended up being implemented.
If the result is wrong, it is not necessarily because the implementation of SQL's ORDER BY is incorrect (it could be but it's unlikely for a popular SQL implementation), and if you know that the the rules are incorrect then I agree, I definitely wouldn't blame the sorting algorithm (at least initially, although it's possible it's also be wrong).
But the point that machine learning has become an ideal package for existing biases still is worth mentioning because machine learning becomes an ideal way to present mistakes involving values, such racism, as mistakes that are simply technical and can't be helped and this allows regular algorithms to also get this kind of pass.
https://en.wikipedia.org/wiki/Money_laundering
So the idea is, you have some bias in some process (racial, religious, whatever). You set up some algorithm that relies on the bias to make predictions or classifications. Now you can say it's not you that's biased, it's just the algorithm. The algorithm is some process by which your bias is "made legitimate".
It's not that you set up the algorithm to rely on the bias - ML trains on data produced by a biased system and ends up building a model containing the same biases - you don't need to "set-up" anything - if you're fine with the existing biases you can "launder" them through ML.
https://www.technologyreview.com/2016/07/27/158634/how-vecto...
As another example, predictive policing tries to place police in places with higher crime rates. Those crime rates are determined by looking at past history of police reports and arrests. That past history has human bias already in it, with disproportionately higher arrest rates in places with racial minorities. The effect of the predictive policing is to justify overpolicing of minorities.
https://en.wikipedia.org/wiki/Predictive_policing#Criticisms
Ironically, this makes the original point nearly as well: we need to evaluate the hell out of machine learning systems to make sure that they’re doing what we think they are and that they’re not keying off something else instead, especially something biased. To date, the field has been...not great about this.
Man is to Woman as Doctor is to ___ gives 1) gynecologist 2) nurse 3) doctors 4) physician 5) pediatrician
Woman is to Man as Doctor is to ___ gives: 1) physician 2) doctors 3) surgeon 4) dentist 5) cardiologist
These are just generally near "Doctor" though: the ten nearest terms are physician, doctors, gynecologist, surgeon, dentist, pediatrician, pharmacist, neurologist, cardiologist, and nurse.
Some gender differences may persist (nurse is #2 for `woman`, but #68 for `man`, but it's also near `woman` generally and you could imagine it gets a bit of a boost from the verb ("to feed a baby") being attached exclusively to women too.
Anyway, my point is not that there's no bias (there certainly can be--seed GTP-3 with a prompt about Muslims) but that one should be wary of thinking they know what the model is doing.
I'm being lazy, but the results for Gutenberg books you can check online at http://labs.statsbiblioteket.dk/dsc/
- man is to woman as doctor is to reprovingly (nurse is the first noun, on position 4) - woman is to man as doctor is to snodgrass (after a couple nonsense/rare words)
The most important thing that teaches us is that big corpora (bigger than PG) are essential for this method.
Depending on the input “homemaker” may be a technically reasonable output.
Should we use such a model to suggest career paths to high school students? Or should we reevaluate the methodology?
https://www.slideshare.net/yuyomajadero/jobs-occupations-pro...
I'd like to add that there's also a danger in people trying too hard to avoid bias and losing important information. For example, a man who's a homemaker instead of a programmer is a less attractive partner for a woman. So such an occupation might harm both his quality of life and that of his partner. There is some useful information encoded in cultural bias. Even if that information turns out to be entirely socially constructed, it still has real harmful effects on real humans who go against it.
I know some people who, fairly strongly, believe that ML is the future of non-biased decision making, yes.
> a man who's a homemaker instead of a programmer is a less attractive partner for a woman
That's definitely going to be [CITATION NEEDED].
This is much to sweeping a generalization to have a place here. You might say a man who is a homemaker is less attractive than a programmer to you, but don't speak for everybody else.
Who are we to decide for the woman what she should feel about it? Is bias absolute or relative, objective or subjective? Is there a one true policy for dealing with bias or can there be one?
I've never done any, but my understanding is that machine learning is just correlation. It's good at figuring out "what", but not "why". Consider training an algorithm to recognize horses by feeding it millions of pictures of horses. Eventually, the algorithm "learns" what a horse is, but it's definition of a horse is based on the inputs it was given by a human.
So now, consider the scenario where the millions of pictures of horses were all brown. If you give the algorithm a picture of a white horse, it'll tell you it's not a horse. If you give it a picture of a brown donkey, it might think it's a horse because it's learned to put too much emphasis on the color brown.
If that algorithm becomes relied on to define a horse, "the system" will insist there are no white horses even though you can walk outside and see them plain as day.
Now, apply the same kind of idea and feed an algorithm mugshots of all criminals. It's going to develop the same bias and tell you that a black person is more likely to be a criminal than a white person. There's no nuance. The inputs used to train the AI were tainted by decades of systematic discrimination, but the AI doesn't know that.
Of course you could try to take that input bias into account, but the whole sales pitch of machine learning is that you feed it tons of data and it gives you an objective result. As far as I know, no one is trying to quantify, and correct, the biases in the inputs.
The phrase "money laundering for bias" means the machine learning algorithms are used to re-enforce incorrect opinions and assumptions because it gives the excuse that an "objective" computer used cold hard data to draw the same conclusion.
Machine learning is one of the scariest parts of tech right now because it's the equivalent of an extremely stupid person that only understands correlation and not causality and the systems being built are going to be making a lot of decisions at scale.
No. Nobody training an ML system to detect criminals would train it only with pictures of criminals. And if you somehow did, it wouldn't determine that black people are more likely to be criminals, but that humans are more likely to be criminals than say ducks or fire engines.
Yes, ML models can end up reflecting prejudices in their training data, but this description is incorrect, reductionist and unhelpful.
I would, but the people in charge won't. Some of my family members MUST chat with a crappy bot before they can get support from their mobile phone provider and that's in Canada where we pay an astronomical amount of money for our phones/plans.
A penny saved is a penny earned, even if it costs someone else a dollar.
The first is statistical bias - feeding algorithms training data that is somehow unrepresentative of the "real world" (or more specifically, the actual class of data for the intended use case). As an example, applying facial recognition to Caucasian faces when the model was solely trained on Chinese faces, you're going to have a bad time. The problem was that your data was "biased", because you actually wanted a model that recognizes "human faces", but you trained on the biased subset of "Chinese faces".
The second is the ethical/political notion of "bias" against individuals; more concretely, the idea that a society is "just" when people are judged as individuals, and not prejudiced by their gender/skin colour/etc. In this respect, when we say "we should not be biased against men", we really mean that "an individual man should not be treated any differently from a woman, even though men are overwhelmingly perpetrators (and victims) homicide".
The complication is that reality is inherently imbalanced/biased. Society can be chopped up into a lot of sub-views that skew towards particular demographics. Some are relatively harmless. "OnlyFans content creators" aren't 50-50 men-women, and men aren't charging the same as women either. Some are not - "murderers" are mostly men, black men are overrepresented in the "criminal" group, and so on.
This raises some obvious questions:
1) Why is this the case? Is this the result of systemic discrimination? Historical oppression? Innate preference? Cultural pressure to conform?
2) If you can answer (1), how does that influence your view of what a "just" society is? For example, do you consider it to be "unjust" to be wary of men (and men only) to protect yourself from random physical violence when you're out and about?
3) Does everyone share your view on what a "just" society is?
4) How do these answers dictate what you should "do" about it? As a voter? As an ML practitioner? As a CEO?
I'm not going to delve further into these questions, because they cause a lot of contention and deserve more time/consideration than I can justify right now in a HN post.
The main reason I decided to comment is that I've seen too much debate that tries to steamroll people into accepting conclusions without considering or answering these questions. Even worse, some people are actively trying to silence others who simply want to discuss these questions, rather than swallowing their conclusions uncritically.
(This is not levelled at you, by the way, your comment just presented an opportunity to lay out my thoughts.)
I don't think anyone can meaningfully discuss the (ethical) concept of "bias" without first laying out a very comprehensive perspective of "society" that touches on all of these points.
There was a joint paper from Google, Facebook (and possibly others) about 2 years ago that I thought handled this exceptionally well. The authors addressed many of these questions honestly and objectively, and most importantly, acknowledged the potential for disagreement.
Bias are subjective assumption you have of the world and people.
So this quote is saying that machine learning hides the source of bias.
Imagine a dataset about healthcare outcomes. You want to know whether airlifting a patient is good or bad, so you train a machine learning model to predict mortality given airlifting a patient versus not airlifting them. Turns out a lot more of the airlifted patients died, and the model picks up on that, deciding that airlifting is dangerous.
Obviously, we’re airlifting the patients who are in the most dire circumstances, and that’s why they die more, but maybe the model doesn’t have the context/circumstance variables to see that, or maybe it’s just regularized and thinks those context variables are noise. So the bias of “airlift = bad” gets stuck in the model, but then people defend it as mathematically precise so it can’t be biased like a person can. They say that the model just reflects reality, pretending the model or data is wrong is blindly rejecting reality for the sake of political correctness.
It’s worse with really human stuff like recidivism because the context variables that might be useful (like the patient’s dire circumstances) tend to be very human and complex and they’re unlikely to be captured in a simple form. Even if they were, interpreting them might require human level AI. By analogy, it is frequently impossible (or just too hard to be worth it) for current technology’s ML to tell from the data we feed it that the patients being airlifted were most near death to begin with.
So you end up with an algorithm biased against airlifting patients getting defended as mathematically bulletproof.
"Money laundering for bias" in this context can be translated as "Finding a plausible excuse/cover for a specific bias (your computer/model says the same!)".
You employ a machine learning algorithm, feed it some data that shows black people in poverty have high risk of default on loans and avoid giving it data that shows otherwise. Train your neural network hard.
Now when people ask about why you won’t don’t give out many loans to blacks, throw your hands up and say “I would but our advanced machine learning algorithms say these people are high risk, we’re not racist we’re just following the results.”
> An algorithm was used to assign its first allotment of the vaccine. The algorithm prioritized health care workers at highest risk for COVID infections, along with factors like age and the location or unit where they work in the hospital. Residents did not have an assigned location, and along with their typically young age, they were dropped low on the priority list.
Whoever coded up the algorithm for personal COVID-risk probably forgot to take care of null-states for the location input. Residents had their location set to null, and were therefore prioritized lower than they should've been.
It's possible that the admins purposefully created this null-state bug so as to have a reasonable fallback story in the case they got caught, but per Occam's Razor, I think it's much more likely it was a dumb, honest mistake.
it's a rather important thing not to check on actual data, including residents in the sample, isn't it? they just never bothered to test the algorithm, didn't even review the results it spit out, before delivering the results as a plan? (it is quite possible indeed they didn't, I'm not saying that is a coverup for some other motive, I'm saying that is incompetent). What was the QA process like? Who was involved in it? From the article, apparently not any department heads or medical staff in general.
That's not a competent design process for something so important, is it?
That they never bothered to see how "the algorithm" treated residents demonstrates that they didn't give a shit about residents, not necessarily that they were intentionally screwing them.
The administration doesn't say much about how the algorithm was designed, they just say "oh, it was an algorithm." I think many people believe that "algorithms are objective" and they are trying to play that.
That said, I think the reason this story is getting so much attention is because of the assumed selfish intent. And, that assumption is probably wrong.
This wasn't some random HR benefits process that they screwed up. It was a triage process. A medical triage process. That's what they should be good at.
They've agreed it's wrong, and intend to fix it.
Send like a very average Thursday to me!
Enter some poor analyst working with a pile of messy data and “The Algorithm”.
If machine learning can be “money laundering for bias”, then “algorithms” can be money laundering for responsibility.
> While leadership is pointing to an error in an algorithm meant to ensure equity and justice, our understanding is this error was identified on Tuesday and a decision was made not to revise the vaccine allocation scheme before its release today
Meaning: administrators saw that the results were dumb, and didn't compensate.
From Facebook to the VW emissions scandal to Stanford’s vaccine allocation, people love narratives that blame engineers. I suspect this trend will only get worse as the general public realizes that engineers are now a highly compensated professional, similar to how lawyers are the butt of so many jokes.
So the responsibility should lie, as usual, with the people at the top, who set the direction for the company, not for low-level peons like engineers, who might be highly compensated, but the engineers don't set the direction for the company nor make the ultimate decisions regarding these algorithms.
But part of the tragedy of organizations is that responsibility tends to be diffused, so it's really hard to ultimately blame any one person.
There was a documentary (maybe called "The Corporation" or something) which showed some protestors outside some CEO's home, and the CEO's wife went out with some tea and cookies or something and invited the protestors inside their home to have a chat, and the CEO talked to the protestors and told them how helpless he himself was, as he was just part of the system with relatively limited ability to change it.
I'm not sure I buy that, and not sure the protestors did either, as the CEO still has enormous power. At the very least the CEO has the ear of the board of directors, and quite a lot of leeway as to how to run the business. They might not be able to change it all, but they can change a lot. Still, there's no denying that especially in a large organization no one person knows everything that's going on and can be accountable for absolutely everything, but leadership still exists and still is ultimately responsible.
Precisely. Nobody should accept that kind of obvious nonsense.
People are put into positions to make decisions. So make better decisions.
Some specific people in positions of responsibility that screw up badly see blaming engineers/tech as an easy way out of their responsibilities.
Similar outcome different thing.
And yet that classic example is also an example of people really not understanding the nuance of larger systems; in that specific case, there were concerns about graphite creating electrical issues in the ship's electrical systems.
Nobody's blaming software programmers (or "engineers")
Residents are generally under 34 and in good heath. In the USA, only ~2400 people 34 have died from COVID--and that is mostly people with comorbidites.
In comparison about ~250,000 people over 55 have died.
You'd obviously have do account for life-years lost, risk of exposure [1], etc. I wouldn't be shocked if giving the vaccine to a 25 year old resident is a sub-optimal choice.
[1]Based on my MiL's experience (an ER doc), the risk of infection at a hospital is a lot lower than it used to be because they have PPE now. She's more worried about catching it from her son who works in an office. That said, they need to take into account risk of exposure and risk that the residents spread it to others.
There's simply no excuse for not ensuring everybody in the hospitals, then in clinics, gets the vaccine before anyone else in the offices and working from home.
- Dune
Otherwise C Level folks who take a $1 salary but receive tons of options would be put at the bottom.
It's sort of like saying, "That's definitely a bug. It was unacceptable for me to put my code into production without ever testing it, and for that I take the blame. But I assure you that I did not intentionally write code designed to delete everyone's data."
Obviously this may or may not be what happened. It may even be what they're pretending happened.
The chief resident sent an email explaining the nature of the error. The chief resident is, per my limited understanding, a resident from the previous year who stays on in a leadership role. Not obvious to me that someone in that position explaining the root cause is problematic.
The email sent by a chief resident was simply passing on the administration’s explanation.
There's so many lessons why that is terrible, like the fighter cockpit design history, and yet it's super common for people to stop all inquisition when the average shows them what they hope is true across all subgroups.
The people/organization deploying a system are responsible for evaluating the system and how it might fail. You can’t just throw up your hands and go “Well, we had that data and this network so...it is what it is.” You could have fit a different model—-or not at all.
Stanford is not the only hospital system to restrict access to the vaccine from frontline residents. I can name 3 other local hospital systems in my city that have vaccinated administrative & C-suite/VP level staff before doctors, nurses, and other frontline employees. If vaccine allocation is getting messed up this early on within these closed systems, I can't help but think the next 2-3 phases will go awry as well--what checks are in place to ensure these vaccines get distributed to grocery store workers before people who are willing to pay more to get it early?
I don't understand why society is putting up with this. Right now if you're not in a daily COVID-facing role (i.e. an actual front line medical worker) or in a nursing home you should not be getting the shot. This makes my blood boil. There should have been laws passed regarding ordering of the distribution with criminal penalties for line jumpers like this.
There's an article in our local paper with a happy picture of one of our state's congressional representatives (a healthy 34-year-old!) getting the shot. Like WTF? There's doctors and nurses who are treating covid patients who can't get it yet. Why the heck does Congress get priority over them?
Not only do these people have no shame, half of them even have the nerve to brag about it to the rest of us plebes who will have to wait months or more to get it.
Stanford resident acted. Health care workers at those other hospitals are apparently silent. That makes the difference.
What ever happened to starting out with assuming good intentions?
Right now there's a huge shortage of vaccines compared to the number of people who are both eligible and willing. No need to worry about the unwilling at this point since there's not enough to go around anyway.
Congress took it because they think they're more important than the rest of us, and apparently they think they're even more important than the frontline workers who are still waiting.
https://en.wikipedia.org/wiki/United_States_federal_governme...
Of course it is unlikely that congress would ever change that law...
Now - could he have just vocally stated "I am your congressional rep - I have full trust in the vaccine and encourage you all to receive it as I wait for my time in the line of priority"
Yes, yes he could've.
There are large parts of the population who say they won't get vaccinated because they're afraid that politicians exploit them as "guinea pigs" (especially the PoC community has a really bad history, e.g. Tuskegee syphilis study). Time to turn the usual situation around.
A decent government should fund vaccinations out of taxpayer money. It's simply way more cost-effective than having people around who want but can't afford vaccination and then society has to pay many orders of magnitude more for treating the illness...
My understanding is the training is capped at 80 hours/week, but they average that over the month. They definitely work more than that some weeks, but it's usually because it's not scheduled hours; it's shifts that run long because of emergencies or codes or whatever. When she was a resident and had to write patient notes, that definitely took extra hours each night after her shifts. It's brutal.
And for what it's worth, not all doctors make great money after training. In pediatrics, salaries are generally half what adult doctors make. For a lot of subspecialties, that's on par with average software engineering salaries, even after about 14 years of education and training.
I thought about a top level comment to link this, but we are naming and shaming programs that exploit residents during this time.
https://docs.google.com/spreadsheets/d/1ZgEKvTr1lvTLHsREeill...
What's stopping you?
Our institution built a new billion dollar hospital and did not include call rooms.
Great job.
A couple alternative links for reading:
When I load with content blockers disabled, Safari reports 17 (!) trackers prevented.
Only adding that summary because I assumed, given context of GP, that absence of call rooms would have been discussed. If you're like me, hope I've saved you the time.
I think it would have helped if you had described why you felt the link was worth sharing but I intend that feedback as gently as possible and not as a rebuke of any sort.
Our program director (in psychiatry) always pointed out that psychiatry had one of the highest rates of people going over the 80 hour-per-week limit in the health system. It was much more uncommon for us than for most of our colleagues, but we tended to report it on the rare occasion it happened. My surgical colleagues who went over the limit were asked to meet with their program director when it happened, and it was much easier just to "round down."
Glad I did a residency, but mine was relatively easy. I probably wouldn't have made it through some programs.
"According to an email sent by a chief resident to other residents, Stanford's leaders explained that an algorithm was used to assign its first allotment of the vaccine. The algorithm was said to have prioritized those health care workers at highest risk for COVID infections, along with factors like age and the location or unit where they work in the hospital. Residents apparently did not have an assigned location, and along with their typically young age, they were dropped low on the priority list."
The further, unforgivable sin is that the error was brought to their attention internally on Tuesday, the administrators didn't bother to fix it, and then when it became more widely known to the hospital staff Thursday afternoon they still did not act to fix it. And then Friday, suddenly they're in the news, they decide they're going to fix it?
Disclosure: Am a physician at Stanford.
It's not like Stanford didn't know a limited supply vaccine was coming several months ago, there was time to figure this stuff out.
And it would have been, if it had been a high enough priority.
Government workers are particularly vulnerable to this sort of failure mode: assuming everything is hard. This is not that hard. It's pretty easy. But big company workers are also usually susceptible to this. They don't get things done very fast so they're used to things taking a long time to do and assume that even trivialities must take a couple of days.
But that's why companies like Tesla and SpaceX succeed to these peoples' surprise. They look at these companies and say "They can't do that. This is hard. It takes maybe a half century of engineering."
Turns out lots of things, even if they're hard, can be done pretty fast. But not by those who've been damaged by their own slowness not being punished.
So we're to believe that a rogue computer code screwed up?.. i.e.: nobody in charge mess'ed up?
Why again do those with the largest paychecks, given the largest slack?
Its a weird system, I asked someone I know who is doing her residency how many times she's been COVID tested this year... just once. Apparently as long as you're not working on the COVID floor there isn't really a requirement at her hospital.
Every report I’ve seen from experiment subjects (who may have received the placebo) indicates that the second dose sucked for 2-3 days, and was much worse than the first.
If you give all your, for example, ICU staff the second dose at the same time, and then they can’t work for 2 days, how do you staff that?
Did you plan for this, or will it be a surprise that “The Algorithm” missed?
The key is that you need to stagger them now - maybe over a 10 day period to be safe (plan for 2 days off, so 20% out of action with 10 days, try to use “weekend” time for this)
But in the middle of a pandemic, even 20% is a lot of lost capacity
And then some people need to be in the day 8, 9 and 10 groups so won’t “get the vaccine first”
So it’s actually not quite as simple as just spreading them out over a few days and hope for the best.
I bet the "outcome" they're talking about is the _protest_, not the allocation.
Some people I know of who got it instead: Radiologists who primarily work from home. Administrative roles with relatively low (or no) patient interaction.
Not saying those people shouldn't get vaccinated -- just that if you were asked to come up with a priority list, it wouldn't be this.
Disclosure: am a physician at Stanford.
For what its worth, faculty across multiple departments (including those radiologists) stepped up and declined their scheduled vaccine appointments until residents get vaccinated first.
It's a big place made up of mostly smart (and good) people. A lot of outrage is at the administration's poor planning, failure to respond quickly the problem, and subsequent excuse-making.
I can't find direct links on mobile
I guarantee you that you'll see access for the wealthy much sooner than you'll see access for the folks at highest risk. I don't know how to help with this, but I'd much rather see farm workers, grocery store clerks, the homeless, bus drivers, etc. get access after we take care of the medical staff (who are exposed to patients) and the elderly (and others in extremely risky environments.)
Yet I'm sure that's not how this will go.
How long before testing and vaccination becomes an employment perk at a FAANG company?
Inequality in the US is getting more and more extreme. This shit will not be tolerated much longer, riots will get worse, people will get angrier.
Under a socialist system, resources are allocated to those with the most political power.
Under an anarchist system, resources are allocated to those with the most firepower.
The vaccine is being distributed under a socialist system. Nobody should be surprised at the results.
Capitalism doesn't help in the case that the article is discussing, either. Older doctors tend to be paid more than younger doctors, and are also at higher risk of COVID-19 without taking into account how many COVID-19 patients they see. If we just charged a high price for the COVID-19 vaccine, the older doctors would easily be able to afford it and get vaccinated and the residents wouldn't be able to afford it -- exactly what's being complained about in the article. You don't get paid for each COVID patient you treat (caring for dying pandemic victims is not a high-margin business), so using ability to pay as the means of distributing the vaccine doesn't maximize its value to society. The problem that the designers of the distribution model faced was balancing risk from old age with risk from exposure to patients with COVID-19. They balanced it wrong, and over-weigthed old age and under-weighted daily exposure to people sick with COVID-19.
It's not about capitalism, socialism, or anarchy. It's about getting back to normal as quickly as possible.
But then you go on to explain how it would be effective:
> Think about people like me: I could easily afford to pay whatever it would cost to be vaccinated. But, it's largely pointless: I work at home and don't go outside except to get some groceries now and again. If I did get COVID-19, I'm at a very low risk of dying. I'm probably not going to spread it to anyone either; I live alone and would just stay in bed for the two weeks I was contagious.
I.e. you wouldn't find it worthwhile to pay whatever it may cost for an early dose (the LA Times reports people offering up to $25,000). Distribution by willingness to pay does sort by those who are able to pay and finding it worth the cost.
Under a socialist system, those with the most political power have the most resources and the most firepower.
Under an anarchist system, those with the most firepower have the most resources and the most political power.
(And it’s not because they’re paying more)
We can argue about whether or not Stanford is “socialist”, but I don’t feel he’s wrong that Stanford allocated vaccines according to political power. He also doesn’t judge any system. He’s just giving an observation
Powerful politicians are already getting vaccinated.
It's a virtual certainty that those offering $25,000 for a dose are going to find someone in the distribution system providing it and pocketing the money.
https://www.latimes.com/california/story/2020-12-18/wealthy-...
So yeah, socialism :-)
Oh, wait, no more need for false PCR tests, we have vaccines now. Forget testing, they’re wrong anyway, everyone get in line for a dose of nothing.
The good news appearing in media recently are the high percentage of people all over the world against being vaccinated, it means the human reason algorithm is still top notch.
Yeah, I think management learnt something /s.
We're always going to keep getting more of this behavior unless and until we start holding management fully accountable for this type of insider dealing. And accountable means being immediately fired, no golden handcuffs, and a full clawback of any issued bonuses and stock options.
Of course, that requires that strikes be legal. In the USA, that is frequently not the case.
Another factor that should be accounted for, but I've not seen in any official policy, is the people who've contracted the virus and recovered. While there is research showing that the human immune response to SARS-COV-2 may persist for years[0], even if that's not true we do know that it lasts for 6+ months as shown by the small amount of confirmed reinfections[1]. So put those people at the end of the line.
We really should write down the objective and then work out the way to achieve that objective. If the objective is to preserve the maximum amount of life years, then the criteria should heavily bias toward people older 55+ and people with comorbidities. Being a front line health care worker is very honorable and should carry a lot of respect, but it may not be the truly relevant criteria for maximizing the preservation of life.
0. https://bgr.com/2020/11/18/coronavirus-immunity-years-antibo... 1. https://www.forbes.com/sites/joshuacohen/2020/11/16/though-r...
Which sucks, but is 100% the most efficient way to prevent deaths. Giving the vaccines to older workers first is correct.
Your opinion is poorly thought out.
Most states seem to be deciding to prioritize healthcare workers (especially those taking care of covid patients) either entirely or among other groups. So the way you do that is delivering to hospitals makes sense. CA presumably allocated a certain amount to Stanford Medicine, as a large hospital system. I don't know how CA decided what hospitals to give to in what amounts, but it certainly wouldn't shock me if political influence were part of it, why wouldn't it be, what sort of guidelines or transparency is there to stop it? There is not really any written procedure for how to decide which hospitals to get how much in most states, it's just... happening.
Then Stanford was clearly left entirely on their own to decide how to allocate internally.
This is far too important and too politically fraught (political in terms of POWER, in terms of distributing a resource for which demand far exceeds supply for something that can literally be life-and-death) -- to have left it up for everyone to just make up their own prioritization rules and medical ethics determinations on the fly, in a hetereogenous way. It means people will make terrible mistakes, and the powerful will take advantage to have their needs prioritized.
It is ridiculous there aren't much more clear/specific guidelines, and in some cases enforceable policies/regulations, from the federal government. It's like nobody's driving the bus here
https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-...
Why is this not California's fault? States were each permitted to establish their own procedures, which somewhat makes sense given the challenging distribution requirements of the Pfizer vaccine. Montana has significantly different challenges than Rhode Island in that sense. Most states that I know of have established clear guidelines saying who gets it and when - I assume California is the same.
Seems like California is the governmental entity that failed to exercise proper oversight and/or requirements specification here.
Most states you know have established clear guidelines sayign who gets it and when? Including specifics on what medical staff within a hospital system would get it? Like not just "health care staff" or "first responders" (everyone in the Stanford Medicine system is that already right, this is about who within that group gets it).
Please back that up by showing me such clear guidelines from a few states. It should be easy to find this, if indeed most states have done this, presumably in a very transparent way for something so important and contentious, right?
I don't believe most states have.
(It doesn't make it easier that the federal government told states how much they'd get them REDUCED it, and in general is only committing to telling states how much they'll get a week in advance).
Phase 1 says "Healthcare workers in patient care settings"
Tennessee's: https://www.tn.gov/content/dam/tn/health/documents/cedep/nov...
Phase 1 says "hospital/free-standing emergency department staff with direct patient exposure and/or exposure to potentially infectious materials"
Other states have similar wording. You'd really have to twist yourself into a knot to convince yourself that a work-from-home administrator falls into the categories specified above. Shame on any state who didn't include wording like that - there was nothing stopping them from putting some common-sense wording in their plans. Beyond the written rules, you'd also have to be a selfish idiot to think that just because you're related to a healthcare company that you should get it this week if you're working from home. If I were in that kind of role, shame would be enough to stop me but as we've seen the elite often have no shame.
(edit) California's own plan [0] says Phase 1-A includes "paid and unpaid persons serving in healthcare settings who have the potential for direct or indirect exposure to patients and infectious materials and are unable to work from home". So if Stanford was really vaccinating admins who are working from home, then it seems like they violated state guidelines and should be punished appropriately.
[0] https://www.cdph.ca.gov/Programs/CID/DCDC/CDPH%20Document%20...
my wife works for the hospital but has been working from home most of the time since covid started. she's low on the priority list because of this, and rightfully so. her co-workers that are treating the (sometimes infected) patients should get access first, if they want it.
While age does matter, ability to control exposure and risk of spreading the disease within the hospital should be at least as important, in my opinion.
Medical residents:
1) are at high risk, overworked, stressed out, and underpaid;
2) are regularly being called "heroes" in slick posters and PR campaigns; and
3) have just been left out of the initial vaccine allocation.
The combination of 1), 2), and 3) seems almost "engineered" to induce psychological and emotional breakdown in the very people who least deserve it. Horrible.
From the article,
> While leadership is pointing to an error in an algorithm meant to ensure equity and justice, our understanding is this error was identified on Tuesday and a decision was made not to revise the vaccine allocation scheme before its release today," they wrote.
The residents' point here is that it ceases to be an innocent mistake when it is pointed out to you, and you deliberately decide to do nothing about it.
I doubt it. Per Hanlon's razor, we should never attribute to malice that which is adequately explained by (bureaucratic) stupidity.[a]
So, basically, whoever coded up the algorithm for personal COVID-risk forgot to take care of null-states for the location input.
So much for all the outrage over selfish intent.
Sure, it's possible that the admins purposefully created this glitch so as to have a reasonable fallback story in the case they got caught, but per Occam's Razor, I think it's much more likely it was a dumb, honest mistake.
This might be the reason you're outraged, but most outraged people here don't even know the administration doubled down on their position. They're outraged due to assumed selfish intent. I think this assumption is wrong.
So, I'm not sure that it makes a difference if some people—hearing about their screw-up—are angry for the wrong reason.
To be clear, I'm with you that this probably wasn't a mustache-twirling attempt to divert vaccinations from the frontline staff. But I don't think that it matters much, given the medical expertise we would expect from an organization like this.
I think the real reason this story is getting attention is because people are assuming selfish intent. I think this assumption is probably wrong.
If the vaccines have a delayed negative effect you cripple health care.
Vaccines are great once they've had years of testing, but risk management would tell you to play it safe with critical staff. If the vaccine disabled 20% of medical professionals in January how many people would die as a consequence?
Is this possible that a yet completely unknown sideeffect that hasn't shown up until now will suddenly appear? Yes. Is it likely? No, absolutely not.
Is it possible that a mutated strain of Covid-19 will disable 20% of medical personel? Yes. Is it likely? No. But still: It's much more likely than a vaccine that has not shown any serious adverse events until now doing this.
To see such a highly regarded institution fail this very simple test is another example of how the human creature needs more time to evolve. It's not just a mistake, this is a pattern playing out all over. Anyone who spends time in these environs would not be much surprised, but when the best hospitals still can't take care of their most vulnerable staff because of pernicious political maneuvering, the contrast between perception and reality is all the more stark.