Adonis Diaries

Archive for October 27th, 2016

Cuba: best location to train doctors?

Big problems need big solutions, sparked by big ideas, imagination and audacity.

journalist Gail Reed profiles one big solution worth noting: Havana’s Latin American Medical School, which trains global physicians to serve the local communities that need them most.

Gail Reed. Cuban health care expert. Full bio

Filmed Sept. 2014

I want to tell you how 20,000 remarkable young people from over 100 countries ended up in Cuba and are transforming health in their communities. 90% of them would never have left home at all if it weren’t for a scholarship to study medicine in Cuba and a commitment to go back to places like the ones they’d come from — remote farmlands, mountains, ghettos — to become doctors for people like themselves, to walk the walk.

0:46 Havana’s Latin American Medical School: It’s the largest medical school in the world, graduating 23,000 young doctors since its first class of 2005, with nearly 10,000 more in the pipeline.

Its mission, to train physicians for the people who need them the most: the over one billion who have never seen a doctor, the people who live and die under every poverty line ever invented.

Its students defy all norms. They’re the school’s biggest risk and also its best bet. They’re recruited from the poorest, most broken places on our planet by a school that believes they can become not just the good but the excellent physicians their communities desperately need, that they will practice where most doctors don’t, in places not only poor but oftentimes dangerous, carrying venom antidotes in their backpacks or navigating neighborhoods riddled by drugs, gangs and bullets, their home ground.

The hope is that they will help transform access to care, the health picture in impoverished areas, and even the way medicine itself is learned and practiced, and that they will become pioneers in our global reach for universal health coverage, surely a tall order.

Two big storms and this notion of “walk the walk prompted creation of ELAM back in 1998. The Hurricanes Georges and Mitch had ripped through the Caribbean and Central America, leaving 30,000 dead and two and a half million homeless.

Hundreds of Cuban doctors volunteered for disaster response, but when they got there, they found a bigger disaster: whole communities with no healthcare, doors bolted shut on rural hospitals for lack of staff, and just too many babies dying before their first birthday.

What would happen when these Cuban doctors left? New doctors were needed to make care sustainable, but where would they come from? Where would they train?

How Cuba is training the kind of doctors who local patients need:

ted.com|By Gail Reed

In Havana, the campus of a former naval academy was turned over to the Cuban Health Ministry to become the Latin American Medical School, ELAM. Tuition, room and board, and a small stipend were offered to hundreds of students from the countries hardest hit by the storms.

As a journalist in Havana, I watched the first 97 Nicaraguans arrive in March 1999, settling into dorms barely refurbished and helping their professors not only sweep out the classrooms but move in the desks and the chairs and the microscopes.

Over the next few years, governments throughout the Americas requested scholarships for their own students, and the Congressional Black Caucus asked for and received hundreds of scholarships for young people from the USA.

Today, among the 23,000 are graduates from 83 countries in the Americas, Africa and Asia, and enrollment has grown to 123 nations. More than half the students are young women.

They come from 100 ethnic groups, speak 50 different languages. WHO Director Margaret Chan said, “For once, if you are poor, female, or from an indigenous population, you have a distinct advantage, an ethic that makes this medical school unique.”

Luther Castillo comes from San Pedro de Tocamacho on the Atlantic coast of Honduras. There’s no running water, no electricity there, and to reach the village, you have to walk for hours or take your chances in a pickup truck like I did skirting the waves of the Atlantic. Luther was one of 40 Tocamacho children who started grammar school, the sons and daughters of a black indigenous people known as the Garífuna, 20 percent of the Honduran population.

The nearest healthcare was fatal miles away. Luther had to walk three hours every day to middle school. Only 17 made that trip. Only five went on to high school, and only one to university: Luther, to ELAM, among the first crop of Garífuna graduates. Just two Garífuna doctors had preceded them in all of Honduran history. Now there are 69, thanks to ELAM.

Big problems need big solutions, sparked by big ideas, imagination and audacity, but also solutions that work. ELAM’s faculty had no handy evidence base to guide them, so they learned the hard way, by doing and correcting course as they went.

Even the brightest students from these poor communities weren’t academically prepared for six years of medical training, so a bridging course was set up in sciences.

Then came language: these were Mapuche, Quechuas, Guaraní, Garífuna, indigenous peoples who learned Spanish as a second language, or Haitians who spoke Creole.

So Spanish became part of the pre-med curriculum. Even so, in Cuba, the music, the food, the smells, just about everything was different, so faculty became family, ELAM home. Religions ranged from indigenous beliefs to Yoruba, Muslim and Christian evangelical. Embracing diversity became a way of life.

Why have so many countries asked for these scholarships?

First, they just don’t have enough doctors, and where they do, their distribution is skewed against the poor, because our global health crisis is fed by a crisis in human resources.

We are short four to seven million health workers just to meet basic needs, and the problem is everywhere. Doctors are concentrated in the cities, where only half the world’s people live, and within cities, not in the shantytowns or South L.A.

Here in the United States, where we have healthcare reform, we don’t have the professionals we need. By 2020, we will be short 45,000 primary care physicians. And we’re also part of the problem. The United States is the number one importer of doctors from developing countries.

The second reasons students flock to Cuba is the island’s own health report card, relying on strong primary care. A commission from The Lancet rates Cuba among the best performing middle-income countries in health.

Save the Children ranks Cuba the best country in Latin America to become a mother. Cuba has similar life expectancy and lower infant mortality than the United States, with fewer disparities, while spending per person one 20th of what we do on health here in the USA.

Academically, ELAM is tough, but 80 percent of its students graduate. The subjects are familiar — basic and clinical sciences — but there are major differences.

First, training has moved out of the ivory tower and into clinic classrooms and neighborhoods, the kinds of places most of these grads will practice. Sure, they have lectures and hospital rotations too, but community-based learning starts on day one.

Second, students treat the whole patient, mind and body, in the context of their families, their communities and their culture.

Third, they learn public health: to assess their patients’ drinking water, housing, social and economic conditions. Fourth, they are taught that a good patient interview and a thorough clinical exam provide most of the clues for diagnosis, saving costly technology for confirmation.

And fifth, they’re taught over and over again the importance of prevention, especially as chronic diseases cripple health systems worldwide.

Such an in-service learning also comes with a team approach, as much how to work in teams as how to lead them, with a dose of humility. Upon graduation, these doctors share their knowledge with nurse’s aids, midwives, community health workers, to help them become better at what they do, not to replace them, to work with shamans and traditional healers.

ELAM’s graduates: Are they proving this audacious experiment right? Dozens of projects give us an inkling of what they’re capable of doing.

Take the Garífuna grads. They not only went to work back home, but they organized their communities to build Honduras’ first indigenous hospital. With an architect’s help, residents literally raised it from the ground up.

The first patients walked through the doors in December 2007, and since then, the hospital has received nearly one million patient visits. And government is paying attention, upholding the hospital as a model of rural public health for Honduras.

 ELAM’s graduates are smart, strong and also dedicated. Haiti, January 2010. The pain. People buried under 30 million tons of rubble. Overwhelming. 340  Cuban doctors were already on the ground long term. More were on their way. Many more were needed.

At ELAM, students worked round the clock to contact 2,000 graduates. As a result, hundreds arrived in Haiti, 27 countries’ worth, from Mali in the Sahara to St. Lucia, Bolivia, Chile and the USA. They spoke easily to each other in Spanish and listened to their patients in Creole thanks to Haitian medical students flown in from ELAM in Cuba. Many stayed for months, even through the cholera epidemic.

Hundreds of Haitian graduates had to pick up the pieces, overcome their own heartbreak, and then pick up the burden of building a new public health system for Haiti. Today, with aid of organizations and governments from Norway to Cuba to Brazil, dozens of new health centers have been built, staffed, and in 35 cases, headed by ELAM graduates.

Yet the Haitian story also illustrates some of the bigger problems faced in many countries. Take a look: 748 Haitian graduates by 2012, when cholera struck, nearly half working in the public health sector but one quarter unemployed, and 110 had left Haiti altogether.

So in the best case scenarios, these graduates are staffing and thus strengthening public health systems, where often they’re the only doctors around. In the worst cases, there are simply not enough jobs in the public health sector, where most poor people are treated, not enough political will, not enough resources, not enough anything — just too many patients with no care.

The grads face pressure from their families too, desperate to make ends meet, so when there are no public sector jobs, these new MDs decamp into private practice, or go abroad to send money home.

Worst of all, in some countries, medical societies influence accreditation bodies not to honor the ELAM degree, fearful these grads will take their jobs or reduce their patient loads and income.

It’s not a question of competencies. Here in the USA, the California Medical Board accredited the school after rigorous inspection, and the new physicians are making good on Cuba’s big bet, passing their boards and accepted into highly respected residencies from New York to Chicago to New Mexico.

Two hundred strong, they’re coming back to the United States energized, and also dissatisfied. As one grad put it, in Cuba, “We are trained to provide quality care with minimal resources, so when I see all the resources we have here, and you tell me that’s not possible, I know it’s not true. Not only have I seen it work, I’ve done the work.”

14:57 ELAM’s graduates, some from right here in D.C. and Baltimore, have come from the poorest of the poor to offer health, education and a voice to their communities. They’ve done the heavy lifting.

Now we need to do our part to support the 23,000 and counting, All of us — foundations, residency directors, press, entrepreneurs, policymakers, people — need to step up. We need to do much more globally to give these new doctors the opportunity to prove their mettle.

They need to be able to take their countries’ licensing exams. They need jobs in the public health sector or in nonprofit health centers to put their training and commitment to work. They need the chance to be the doctors their patients need.

15:57 To move forward, we may have to find our way back to that pediatrician who would knock on my family’s door on the South Side of Chicago when I was a kid, who made house calls, who was a public servant.

These aren’t such new ideas of what medicine should be. What’s new is the scaling up and the faces of the doctors themselves: an ELAM graduate is more likely to be a she than a he; In the Amazon, Peru or Guatemala, an indigenous doctor; in the USA, a doctor of color who speaks fluent Spanish. She is well trained, can be counted on, and shares the face and culture of her patients, and she deserves our support surely, because whether by subway, mule, or canoe, she is teaching us to walk the walk.

Can’t control what our intelligent machines are learning: Once they learned?

We’re asking questions to computation that have no single right answers, that are subjective and open-ended and value-laden

Machines that could just be reflecting our biases, and these systems could be picking up on our biases and amplifying them and showing them back to us, while we’re telling ourselves, “We’re just doing objective, neutral computation.”

Machine intelligence is here, and we’re already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control.

Zeynep Tufekci explains how intelligent machines can fail in ways that don’t fit human error patterns — and in ways we won’t expect or be prepared for. “We cannot outsource our responsibilities to machines,” she says. “We must hold on ever tighter to human values and human ethics.”

Zeynep Tufekci. Techno-sociologist. She asks big questions about our societies and our lives, as both algorithms and digital connectivity spread. Full bio
Filmed June 2016

I started my first job as a computer programmer in my very first year of college — basically, as a teenager.

0:19 Soon after I started working, writing software in a company, a manager who worked at the company came down to where I was, and he whispered to me, “Can he tell if I’m lying?” There was nobody else in the room.

“Can who tell if you’re lying? And why are we whispering?”

The manager pointed at the computer in the room. “Can he tell if I’m lying?” Well, that manager was having an affair with the receptionist.

And I was still a teenager. So I whisper-shouted back to him, Yes, the computer can tell if you’re lying.”

I laughed, but actually, the laugh’s on me. Nowadays, there are computational systems that can suss out emotional states and even lying from processing human faces. Advertisers and even governments are very interested.

I had become a computer programmer because I was one of those kids crazy about math and science.

But somewhere along the line I’d learned about nuclear weapons, and I’d gotten really concerned with the ethics of science. I was troubled.

Because of family circumstances, I also needed to start working as soon as possible. So I thought to myself, hey, let me pick a technical field where I can get a job easily and where I don’t have to deal with any troublesome questions of ethics. So I picked computers.

Patsy Z shared this link. TED. October 19 at 8:25pm ·

Using a computer program to decide which applicant to hire is a pretty terrible idea. Here’s why:

ted.com|By Zeynep Tufekci

Well, ha, ha, ha! All the laughs are on me. Nowadays, computer scientists are building platforms that control what a billion people see every day. They’re developing cars that could decide who to run over. They’re even building machines, weapons, Drones that might kill human beings in war. It’s ethics all the way down.

2:18 Machine intelligence is here. We’re now using computation to make all sort of decisions, but also new kinds of decisions. We’re asking questions to computation that have no single right answers, that are subjective and open-ended and value-laden.

We’re asking questions like, “Who should the company hire?” “Which update from which friend should you be shown?” “Which convict is more likely to reoffend?” “Which news item or movie should be recommended to people?”

we’ve been using computers for a while, but this is different. This is a historical twist, because we cannot anchor computation for such subjective decisions the way we can anchor computation for flying airplanes, building bridges, going to the moon.

Are airplanes safer? Did the bridge sway and fall? There, we have agreed-upon, fairly clear benchmarks, and we have laws of nature to guide us. We have no such anchors and benchmarks for decisions in messy human affairs.

To make things more complicated, our software is getting more powerful, but it’s also getting less transparent and more complex.

Recently, in the past decade, complex algorithms have made great strides. They can recognize human faces. They can decipher handwriting. They can detect credit card fraud and block spam and they can translate between languages. They can detect tumors in medical imaging. They can beat humans in chess and Go.

Much of this progress comes from a method called “machine learning.” Machine learning is different than traditional programming, where you give the computer detailed, exact, painstaking instructions. It’s more like you take the system and you feed it lots of data, including unstructured data, like the kind we generate in our digital lives.

And the system learns by churning through this data. (Machine learning to apply specific statistical packages?) And also, crucially, these systems don’t operate under a single-answer logic. They don’t produce a simple answer; it’s more probabilistic: “This one is probably more like what you’re looking for.”

the upside is: this method is really powerful. The head of Google’s AI systems called it, the unreasonable effectiveness of data.” (Unreasonable if data are arranged or fed the wrong ways, and into statistical models that barely match human behavior)

The downside is, we don’t really understand what the system learned. In fact, that’s its power. This is less like giving instructions to a computer; it’s more like training a puppy-machine-creature we don’t really understand or control. So this is our problem.

It’s a problem when this artificial intelligence system gets things wrong. It’s also a problem when it gets things right, because we don’t even know which is which when it’s a subjective problem. We don’t know what this thing is thinking.

consider a hiring algorithm — a system used to hire people, using machine-learning systems. Such a system would have been trained on previous employees’ data and instructed to find and hire people like the existing high performers in the company. (And the idiosyncrasies among cultures?)

Sounds good. I once attended a conference that brought together human resources managers and executives, high-level people, using such systems in hiring. They were super excited. They thought that this would make hiring more objective, less biased, and give women and minorities a better shot against biased human managers.

And look — human hiring is biased. I know. I mean, in one of my early jobs as a programmer, my immediate manager would sometimes come down to where I was really early in the morning or really late in the afternoon, and she’d say, “Zeynep, let’s go to lunch!” I’d be puzzled by the weird timing. It’s 4pm. Lunch?

I was broke, so free lunch. I always went. I later realized what was happening. My immediate managers had not confessed to their higher-ups that the programmer they hired for a serious job was a teen girl who wore jeans and sneakers to work. I was doing a good job, I just looked wrong and was the wrong age and gender.

So hiring in a gender- and race-blind way certainly sounds good to me. But with these systems, it is more complicated, and here’s why: Currently, computational systems can infer all sorts of things about you from your digital crumbs, even if you have not disclosed those things.

They can infer your sexual orientation, your personality traits, your political leanings. They have predictive power with high levels of accuracy. Remember — for things you haven’t even disclosed. This is inference. (or interference in personal rights)

I have a friend who developed such computational systems to predict the likelihood of clinical or postpartum depression from social media data. The results are impressive. Her system can predict the likelihood of depression months before the onset of any symptoms — months before.

No symptoms, there’s prediction. She hopes it will be used for early intervention. Great! But now put this in the context of hiring.

at this human resources managers conference, I approached a high-level manager in a very large company, and I said to her, “Look, what if, unbeknownst to you, your system is weeding out people with high future likelihood of depression? They’re not depressed now, just maybe in the future, more likely. What if it’s weeding out women more likely to be pregnant in the next year or two but aren’t pregnant now? What if it’s hiring aggressive people because that’s your workplace culture?” You can’t tell this by looking at gender breakdowns.

Those may be balanced. And since this is machine learning, not traditional coding, there is no variable there labeled “higher risk of depression,” “higher risk of pregnancy,” “aggressive guy scale.” Not only do you not know what your system is selecting on, you don’t even know where to begin to look. It’s a black box. It has predictive power, but you don’t understand it.

“What safeguards,” I asked, “do you have to make sure that your black box isn’t doing something shady?” She looked at me as if I had just stepped on 10 puppy tails.

She stared at me and she said, “I don’t want to hear another word about this.” And she turned around and walked away. Mind you — she wasn’t rude. It was clear: what I don’t know isn’t my problem, go away, death stare.

such a system may even be less biased than human managers in some ways. And it could make monetary sense. But it could also lead to a steady but stealthy shutting out of the job market of people with higher risk of depression. Is this the kind of society we want to build, without even knowing we’ve done this, because we turned decision-making to machines we don’t totally understand?

Another problem is this: these systems are often trained on data generated by our actions, human imprints. Well, they could just be reflecting our biases, and these systems could be picking up on our biases and amplifying them and showing them back to us, while we’re telling ourselves, “We’re just doing objective, neutral computation.”

Researchers found that on Google, women are less likely than men to be shown job ads for high-paying jobs. And searching for African-American names is more likely to bring up ads suggesting criminal history, even when there is none. Such hidden biases and black-box algorithms that researchers uncover sometimes but sometimes we don’t know, can have life-altering consequences.

In Wisconsin, a defendant was sentenced to six years in prison for evading the police. You may not know this, but algorithms are increasingly used in parole and sentencing decisions. He wanted to know: How is this score calculated? It’s a commercial black box.

The company refused to have its algorithm be challenged in open court. But ProPublica, an investigative nonprofit, audited that very algorithm with what public data they could find, and found that its outcomes were biased and its predictive power was dismal, barely better than chance, and it was wrongly labeling black defendants as future criminals at twice the rate of white defendants.

consider this case: This woman was late picking up her godsister from a school in Broward County, Florida, running down the street with a friend of hers. They spotted an unlocked kid’s bike and a scooter on a porch and foolishly jumped on it. As they were speeding off, a woman came out and said, “Hey! That’s my kid’s bike!” They dropped it, they walked away, but they were arrested.

She was wrong, she was foolish, but she was also just 18. She had a couple of juvenile misdemeanours. Meanwhile, that man had been arrested for shoplifting in Home Depot — 85 dollars’ worth of stuff, a similar petty crime. But he had two prior armed robbery convictions.

But the algorithm scored her as high risk, and not him. Two years later, ProPublica found that she had not reoffended. It was just hard to get a job for her with her record. He, on the other hand, did reoffend and is now serving an eight-year prison term for a later crime. Clearly, we need to audit our black boxes and not have them have this kind of unchecked power.

Audits are great and important, but they don’t solve all our problems. Take Facebook’s powerful news feed algorithm — you know, the one that ranks everything and decides what to show you from all the friends and pages you follow. Should you be shown another baby picture?

A sullen note from an acquaintance? An important but difficult news item? There’s no right answer. Facebook optimizes for engagement on the site: likes, shares, comments.

 In August of 2014, protests broke out in Ferguson, Missouri, after the killing of an African-American teenager by a white police officer, under murky circumstances. The news of the protests was all over my algorithmically unfiltered Twitter feed, but nowhere on my Facebook. Was it my Facebook friends?

I disabled Facebook’s algorithm, which is hard because Facebook keeps wanting to make you come under the algorithm’s control, and saw that my friends were talking about it. It’s just that the algorithm wasn’t showing it to me. I researched this and found this was a widespread problem.

The story of Ferguson wasn’t algorithm-friendly. It’s not “likable.” Who’s going to click on “like?” It’s not even easy to comment on. Without likes and comments, the algorithm was likely showing it to even fewer people, so we didn’t get to see this.

Instead, that week, Facebook’s algorithm highlighted this, which is the ALS Ice Bucket Challenge. Worthy cause; dump ice water, donate to charity, fine. But it was super algorithm-friendly. The machine made this decision for us. A very important but difficult conversation might have been smothered, had Facebook been the only channel.

finally, these systems can also be wrong in ways that don’t resemble human systems. Do you guys remember Watson, IBM’s machine-intelligence system that wiped the floor with human contestants on Jeopardy? It was a great player.

But then, for Final Jeopardy, Watson was asked this question: “Its largest airport is named for a World War II hero, its second-largest for a World War II battle.”

14:58 (Hums Final Jeopardy music)

Chicago. The two humans got it right. Watson, on the other hand, answered “Toronto” — for a US city category! The impressive system also made an error that a human would never make, a second-grader wouldn’t make.

Our machine intelligence can fail in ways that don’t fit error patterns of humans, in ways we won’t expect and be prepared for. It’d be lousy not to get a job one is qualified for, but it would triple suck if it was because of stack overflow in some subroutine.

In May of 2010, a flash crash on Wall Street fueled by a feedback loop in Wall Street’s “sell” algorithm wiped a trillion dollars of value in 36 minutes. I don’t even want to think what “error” means in the context of lethal autonomous weapons.

yes, humans have always made biases. Decision makers and gatekeepers, in courts, in news, in war … they make mistakes; but that’s exactly my point. We cannot escape these difficult questions. We cannot outsource our responsibilities to machines.

Artificial intelligence does not give us a “Get out of ethics free” card.

 Data scientist Fred Benenson calls this math-washing. We need the opposite.

We need to cultivate algorithm suspicion, scrutiny and investigation.

We need to make sure we have algorithmic accountability, auditing and meaningful transparency.

We need to accept that bringing math and computation to messy, value-laden human affairs does not bring objectivity; rather, the complexity of human affairs invades the algorithms.

Yes, we can and we should use computation to help us make better decisions. But we have to own up to our moral responsibility to judgment, and use algorithms within that framework, not as a means to abdicate and outsource our responsibilities to one another as human to human.

17:24 Machine intelligence is here. That means we must hold on ever tighter to human values and human ethics.


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