Connect with us

Tech

This is the Stanford vaccine algorithm that left out frontline doctors

Published

on

This is the Stanford vaccine algorithm that left out frontline doctors


What these factors do not take into account is exposure to patients with covid-19, say residents. That means the algorithm did not distinguish between those who had caught covid from patients and those who got it from community spread—including employees working remotely. And, as first reported by ProPublica, residents were told that because they rotate between departments rather than maintain a single assignment, they lost out on points associated with the departments where they worked. 

The algorithm’s third category refers to the California Department of Public Health’s vaccine allocation guidelines. These focus on exposure risk as the single highest factor for vaccine prioritization. The guidelines are intended primarily for county and local governments to decide how to prioritize the vaccine, rather than how to prioritize between a hospital’s departments. But they do specifically include residents, along with the departments where they work, in the highest-priority tier. 

It may be that the “CDPH range” factor gives residents a higher score, but still not high enough to counteract the other criteria.

“Why did they do it that way?” 

Stanford tried to factor in a lot more variables than other medical facilities, but Jeffrey Kahn, the director of the Johns Hopkins Berkman Institute of Bioethics, says the approach was overcomplicated. “The more there are different weights for different things, it then becomes harder to understand—‘Why did they do it that way?’” he says.

Kahn, who sat on Johns Hopkins’ 20-member committee on vaccine allocation, says his university allocated vaccines based simply on job and risk of exposure to covid-19.

He says that decision was based on discussions that purposefully included different perspectives—including those of residents—and in coordination with other hospitals in Maryland. Elsewhere, the University of California San Francisco’s plan is based on a similar assessment of risk of exposure to the virus. Mass General Brigham in Boston categorizes employees into four groups based on department and job location, according to an internal email reviewed by MIT Technology Review.

“There’s so little trust around so much related to the pandemic, we cannot squander it.”

“It’s really important [for] any approach like this to be transparent and public …and not something really hard to figure out,” Kahn says. “There’s so little trust around so much related to the pandemic, we cannot squander it.” 

Algorithms are commonly used in health care to rank patients by risk level in an effort to distribute care and resources more equitably. But the more variables used, the harder it is to assess whether the calculations might be flawed.

For example, in 2019, a study published in Science showed that 10 widely used algorithms for distributing care in the US ended up favoring white patients over Black ones. The problem, it turned out, was that the algorithms’ designers assumed that patients who spent more on health care were more sickly and needed more help. In reality, higher spenders are also richer, and more likely to be white. As a result, the algorithm allocated less care to Black patients with the same medical conditions as white ones.

Irene Chen, an MIT doctoral candidate who studies the use of fair algorithms in health care, suspects this is what happened at Stanford: the formula’s designers chose variables that they believed would serve as good proxies for a given staffer’s level of covid risk. But they didn’t verify that these proxies led to sensible outcomes, or respond in a meaningful way to the community’s input when the vaccine plan came to light on Tuesday last week. “It’s not a bad thing that people had thoughts about it afterward,” says Chen. “It’s that there wasn’t a mechanism to fix it.”

A canary in the coal mine?

After the protests, Stanford issued a formal apology, saying it would revise its distribution plan. 

Hospital representatives did not respond to questions about who they would include in new planning processes, or whether the algorithm would continue to be used. An internal email summarizing the medical school’s response, shared with MIT Technology Review, states that neither program heads, department chairs, attending physicians, nor nursing staff were involved in the original algorithm design. Now, however, some faculty are pushing to have a bigger role, eliminating the algorithms’ results completely and instead giving division chiefs and chairs the authority to make decisions for their own teams. 



Tech

The Download: Introducing our TR35 list, and the death of the smart city

Published

on

JA22 cover


Spoiler alert: our annual Innovators Under 35 list isn’t actually about what a small group of smart young people have been up to (although that’s certainly part of it.) It’s really about where the world of technology is headed next.

As you read about the problems this year’s winners have set out to solve, you’ll also glimpse the near future of AI, biotech, materials, computing, and the fight against climate change.

To connect the dots, we asked five experts—all judges or former winners—to write short essays about where they see the most promise, and the biggest potential roadblocks, in their respective fields. We hope the list inspires you and gives you a sense of what to expect in the years ahead.

Read the full list here.

The Urbanism issue

The modern city is a surveillance device. It can track your movements via your license plate, your cell phone, and your face. But go to any city or suburb in the United States and there’s a different type of monitoring happening, one powered by networks of privately owned doorbell cameras, wildlife cameras, and even garden-variety security cameras. 

The latest print issue of MIT Technology Review examines why, independently of local governments, we have built our neighborhoods into panopticons: everyone watching everything, all the time. Here is a selection of some of the new stories in the edition, guaranteed to make you wonder whether smart cities really are so smart after all:

– How groups of online neighborhood watchmen are taking the law into their own hands.

– Why Toronto wants you to forget everything you know about smart cities.

– Bike theft is a huge problem. Specialized parking pods could be the answer.

– Public transport wants to kill off cash—but it won’t be as disruptive as you think.

Continue Reading

Tech

Toronto wants to kill the smart city forever

Published

on

Toronto wants to kill the smart city forever


Most Quayside watchers have a hard time believing that covid was the real reason for ending the project. Sidewalk Labs never really painted a compelling picture of the place it hoped to build. 

Quayside 2.0

The new Waterfront Toronto project has clearly learned from the past. Renderings of the new plans for Quayside—call it Quayside 2.0—released earlier this year show trees and greenery sprouting from every possible balcony and outcropping, with nary an autonomous vehicle or drone in site. The project’s highly accomplished design team—led by Alison Brooks, a Canadian architect based in London; the renowned Ghanaian-British architect David Adjaye; Matthew Hickey, a Mohawk architect from the Six Nations First Nation; and the Danish firm Henning Larsen—all speak of this new corner of Canada’s largest city not as a techno-utopia but as a bucolic retreat. 

In every way, Quayside 2.0 promotes the notion that an urban neighborhood can be a hybrid of the natural and the manmade. The project boldly suggests that we now want our cities to be green, both metaphorically and literally—the renderings are so loaded with trees that they suggest foliage is a new form of architectural ornament. In the promotional video for the project, Adjaye, known for his design of the Smithsonian Museum of African American History, cites the “importance of human life, plant life, and the natural world.” The pendulum has swung back toward Howard’s garden city: Quayside 2022 is a conspicuous disavowal not only of the 2017 proposal but of the smart city concept itself.

To some extent, this retreat to nature reflects the changing times, as society has gone from a place of techno-optimism (think: Steve Jobs introducing the iPhone) to a place of skepticism, scarred by data collection scandals, misinformation, online harassment, and outright techno-fraud. Sure, the tech industry has made life more productive over the past two decades, but has it made it better? Sidewalk never had an answer to this. 

 “To me it’s a wonderful ending because we didn’t end up with a big mistake,” says Jennifer Keesmaat, former chief planner for Toronto, who advised the Ministry of Infrastructure on how to set this next iteration up for success. She’s enthusiastic about the rethought plan for the area: “If you look at what we’re doing now on that site, it’s classic city building with a 21st-century twist, which means it’s a carbon-neutral community. It’s a totally electrified community. It’s a community that prioritizes affordable housing, because we have an affordable-housing crisis in our city. It’s a community that has a strong emphasis on green space and urban agriculture and urban farming. Are those things that are derived from Sidewalk’s proposal? Not really.”

Continue Reading

Tech

Rewriting what we thought was possible in biotech

Published

on

Rewriting what we thought was possible in biotech


What ML and AI in biotech broadly need to engage with are the holes that are unique to the study of health. Success stories like neural nets that learned to identify dogs in images were built with the help of high-quality image labeling that people were in a good position to provide. Even attempts to generate or translate human language are easily verified and audited by experts who speak a particular language. 

Instead, much of biology, health, and medicine is very much in the stage of fundamental discovery. How do neurodegenerative diseases work? What environmental factors really matter? What role does nutrition play in overall human health? We don’t know yet. In health and biotech, machine learning is taking on a different, more challenging, task—one that will require less engineering and more science.

Marzyeh Ghassemi is an assistant professor at MIT and a faculty member at the Vector Institute (and a 35 Innovators honoree in 2018).

Continue Reading

Copyright © 2021 Seminole Press.