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What these factors don’t take into account is the exposure of patients with covid-19, residents say. This means that the algorithm did not distinguish between those who caught the covid from patients and those who got it from community spread, including employees working remotely. And like first reported By ProPublica, residents were told that because they rotated between departments rather than keeping a single assignment, they were losing points associated with the departments where they worked.
The third category of the algorithm refers to the California Department of Public Health vaccine allocation guidelines. These focus on the risk of exposure as the highest factor for prioritization of vaccines. The guidelines are primarily intended for county and local governments in deciding how to prioritize the vaccine, rather than how to prioritize between departments in a hospital: but they specifically include residents, as well as the departments where they work, in the level. highest priority.
The “CDPH range” factor may give residents a higher score, but this is still not enough to offset the higher points given to other criteria.
“Why did they do it this way?”
Stanford tried to account for many more variables than other medical institutions, but Jeffrey Kahn, director of the Johns Hopkins Berkman Institute of Bioethics, said the approach was too complicated. “The more different weights there are for different things, then it becomes more difficult to understand, ‘why did they act that way?’ ‘
Kahn served on Johns Hopkins’ 20-member committee on vaccine allocation and said his university allocated vaccines based simply on employment and risk of exposure to covid-19.
He says that decision was based on discussions that deliberately included different perspectives – including those of residents – and in coordination with other hospitals in Maryland. Somewhere else, map of the University of California at San Francisco is based on a similar assessment of the risk of exposure to the virus. Mass General Brigham in Boston categorizes employees into four groups based on department and workplace, according to an internal email reviewed by MIT Technology Review.
“There is so little confidence around so much pandemic related stuff, we cannot waste it.”
“It’s really important [for] any approach like this needs to be transparent and public… and not something really hard to understand, ”Kahn says. “There is so little confidence around so much pandemic related stuff, we cannot waste it.”
Algorithms are commonly used in healthcare to categorize patients by level of risk in order to distribute care and resources more equitably. But the more variables used, the more difficult it is to assess whether the calculations may be wrong.
For example, in 2019, a study published in Science showed that 10 algorithms widely used for the delivery of care in the United States ended up favoring white patients over black patients. The problem, it turned out, was that the developers of the algorithms assumed that patients who spent more on healthcare were sicker and needed more help. In reality, the top 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 whites.
Irene Chen, a PhD student at MIT who studies the use of fair algorithms in healthcare, suspects that this is what happened at Stanford: The designers of the formula chose variables they believed would serve good indicators for the level of covid risk of a given staff member. But they did not verify that these proxies lead to reasonable results, nor did they significantly respond to the community’s contribution when the vaccination plan was unveiled on Tuesday last week. “It’s not a bad thing that people thought about this afterwards,” Chen says. “It was because there was no mechanism to remedy it.”
Canary in the coal mine?
After the protests, Stanford issued a apologies, saying he would revise his distribution plan.
Hospital representatives did not respond to questions about who to include in the new planning processes or whether the algorithm would continue to be used. An internal email summarizing the medical school’s response, shared with the MIT Technology Review, says that neither program heads, department heads, attending physicians, nor nurses were involved in the the design of the original algorithm. Now, however, some faculties are pushing for a bigger role, eliminating algorithm results altogether and instead giving division heads and presidents the power to make decisions for their own teams.
Other department directors encouraged residents to get vaccinated first. Some have even asked faculty to bring residents with them when they get their shots, or to delay their shots so others can start.
Some residents bypass the university health system entirely. Nurial Moghavem, a neurology resident who first brought the issues to the attention of Stanford, tweeted on Friday afternoon that he had finally received his vaccine – not at Stanford, but at a public hospital in Santa Clara County.
“I got vaccinated today to protect myself, my family and my patients”, he tweeted. “But I only got the chance because my county public hospital believes residents are essential frontline providers. Grateful. “
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