The Death Predictor: A Helpful New Tool or an Ethical Morass?
Whenever Eric Karl Oermann has to tell a patient about a terrible prognosis, their first question is always: "how long do I have?" Oermann would like to offer a precise answer, to provide some certainty and help guide treatment. But although he's one of the country's foremost experts in medical artificial intelligence, Oermann is still dependent on a computer algorithm that's often wrong.
Doctors are notoriously terrible at guessing how long their patients will live.
Artificial intelligence, now often called deep learning or neural networks, has radically transformed language and image processing. It's allowed computers to play chess better than the world's grand masters and outwit the best Jeopardy players. But it still can't precisely tell a doctor how long a patient has left – or how to help that person live longer.
Someday, researchers predict, computers will be able to watch a video of a patient to determine their health status. Doctors will no longer have to spend hours inputting data into medical records. And computers will do a better job than specialists at identifying tiny tumors, impending crises, and, yes, figuring out how long the patient has to live. Oermann, a neurosurgeon at Mount Sinai, says all that technology will allow doctors to spend more time doing what they do best: talking with their patients. "I want to see more deep learning and computers in a clinical setting," he says, "so there can be more human interaction." But those days are still at least three to five years off, Oermann and other researchers say.
Doctors are notoriously terrible at guessing how long their patients will live, says Nigam Shah, an associate professor at Stanford University and assistant director of the school's Center for Biomedical Informatics Research. Doctors don't want to believe that their patient – whom they've come to like – will die. "Doctors over-estimate survival many-fold," Shah says. "How do you go into work, in say, oncology, and not be delusionally optimistic? You have to be."
But patients near the end of life will get better treatment – and even live longer – if they are overseen by hospice or palliative care, research shows. So, instead of relying on human bias to select those whose lives are nearing their end, Shah and his colleagues showed that they could use a deep learning algorithm based on medical records to flag incoming patients with a life expectancy of three months to a year. They use that data to indicate who might need palliative care. Then, the palliative care team can reach out to treating physicians proactively, instead of relying on their referrals or taking the time to read extensive medical charts.
But, although the system works well, Shah isn't yet sure if such indicators actually get the appropriate patients into palliative care. He's recently partnered with a palliative care doctor to run a gold-standard clinical trial to test whether patients who are flagged by this algorithm are indeed a better match for palliative care.
"What is effective from a health system perspective might not be effective from a treating physician's perspective and might not be effective from the patient's perspective," Shah notes. "I don't have a good way to guess everybody's reaction without actually studying it." Whether palliative care is appropriate, for instance, depends on more than just the patient's health status. "If the patient's not ready, the family's not ready and the doctor's not ready, then you're just banging your head against the wall," Shah says. "Given limited capacity, it's a waste of resources" to put that person in palliative care.
The algorithm isn't perfect, but "on balance, it leads to better decisions more often."
Alexander Smith and Sei Lee, both palliative care doctors, work together at the University of California, San Francisco, to develop predictions for patients who come to the hospital with a complicated prognosis or a history of decline. Their algorithm, they say, helps decide if this patient's problems – which might include diabetes, heart disease, a slow-growing cancer, and memory issues – make them eligible for hospice. The algorithm isn't perfect, they both agree, but "on balance, it leads to better decisions more often," Smith says.
Bethany Percha, an assistant professor at Mount Sinai, says that an algorithm may tell doctors that their patient is trending downward, but it doesn't do anything to change that trajectory. "Even if you can predict something, what can you do about it?" Algorithms may be able to offer treatment suggestions – but not what specific actions will alter a patient's future, says Percha, also the chief technology officer of Precise Health Enterprise, a product development group within Mount Sinai. And the algorithms remain challenging to develop. Electronic medical records may be great at her hospital, but if the patient dies at a different one, her system won't know. If she wants to be certain a patient has died, she has to merge social security records of death with her system's medical records – a time-consuming and cumbersome process.
An algorithm that learns from biased data will be biased, Shah says. Patients who are poor or African American historically have had worse health outcomes. If researchers train an algorithm on data that includes those biases, they get baked into the algorithms, which can then lead to a self-fulfilling prophesy. Smith and Lee say they've taken race out of their algorithms to avoid this bias.
Age is even trickier. There's no question that someone's risk of illness and death goes up with age. But an 85-year-old who breaks a hip running a marathon should probably be treated very differently than an 85-year-old who breaks a hip trying to get out of a chair in a dementia care unit. That's why the doctor can never be taken out of the equation, Shah says. Human judgment will always be required in medical care and an algorithm should never be followed blindly, he says.
Experts say that the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully.
Researchers are also concerned that their algorithms will be used to ration care, or that insurance companies will use their data to justify a rate increase. If an algorithm predicts a patient is going to end up back in the hospital soon, "who's benefitting from knowing a patient is going to be readmitted? Probably the insurance company," Percha says.
Still, Percha and others say, the flaws in artificial intelligence algorithms shouldn't prevent people from using them – carefully. "These are new and exciting tools that have a lot of potential uses. We need to be conscious about how to use them going forward, but it doesn't mean we shouldn't go down this road," she says. "I think the potential benefits outweigh the risks, especially because we've barely scratched the surface of what big data can do right now."
A startup aims to make medicines in space
Story by Big Think
On June 12, a SpaceX Falcon 9 rocket deployed 72 small satellites for customers — including the world’s first space factory.
The challenge: In 2019, pharma giant Merck revealed that an experiment on the International Space Station had shown how to make its blockbuster cancer drug Keytruda more stable. That meant it could now be administered via a shot rather than through an IV infusion.
The key to the discovery was the fact that particles behave differently when freed from the force of gravity — seeing how its drug crystalized in microgravity helped Merck figure out how to tweak its manufacturing process on Earth to produce the more stable version.
Microgravity research could potentially lead to many more discoveries like this one, or even the development of brand-new drugs, but ISS astronauts only have so much time for commercial experiments.
“There are many high-performance products that are only possible to make in zero-gravity, which is a manufacturing capability that cannot be replicated in any factory on Earth.”-- Will Bruey.
The only options for accessing microgravity (or free fall) outside of orbit, meanwhile, are parabolic airplane flights and drop towers, and those are only useful for experiments that require less than a minute in microgravity — Merck’s ISS experiment took 18 days.
The idea: In 2021, California startup Varda Space Industries announced its intention to build the world’s first space factory, to manufacture not only pharmaceuticals but other products that could benefit from being made in microgravity, such as semiconductors and fiber optic cables.
This factory would consist of a commercial satellite platform attached to two Varda-made modules. One module would contain equipment capable of autonomously manufacturing a product. The other would be a reentry capsule to bring the finished goods back to Earth.
“There are many high-performance products that are only possible to make in zero-gravity, which is a manufacturing capability that cannot be replicated in any factory on Earth,” said CEO Will Bruey, who’d previously developed and flown spacecraft for SpaceX.
“We have a team stacked with aerospace talent in the prime of their careers, focused on getting working hardware to orbit as quickly as possible,” he continued.
“[Pharmaceuticals] are the most valuable chemicals per unit mass. And they also have a large market on Earth.” -- Will Bruey, CEO of Varda Space.
What’s new? At the time, Varda said it planned to launch its first space factory in 2023, and, in what feels like a first for a space startup, it has actually hit that ambitious launch schedule.
“We have ACQUISITION OF SIGNAL,” the startup tweeted soon after the Falcon 9 launch on June 12. “The world’s first space factory’s solar panels have found the sun and it’s beginning to de-tumble.”
During the satellite’s first week in space, Varda will focus on testing its systems to make sure everything works as hoped. The second week will be dedicated to heating and cooling the old HIV-AIDS drug ritonavir repeatedly to study how its particles crystalize in microgravity.
After about a month in space, Varda will attempt to bring its first space factory back to Earth, sending it through the atmosphere at hypersonic speeds and then using a parachute system to safely land at the Department of Defense’s Utah Test and Training Range.
Looking ahead: Ultimately, Varda’s space factories could end up serving dual purposes as manufacturing facilities and hypersonic testbeds — the Air Force has already awarded the startup a contract to use its next reentry capsule to test hardware for hypersonic missiles.
But as for manufacturing other types of goods, Varda plans to stick with drugs for now.
“[Pharmaceuticals] are the most valuable chemicals per unit mass,” Bruey told CNN. “And they also have a large market on Earth.”
“You’re not going to see Varda do anything other than pharmaceuticals for the next minimum of six, seven years,” added Delian Asparouhov, Varda’s co-founder and president.
Genes that protect health with Dr. Nir Barzilai
In today’s podcast episode, I talk with Nir Barzilai, a geroscientist, which means he studies the biology of aging. Barzilai directs the Institute for Aging Research at the Albert Einstein College of Medicine.
My first question for Dr. Barzilai was: why do we age? And is there anything to be done about it? His answers were encouraging. We can’t live forever, but we have some control over the process, as he argues in his book, Age Later.
Dr. Barzilai told me that centenarians differ from the rest of us because they have unique gene mutations that help them stay healthy longer. For most of us, the words “gene mutations” spell trouble - we associate these words with cancer or neurodegenerative diseases, but apparently not all mutations are bad.
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Centenarians may have essentially won the genetic lottery, but that doesn’t mean the rest of us are predestined to have a specific lifespan and health span, or the amount of time spent living productively and enjoyably. “Aging is a mother of all diseases,” Dr. Barzilai told me. And as a disease, it can be targeted by therapeutics. Dr. Barzilai’s team is already running clinical trials on such therapeutics — and the results are promising.
More about Dr. Barzilai: He is scientific director of AFAR, American Federation for Aging Research. As part of his work, Dr. Barzilai studies families of centenarians and their genetics to learn how the rest of us can learn and benefit from their super-aging. He also organizing a clinical trial to test a specific drug that may slow aging.
Show Links
Age Later: Health Span, Life Span, and the New Science of Longevity https://www.amazon.com/Age-Later-Healthiest-Sharpest-Centenarians/dp/1250230853
American Federation for Aging Research https://www.afar.org
https://www.afar.org/nir-barzilai
https://www.einsteinmed.edu/faculty/484/nir-barzilai/
Metformin as a Tool to Target Aging
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943638/
Benefits of Metformin in Attenuating the Hallmarks of Aging https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347426/
The Longevity Genes Project https://www.einsteinmed.edu/centers/aging/longevity-genes-project/
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.