The Death Predictor: A Helpful New Tool or an Ethical Morass?
A senior in hospice care.
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."
Here's how one doctor overcame extraordinary odds to help create the birth control pill
Dr. Percy Julian had so many personal and professional obstacles throughout his life, it’s amazing he was able to accomplish anything at all. But this hidden figure not only overcame these incredible obstacles, he also laid the foundation for the creation of the birth control pill.
Julian’s first obstacle was growing up in the Jim Crow-era south in the early part of the twentieth century, where racial segregation kept many African-Americans out of schools, libraries, parks, restaurants, and more. Despite limited opportunities and education, Julian was accepted to DePauw University in Indiana, where he majored in chemistry. But in college, Julian encountered another obstacle: he wasn’t allowed to stay in DePauw’s student housing because of segregation. Julian found lodging in an off-campus boarding house that refused to serve him meals. To pay for his room, board, and food, Julian waited tables and fired furnaces while he studied chemistry full-time. Incredibly, he graduated in 1920 as valedictorian of his class.
After graduation, Julian landed a fellowship at Harvard University to study chemistry—but here, Julian ran into yet another obstacle. Harvard thought that white students would resent being taught by Julian, an African-American man, so they withdrew his teaching assistantship. Julian instead decided to complete his PhD at the University of Vienna in Austria. When he did, he became one of the first African Americans to ever receive a PhD in chemistry.
Julian received offers for professorships, fellowships, and jobs throughout the 1930s, due to his impressive qualifications—but these offers were almost always revoked when schools or potential employers found out Julian was black. In one instance, Julian was offered a job at the Institute of Paper Chemistory in Appleton, Wisconsin—but Appleton, like many cities in the United States at the time, was known as a “sundown town,” which meant that black people weren’t allowed to be there after dark. As a result, Julian lost the job.
During this time, Julian became an expert at synthesis, which is the process of turning one substance into another through a series of planned chemical reactions. Julian synthesized a plant compound called physostigmine, which would later become a treatment for an eye disease called glaucoma.
In 1936, Julian was finally able to land—and keep—a job at Glidden, and there he found a way to extract soybean protein. This was used to produce a fire-retardant foam used in fire extinguishers to smother oil and gasoline fires aboard ships and aircraft carriers, and it ended up saving the lives of thousands of soldiers during World War II.
At Glidden, Julian found a way to synthesize human sex hormones such as progesterone, estrogen, and testosterone, from plants. This was a hugely profitable discovery for his company—but it also meant that clinicians now had huge quantities of these hormones, making hormone therapy cheaper and easier to come by. His work also laid the foundation for the creation of hormonal birth control: Without the ability to synthesize these hormones, hormonal birth control would not exist.
Julian left Glidden in the 1950s and formed his own company, called Julian Laboratories, outside of Chicago, where he manufactured steroids and conducted his own research. The company turned profitable within a year, but even so Julian’s obstacles weren’t over. In 1950 and 1951, Julian’s home was firebombed and attacked with dynamite, with his family inside. Julian often had to sit out on the front porch of his home with a shotgun to protect his family from violence.
But despite years of racism and violence, Julian’s story has a happy ending. Julian’s family was eventually welcomed into the neighborhood and protected from future attacks (Julian’s daughter lives there to this day). Julian then became one of the country’s first black millionaires when he sold his company in the 1960s.
When Julian passed away at the age of 76, he had more than 130 chemical patents to his name and left behind a body of work that benefits people to this day.
Therapies for Healthy Aging with Dr. Alexandra Bause
My guest today is Dr. Alexandra Bause, a biologist who has dedicated her career to advancing health, medicine and healthier human lifespans. Dr. Bause co-founded a company called Apollo Health Ventures in 2017. Currently a venture partner at Apollo, she's immersed in the discoveries underway in Apollo’s Venture Lab while the company focuses on assembling a team of investors to support progress. Dr. Bause and Apollo Health Ventures say that biotech is at “an inflection point” and is set to become a driver of important change and economic value.
Previously, Dr. Bause worked at the Boston Consulting Group in its healthcare practice specializing in biopharma strategy, among other priorities
She did her PhD studies at Harvard Medical School focusing on molecular mechanisms that contribute to cellular aging, and she’s also a trained pharmacist
In the episode, we talk about the present and future of therapeutics that could increase people’s spans of health, the benefits of certain lifestyle practice, the best use of electronic wearables for these purposes, and much more.
Dr. Bause is at the forefront of developing interventions that target the aging process with the aim of ensuring that all of us can have healthier, more productive lifespans.
