Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker
![Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker](https://upworthyscience.com/media-library/health-firm-populytics-tracks-and-analyzes-patient-data-and-makes-care-suggestions-based-on-that-data.jpg?id=24159387&width=1245&height=700&quality=85&coordinates=0%2C82%2C0%2C83)
Health firm Populytics tracks and analyzes patient data, and makes care suggestions based on that data.
The diabetic patient hit the danger zone.
Ideally, blood sugar, measured by an A1C test, rests at 5.9 or less. A 7 is elevated, according to the Diabetes Council. Over 10, and you're into the extreme danger zone, at risk of every diabetic crisis from kidney failure to blindness.
In three months of working with a case manager, Jen's blood sugar had dropped to 7.2, a much safer range.
This patient's A1C was 10. Let's call her Jen for the sake of this story. (Although the facts of her case are real, the patient's actual name wasn't released due to privacy laws.).
Jen happens to live in Pennsylvania's Lehigh Valley, home of the nonprofit Lehigh Valley Health Network, which has eight hospital campuses and various clinics and other services. This network has invested more than $1 billion in IT infrastructure and founded Populytics, a spin-off firm that tracks and analyzes patient data, and makes care suggestions based on that data.
When Jen left the doctor's office, the Populytics data machine started churning, analyzing her data compared to a wealth of information about future likely hospital visits if she did not comply with recommendations, as well as the potential positive impacts of outreach and early intervention.
About a month after Jen received the dangerous blood test results, a community outreach specialist with psychological training called her. She was on a list generated by Populytics of follow-up patients to contact.
"It's a very gentle conversation," says Cathryn Kelly, who manages a care coordination team at Populytics. "The case manager provides them understanding and support and coaching." The goal, in this case, was small behavioral changes that would actually stick, like dietary ones.
In three months of working with a case manager, Jen's blood sugar had dropped to 7.2, a much safer range. The odds of her cycling back to the hospital ER or veering into kidney failure, or worse, had dropped significantly.
While the health network is extremely localized to one area of one state, using data to inform precise medical decision-making appears to be the wave of the future, says Ann Mongovern, the associate director of Health Care Ethics at the Markkula Center for Applied Ethics at Santa Clara University in California.
"Many hospitals and hospital systems don't yet try to do this at all, which is striking given where we're at in terms of our general technical ability in this society," Mongovern says.
How It Happened
While many hospitals make money by filling beds, the Lehigh Valley Health Network, as a nonprofit, accepts many patients on Medicaid and other government insurances that don't cover some of the costs of a hospitalization. The area's population is both poorer and older than national averages, according to the U.S. Census data, meaning more people with higher medical needs that may not have the support to care for themselves. They end up in the ER, or worse, again and again.
In the early 2000s, LVHN CEO Dr. Brian Nester started wondering if his health network could develop a way to predict who is most likely to land themselves a pricey ICU stay -- and offer support before those people end up needing serious care.
Embracing data use in such specific ways also brings up issues of data security and patient safety.
"There was an early understanding, even if you go back to the (federal) balanced budget act of 1997, that we were just kicking the can down the road to having a functional financial model to deliver healthcare to everyone with a reasonable price," Nester says. "We've got a lot of people living longer without more of an investment in the healthcare trust."
Popultyics, founded in 2013, was the result of years of planning and agonizing over those population numbers and cost concerns.
"We looked at our own health plan," Nester says. Out of all the employees and dependants on the LVHN's own insurance network, "roughly 1.5 percent of our 25,000 people — under 400 people — drove $30 million of our $130 million on insurance costs -- about 25 percent."
"You don't have to boil the ocean to take cost out of the system," he says. "You just have to focus on that 1.5%."
Take Jen, the diabetic patient. High blood sugar can lead to kidney failure, which can mean weekly expensive dialysis for 20 years. Investing in the data and staff to reach patients, he says, is "pennies compared to $100 bills."
For most doctors, "there's no awareness for providers to know who they should be seeing vs. who they are seeing. There's no incentive, because the incentive is to see as many patients as you can," he says.
To change that, first the LVHN invested in the popular medical management system, Epic. Then, they negotiated with the top 18 insurance companies that cover patients in the region to allow access to their patient care data, which means they have reams of patient history to feed the analytics machine in order to make predictions about outcomes. Nester admits not every hospital could do that -- with 52 percent of the market share, LVHN had a very strong negotiating position.
Third party services take that data and churn out analytics that feeds models and care management plans. All identifying information is stripped from the data.
"We can do predictive modeling in patients," says Populytics President and CEO Gregory Kile. "We can identify care gaps. Those care gaps are noted as alerts when the patient presents at the office."
Kile uses himself as a hypothetical patient.
"I pull up Gregory Kile, and boom, I see a flag or an alert. I see he hasn't been in for his last blood test. There is a care gap there we need to complete."
"There's just so much more you can do with that information," he says, envisioning a future where follow-up for, say, knee replacement surgery and outcomes could be tracked, and either validated or changed.
Ethical Issues at the Forefront
Of course, embracing data use in such specific ways also brings up issues of security and patient safety. For example, says medical ethicist Mongovern, there are many touchpoints where breaches could occur. The public has a growing awareness of how data used to personalize their experiences, such as social media analytics, can also be monetized and sold in ways that benefit a company, but not the user. That's not to say data supporting medical decisions is a bad thing, she says, just one with potential for public distrust if not handled thoughtfully.
"You're going to need to do this to stay competitive," she says. "But there's obviously big challenges, not the least of which is patient trust."
So far, a majority of the patients targeted – 62 percent -- appear to embrace the effort.
Among the ways the LVHN uses the data is monthly reports they call registries, which include patients who have just come in contact with the health network, either through the hospital or a doctor that works with them. The community outreach team members at Populytics take the names from the list, pull their records, and start calling. So far, a majority of the patients targeted – 62 percent -- appear to embrace the effort.
Says Nester: "Most of these are vulnerable people who are thrilled to have someone care about them. So they engage, and when a person engages in their care, they take their insulin shots. It's not rocket science. The rocket science is in identifying who the people are — the delivery of care is easy."
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.
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.