Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker
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."
Democratize the White Coat by Honoring Black, Indigenous, and People of Color in Science
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
Journalists, educators, and curators have responded to Black Lives Matter by highlighting the history and achievements of Black Americans in a variety of fields, including science. The movement has also sparked important demands to address longstanding scientific inequities such as lack of access to quality healthcare and the disproportionate impact of climate change and environmental pollution on neighborhoods of Black, Indigenous, and people of color (BIPOC). Making such improvements requires bringing BIPOC into science and into positions of leadership in laboratories, graduate schools, medical practices, and clinical trials. The moment is right to challenge scientific gatekeepers to respond to Black Lives Matter by widening the pathways that determine who becomes a scientist, a researcher, or a clinician.
The scientific workforce has long lacked diversity, which in turn discourages Black people from pursuing such careers. Causes include a dearth of mentors and role models, preconceived notions that science is exclusive to white males, and subpar STEM education. Across race, gender, class, ability, and all other dimensions that inform how an individual navigates the world, from the familial to the global level, seeing role models who resemble you impacts what you strive for and believe possible. As Marian Wright Edelman stated, "You can't be what you can't see"—a truth with ever-increasing resonance since the U.S. is projected to be minority-white by 2045.
Black Americans have paved the way for the nation to lead in science and technology, despite marginalization and exclusion from textbooks. Physicist Dr. Shirley Ann Jackson invented the technology behind Caller I.D. and Call Waiting. Otis Boykin's patents made televisions and radios what they are today. Thanks to the 2017 movie Hidden Figures, millions of Americans know about Katherine Johnson, the NASA mathematician whose calculations were essential to the successful trajectory of the Apollo 11 mission.
However, highlighting past role models who were Black achievers is not enough and paints too static a picture—especially when examples of transformative work by contemporary BIPOC scientists serving BIPOC communities abound. Cognitive neuroscientist Dr. Jonathan Jackson founded the Community Access, Recruitment, & Engagement (CARE) Research Center with the goal to break down barriers so that people of color participate in clinical trials. Geneticist Dr. Nanibaa' Garrison's research creates ethical frameworks to overcome genomic injustices so Indigenous populations can benefit from genetic research. Computer scientists Joy Buolamwini and Dr. Timnit Gebru's research drew attention to reinforced racial bias in artificial intelligence, leading Microsoft, Amazon, and IBM this summer to halt use of their facial recognition software.
"Integration does not mean equality if the space being integrated isn't exuberantly down for the cause."
In order to honor concretely the ubiquitous public statements and commitments to justice and equity that flooded everyone's inboxes in early June, we must include traditionally underrepresented voices in all phases of science and its applications. For guidance, we would benefit from listening to activists leading, for example, climate marches and protests over toxic water. Indeed, science is at the core of the issues for which young BIPOC are mobilizing. We need to sit down with these individuals to gain their input on how the narratives, practices, and opportunities in science should change. As Zeena Abdulkarim, a youth climate change organizer working with Zero Hour, explains: "Minority communities are exposed to what the privileged and people in power are not; therefore these communities know the right steps to take in the change we need for the kickstart of true social and environmental justice."
Two other Black youth, for example, used the platform of the laboratory while in high school to mobilize for change. Elle Lanair Lett, now specializing in epidemiology as an M.D.-Ph.D. student in Philadelphia, was prompted by family prevalence of diabetes to research the genetics of pancreatic cells. Dr. Otana Jakpor, now an ophthalmology resident in Michigan, was motivated by the pollution in her hometown of Riverside, California, to research the pulmonary effects of indoor air purifiers, with findings that influenced California ozone regulations. Both became finalists in a national science fair, propelling them on paths toward science careers. These young scientists demonstrate how people's communities and lived experiences can shape trajectories of science research, which, in turn, determines which visions for society are materialized and popularized.
We can also gain insight from another childhood science fair veteran, self-proclaimed "Black STEMinist" Augusta Uwamanzu-Nna, who graduated from college in May and works as a bioengineer. In her view, "we need to shift the burden away from Black people and onto individuals who have contributed to our current reality—fundamentally requiring understanding, open-mindedness, a lack of bias, cultural competency, anti-racism, anti-homophobia, and many, many other things."
Celebrating BIPOC's accomplishments in science and cultivating new leadership today are strong first steps to make science a guiding force for all. Ms. Uwamanzu-Nna keenly reminds us, "Integration does not mean equality if the space being integrated isn't exuberantly down for the cause." Indeed, educational institutions, scientific companies, and medical centers must acknowledge and embrace their role in democratizing science in order for society to realize racial and scientific justice.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
Alethea.ai sports a grid of faces smiling, blinking and looking about. Some are beautiful, some are oddly familiar, but all share one thing in common—they are fake.
Alethea creates "synthetic media"— including digital faces customers can license saying anything they choose with any voice they choose. Companies can hire these photorealistic avatars to appear in explainer videos, advertisements, multimedia projects or any other applications they might dream up without running auditions or paying talent agents or actor fees. Licenses begin at a mere $99. Companies may also license digital avatars of real celebrities or hire mashups created from real celebrities including "Don Exotic" (a mashup of Donald Trump and Joe Exotic) or "Baby Obama" (a large-eared toddler that looks remarkably similar to a former U.S. President).
Naturally, in the midst of the COVID pandemic, the appeal is understandable. Rather than flying to a remote location to film a beer commercial, an actor can simply license their avatar to do the work for them. The question is—where and when this tech will cross the line between legitimately licensed and authorized synthetic media to deep fakes—synthetic videos designed to deceive the public for financial and political gain.
Deep fakes are not new. From written quotes that are manipulated and taken out of context to audio quotes that are spliced together to mean something other than originally intended, misrepresentation has been around for centuries. What is new is the technology that allows this sort of seamless and sophisticated deception to be brought to the world of video.
"At one point, video content was considered more reliable, and had a higher threshold of trust," said Alethea CEO and co-founder, Arif Khan. "We think video is harder to fake and we aren't yet as sensitive to detecting those fakes. But the technology is definitely there."
"In the future, each of us will only trust about 15 people and that's it," said Phil Lelyveld, who serves as Immersive Media Program Lead at the Entertainment Technology Center at the University of Southern California. "It's already very difficult to tell true footage from fake. In the future, I expect this will only become more difficult."
How do we know what's true in a world where original videos created with avatars of celebrities and politicians can be manipulated to say virtually anything?
As the U.S. 2020 Presidential Election nears, the potential moral and ethical implications of this technology are startling. A number of cases of truth tampering have recently been widely publicized. On August 5, President Donald Trump's campaign released an ad featuring several photos of Joe Biden that were altered to make it seem like was hiding all alone in his basement. In one photo, at least ten people who had been sitting with Biden in the original shot were cut out. In other photos, Biden's image was removed from a nature preserve and praying in church to make it appear Biden was in that same basement. Recently several videos of Speaker of the House Nancy Pelosi were slowed down by 75 percent to make her sound as if her speech was slurred.
During a campaign event in Florida on September 15 of this year, former Vice President Joe Biden was introduced by Puerto Rican singer-songwriter Luis Fonsi. After he was introduced, Biden paid tribute to the singer-songwriter—he held up his cell phone and played the hit song "Despecito". Shortly afterward, a doctored version of this video appeared on self-described parody site the United Spot replacing the Despicito with N.W.A.'s "F—- Tha Police". By September 16, Donald Trump retweeted the video, twice—first with the line "What is this all about" and second with the line "China is drooling. They can't believe this!" Twitter was quick to mark the video in these tweets as manipulated media.
Twitter had previously addressed several of Donald Trump's tweets—flagging a video shared in June as manipulated media and removing altogether a video shared by Trump in July showing a group promoting the hydroxychloroquine as an effective cure for COVID-19. Many of these manipulated videos are ultimately flagged or taken down, but not before they are seen and shared by millions of online viewers.
These faked videos were exposed rather quickly, as they could be compared with the original, publicly available source material. But what happens when there is no original source material? How do we know what's true in a world where original videos created with avatars of celebrities and politicians can be manipulated to say virtually anything?
"This type of fake media is a profound threat to our democracy," said Reid Blackman, the CEO of VIRTUE--an ethics consultancy for AI leaders. "Democracy depends on well-informed citizens. When citizens can't or won't discern between real and fake news, the implications are huge."
In light of the importance of reliable information in the political system, there's a clear and present need to verify that the images and news we consume is authentic. So how can anyone ever know that the content they are viewing is real?
"This will not be a simple technological solution," said Blackman. "There is no 'truth' button to push to verify authenticity. There's plenty of blame and condemnation to go around. Purveyors of information have a responsibility to vet the reliability of their sources. And consumers also have a responsibility to vet their sources."
Yet the process of verifying sources has never been more challenging. More and more citizens are choosing to live in a "media bubble"—gathering and sharing news only from and with people who share their political leanings and opinions. At one time, United States broadcasters were bound by the Fairness Doctrine—requiring them to present controversial issues important to the public in a way that the FCC deemed honest, equitable and balanced. The repeal of this doctrine in 1987 paved the way for new forms of cable news channels such as Fox News and MSNBC that appealed to viewers with a particular point of view. The Internet has only exacerbated these tendencies. Social media algorithms are designed to keep people clicking within their comfort zones by presenting members with only the thoughts and opinions they want to hear.
"I sometimes laugh when I hear people tell me they can back a particular opinion they hold with research," said Blackman. "Having conducted a fair bit of true scientific research, I am aware that clicking on one article on the Internet hardly qualifies. But a surprising number of people believe that finding any source online that states the fact they choose to believe is the same as proving it true."
Back to the fundamental challenge: How do we as a society root out what's false online? Lelyveld suggests that it will begin by verifying things that are known to be true rather than trying to call out everything that is fake. "The EU called me in to talk about how to deal with fake news coming out of Russia," said Lelyveld. "I told them Hollywood has spent 100 years developing special effects technology to make things that are wholly fictional indistinguishable from the truth. I told them that you'll never chase down every source of fake news. You're better off focusing on what can be proved true."
Arif Khan agrees. "There are probably 100 accounts attributed to Elon Musk on Twitter, but only one has the blue checkmark," said Khan. "That means Twitter has verified that an account of public interest is real. That's what we're trying to do with our platform. Allow celebrities to verify that specific videos were licensed and authorized directly by them."
Alethea will use another key technology called blockchain to mark all authentic authorized videos with celebrity avatars. Blockchain uses a distributed ledger technology to make sure that no undetected changes have been made to the content. Think of the difference between editing a document in a traditional word processing program and editing in a distributed online editing system like Google Docs. In a traditional word processing program, you can edit and copy a document without revealing any changes. In a shared editing system like Google Docs, every person who shares the document can see a record of every edit, addition and copy made of any portion of the document. In a similar way, blockchain helps Alethea ensure that approved videos have not been copied or altered inappropriately.
While AI companies like Alethea are moving to ensure that avatars based on real individuals aren't wrongly identified, the situation becomes a bit murkier when it comes to the question of representing groups, races, creeds, and other forms of identity. Alethea is rightly proud that the completely artificial avatars visually represent a variety of ages, races and sexes. However, companies could conceivably license an avatar to represent a marginalized group without actually hiring a person within that group to decide what the avatar will do or say.
"I don't know if I would call this tokenism, as that is difficult to identify without understanding the hiring company's intent," said Blackman. "Where this becomes deeply troubling is when avatars are used to represent a marginalized group without clearly pointing out the actor is an avatar. It's one thing for an African American woman avatar to say, 'I like ice cream.' It's entirely different thing for an African American woman avatar to say she supports a particular political candidate. In the second case, the avatar is being used as social proof that real people of a certain type back a certain political idea. And there the deception is far more problematic."
"It always comes down to unintended consequences of technology," said Lelyveld. "Technology is neutral—it's only the implementation that has the power to be good or bad. Without a thoughtful approach to the cultural, moral and political implications of technology, it often drifts towards the bad. We need to make a conscious decision as we release new technology to ensure it moves towards the good."
When presented with the idea that his avatars might be used to misrepresent marginalized groups, Khan was thoughtful. "Yes, I can see that is an unintended consequence of our technology. We would like to encourage people to license the avatars of real people, who would have final approval over what their avatars say or do. As to what people do with our completely artificial avatars, we will have to consider that moving forward."
Lelyveld frankly sees the ability for advertisers to create avatars that are our assistants or even our friends as a greater moral concern. "Once our digital assistant or avatar becomes an integral part of our life—even a friend as it were, what's to stop marketers from having those digital friends make suggestions about what drink we buy, which shirt we wear or even which candidate we elect? The possibilities for bad actors to reach us through our digital circle is mind-boggling."
Ultimately, Blackman suggests, we as a society will need to make decisions about what matters to us. "We will need to build policies and write laws—tackling the biggest problems like political deep fakes first. And then we have to figure out how to make the penalties stiff enough to matter. Fining a multibillion-dollar company a few million for a major offense isn't likely to move the needle. The punishment will need to fit the crime."
Until then, media consumers will need to do their own due diligence—to do the difficult work of uncovering the often messy and deeply uncomfortable news that's the truth.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]