Who Qualifies as an “Expert” And How Can We Decide Who Is Trustworthy?
Discerning a real expert from a charlatan is crucial during the COVID-19 pandemic and beyond.
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.
Expertise is a slippery concept. Who has it, who claims it, and who attributes or yields it to whom is a culturally specific, sociological process. During the COVID-19 pandemic, we have witnessed a remarkable emergence of legitimate and not-so-legitimate scientists publicly claiming or being attributed to have academic expertise in precisely my field: infectious disease epidemiology. From any vantage point, it is clear that charlatans abound out there, garnering TV coverage and hundreds of thousands of Twitter followers based on loud opinions despite flimsy credentials. What is more interesting as an insider is the gradient of expertise beyond these obvious fakers.
A person's expertise is not a fixed attribute; it is a hierarchical trait defined relative to others. Despite my protestations, I am the go-to expert on every aspect of the pandemic to my family. To a reporter, I might do my best to answer a question about the immune response to SARS-CoV-2, noting that I'm not an immunologist. Among other academic scientists, my expertise is more well-defined as a subfield of epidemiology, and within that as a particular area within infectious disease epidemiology. There's a fractal quality to it; as you zoom in on a particular subject, a differentiation of expertise emerges among scientists who, from farther out, appear to be interchangeable.
We all have our scientific domain and are less knowledgeable outside it, of course, and we are often asked to comment on a broad range of topics. But many scientists without a track record in the field have become favorites among university administrators, senior faculty in unrelated fields, policymakers, and science journalists, using institutional prestige or social connections to promote themselves. This phenomenon leads to a distorted representation of science—and of academic scientists—in the public realm.
Trustworthy experts will direct you to others in their field who know more about particular topics, and will tend to be honest about what is and what isn't "in their lane."
Predictably, white male voices have been disproportionately amplified, and men are certainly over-represented in the category of those who use their connections to inappropriately claim expertise. Generally speaking, we are missing women, racial minorities, and global perspectives. This is not only important because it misrepresents who scientists are and reinforces outdated stereotypes that place white men in the Global North at the top of a credibility hierarchy. It also matters because it can promote bad science, and it passes over scientists who can lend nuance to the scientific discourse and give global perspectives on this quintessentially global crisis.
Also at work, in my opinion, are two biases within academia: the conflation of institutional prestige with individual expertise, and the bizarre hierarchy among scientists that attributes greater credibility to those in quantitative fields like physics. Regardless of mathematical expertise or institutional affiliation, lack of experience working with epidemiological data can lead to over-confidence in the deceptively simple mathematical models that we use to understand epidemics, as well as the inappropriate use of uncertain data to inform them. Prominent and vocal scientists from different quantitative fields have misapplied the methods of infectious disease epidemiology during the COVID-19 pandemic so far, creating enormous confusion among policymakers and the public. Early forecasts that predicted the epidemic would be over by now, for example, led to a sense that epidemiological models were all unreliable.
Meanwhile, legitimate scientific uncertainties and differences of opinion, as well as fundamentally different epidemic dynamics arising in diverse global contexts and in different demographic groups, appear in the press as an indistinguishable part of this general chaos. This leads many people to question whether the field has anything worthwhile to contribute, and muddies the facts about COVID-19 policies for reducing transmission that most experts agree on, like wearing masks and avoiding large indoor gatherings.
So how do we distinguish an expert from a charlatan? I believe a willingness to say "I don't know" and to openly describe uncertainties, nuances, and limitations of science are all good signs. Thoughtful engagement with questions and new ideas is also an indication of expertise, as opposed to arrogant bluster or a bullish insistence on a particular policy strategy regardless of context (which is almost always an attempt to hide a lack of depth of understanding). Trustworthy experts will direct you to others in their field who know more about particular topics, and will tend to be honest about what is and what isn't "in their lane." For example, some expertise is quite specific to a given subfield: epidemiologists who study non-infectious conditions or nutrition, for example, use different methods from those of infectious disease experts, because they generally don't need to account for the exponential growth that is inherent to a contagion process.
Academic scientists have a specific, technical contribution to make in containing the COVID-19 pandemic and in communicating research findings as they emerge. But the liminal space between scientists and the public is subject to the same undercurrents of sexism, racism, and opportunism that society and the academy have always suffered from. Although none of the proxies for expertise described above are fool-proof, they are at least indicative of integrity and humility—two traits the world is in dire need of at this moment in history.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
A Surprising Breakthrough Will Allow Tiny Implants to Fix—and Even Upgrade—Your Body
The medical implants of the future will prompt lively discussion around the boundaries between treatment and enhancement.
Imagine it's the year 2040 and you're due for your regular health checkup. Time to schedule your next colonoscopy, Pap smear if you're a woman, and prostate screen if you're a man.
"The evolution of the biological ion transistor technology is a game changer."
But wait, you no longer need any of those, since you recently got one of the new biomed implants – a device that integrates seamlessly with body tissues, because of a watershed breakthrough that happened in the early 2020s. It's an improved biological transistor driven by electrically charged particles that move in and out of your own cells. Like insulin pumps and cardiac pacemakers, the medical implants of the future will go where they are needed, on or inside the body.
But unlike current implants, biological transistors will have a remarkable range of applications. Currently small enough to fit between a patient's hair follicles, the devices could one day enable correction of problems ranging from damaged heart muscle to failing retinas to deficiencies of hormones and enzymes.
Their usefulness raises the prospect of overcorrection to the point of human enhancement, as in the bionic parts that were imagined on the ABC television series The Six Million Dollar Man, which aired in the 1970s.
"The evolution of the biological ion transistor technology is a game changer," says Zoltan Istvan, who ran as a U.S. Presidential candidate in 2016 for the Transhumanist Party and later ran for California governor. Istvan envisions humans becoming faster, stronger, and increasingly more capable by way of technological innovations, especially in the biotechnology realm. "It's a big step forward on how we can improve and upgrade the human body."
How It Works
The new transistors are more like the soft, organic machines that biology has evolved than like traditional transistors built of semiconductors and metal, according to electric engineering expert Dion Khodagholy, one of the leaders of the team at Columbia University that developed the technology.
The key to the advance, notes Khodagholy, is that the transistors will interface seamlessly with tissue, because the electricity will be of the biological type -- transmitted via the flow of ions through liquid, rather than electrons through metal. This will boost the sensitivity of detection and decoding of biological change.
Naturally, such a paradigm change in the world of medical devices raises potential societal and ethical dilemmas.
Known as an ion-gated transistor (IGT), the new class of technology effectively melds electronics with molecules of human skin. That's the current prototype, but ultimately, biological devices will be able to go anywhere in the body. "IGT-based devices hold great promise for development of fully implantable bioelectronic devices that can address key clinical issues for patients with neuropsychiatric disease," says Khodagholy, based on the expectation that future devices could fuse with, measure, and modulate cells of the human nervous system.
Ethical Implications
Naturally, such a paradigm change in the world of medical devices raises potential societal and ethical dilemmas, starting with who receives the new technology and who pays for it. But, according clinical ethicist and health care attorney David Hoffman, we can gain insight from past experience, such as how society reacted to the invention of kidney dialysis in the mid 20th century.
"Kidney dialysis has been federally funded for all these decades, largely because the who-gets-the-technology question was an issue when the technology entered clinical medicine," says Hoffman, who teaches bioethics at Columbia's College of Physicians and Surgeons as well as at the law school and medical school of Yeshiva University. Just as dialysis became a necessity for many patients, he suggests that the emerging bio-transistors may also become critical life-sustaining devices, prompting discussions about federal coverage.
But unlike dialysis, biological transistors could allow some users to become "better than well," making it more similar to medication for ADHD (attention deficit hyperactivity disorder): People who don't require it can still use it to improve their baseline normal functioning. This raises the classic question: Should society draw a line between treatment and enhancement? And who gets to decide the answer?
If it's strictly a medical use of the technology, should everyone who needs it get to use it, regardless of ability to pay, relying on federal or private insurance coverage? On the other hand, if it's used voluntarily for enhancement, should that option also be available to everyone -- but at an upfront cost?
From a transhumanist viewpoint, getting wrapped up with concerns about the evolution of devices from therapy to enhancement is not worth the trouble.
It seems safe to say that some lively debates and growing pains are on the horizon.
"Even if [the biological ion transistor] is developed only for medical devices that compensate for losses and deficiencies similar to that of a cardiac pacemaker, it will be hard to stop its eventual evolution from compensation to enhancement," says Istvan. "If you use it in a bionic eye to restore vision to the blind, how do you draw the line between replacement of normal function and provision of enhanced function? Do you pass a law placing limits on visual capabilities of a synthetic eye? Transhumanists would oppose such laws, and any restrictions in one country or another would allow another country to gain an advantage by creating their own real-life super human cyborg citizens."
In the same breath though, Istvan admits that biotechnology on a bionic scale is bound to complicate a range of international phenomena, from economic growth and military confrontations to sporting events like the Olympic Games.
The technology is already here, and it's just a matter of time before we see clinically viable, implantable devices. As for how society will react, it seems safe to say that some lively debates and growing pains are on the horizon.
Clever Firm Predicts Patients Most at Risk, Then Tries to Intervene Before They Get Sicker
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."