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."
7 Reasons Why We Should Not Need Boosters for COVID-19
There are at least 7 reasons why immunity after vaccination or infection with COVID-19 should likely be long-lived. If durable, I do not think boosters will be necessary in the future, despite CEOs of pharmaceutical companies (who stand to profit from boosters) messaging that they may and readying such boosters. To explain these reasons, let's orient ourselves to the main components of the immune system.
There are two major arms of the immune system: B cells (which produce antibodies) and T cells (which are formed specifically to attack and kill pathogens). T cells are divided into two types, CD4 cells ("helper" T cells) and CD8 cells ("cytotoxic" T cells).
Each arm, once stimulated by infection or vaccine, should hopefully make "memory" banks. So if the body sees the pathogen in the future, these defenses should come roaring back to attack the virus and protect you from getting sick. Plenty of research in COVID-19 indicates a likely long-lasting response to the vaccine or infection. Here are seven of the most compelling reasons:
REASON 1: Memory B Cells Are Produced By Vaccines and Natural Infection
In one study, 12 volunteers who had never had Covid-19--and were fully vaccinated with two Pfizer/BioNTech shots-- underwent biopsies of their lymph nodes. This is where memory B cells are stored in places called "germinal centers". The biopsies were performed three, four, six, and seven weeks after the first mRNA vaccine shot, and were stained to reveal that germinal center memory B cells in the lymph nodes increased in concentration over time.
Natural infection also generates memory B cells. Even after antibody levels wane over time, strong memory B cells were detected in the blood of individuals six and eight months after infection in different studies. Indeed, the half-lives of the memory B cells seen in the study examining patients 8 months after COVID-19 led the authors to conclude that "B cell memory to SARS-CoV-2 was robust and is likely long-lasting." Reason #2 tells us that memory B cells can be active for a very long time indeed.
REASON #2: Memory B Cells Can Produce Neutralizing Antibodies If They See Infection Again Decades Later
Demonstrated production of memory B cells after vaccination or natural infection with COVID-19 is so important because memory B cells, once generated, can be activated to produce high levels of neutralizing antibodies against the pathogen even if encountered many years after the initial exposure. In one amazing study (published in 2008), researchers isolated memory B cells against the 1918 flu strain from the blood of 32 individuals aged 91-101 years. These people had been born on or before 1915 and had survived that pandemic.
Their memory B cells, when exposed to the 1918 flu strain in a test tube, generated high levels of neutralizing antibodies against the virus -- antibodies that then protected mice from lethal infection with this deadly strain. The ability of memory B cells to produce complex antibody responses against an infection nine decades after exposure speaks to their durability.
REASON #3: Vaccines or Natural Infection Trigger Strong Memory T Cell Immunity
All of the trials of the major COVID-19 vaccine candidates measured strong T cell immunity following vaccination, most often assessed by measuring SARS-CoV-2 specific T cells in the phase I/II safety and immunogenicity studies. There are a number of studies that demonstrate the production of strong T cell immunity to COVID-19 after natural infection as well, even when the infection was mild or asymptomatic.
The same study that showed us robust memory B cell production 8 months after natural infection also demonstrated strong and sustained memory T cell production. In fact, the half-lives of the memory T cells in this cohort were long (~125-225 days for CD8+ and ~94-153 days for CD4+ T cells), comparable to the 123-day half-life observed for memory CD8+ T cells after yellow fever immunization (a vaccine usually given once over a lifetime).
A recent study of individuals recovered from COVID-19 show that the initial T cells generated by natural infection mature and differentiate over time into memory T cells that will be "put in the bank" for sustained periods.
REASON #4: T Cell Immunity Following Vaccinations for Other Infections Is Long-Lasting
Last year, we were fortunate to be able to measure how T cell immunity is generated by COVID-19 vaccines, which was not possible in earlier eras when vaccine trials were done for other infections (such as measles, mumps, rubella, pertussis, diphtheria). Antibodies are just the "tip of the iceberg" when assessing the response to vaccination, but were the only arm of the immune response that could be measured following vaccination in the past.
Measuring pathogen-specific T cell responses takes sophisticated technology. However, T cell responses, when assessed years after vaccination for other pathogens, has been shown to be long-lasting. For example, in one study of 56 volunteers who had undergone measles vaccination when they were much younger, strong CD8 and CD4 cell responses to vaccination could be detected up to 34 years later.
REASON #5: T Cell Immunity to Related Coronaviruses That Caused Severe Disease is Long-Lasting
SARS-CoV-2 is a coronavirus that causes severe disease, unlike coronaviruses that cause the common cold. Two other coronaviruses in the recent past caused severe disease, specifically Severely Acute Respiratory Distress Syndrome (SARS) in late 2002-2003 and Middle East Respiratory Syndrome (MERS) in 2011.
A study performed in 2020 demonstrated that the blood of 23 recovered SARS patients possess long-lasting memory T cells that were still reactive to SARS 17 years after the outbreak in 2003. Many scientists expect that T cell immunity to SARS-CoV-2 will be equally durable to that of its cousin.
REASON #6: T Cell Responses from Vaccination and Natural Infection With the Ancestral Strain of COVID-19 Are Robust Against Variants
Even though antibody responses from vaccination may be slightly lower against various COVID-19 variants of concern that have emerged in recent months, T cell immunity after vaccination has been shown to be unperturbed by mutations in the spike protein (in the variants). For instance, T cell responses after mRNA vaccines maintained strong activity against different variants (including P.1 Brazil variant, B.1.1.7 UK variant, B.1.351 South Africa variant and the CA.20.C California variant) in a recent study.
Another study showed that the vaccines generated robust T cell immunity that was unfazed by different variants, including B.1.351 and B.1.1.7. The CD4 and CD8 responses generated after natural infection are equally robust, showing activity against multiple "epitopes" (little segments) of the spike protein of the virus. For instance, CD8 cells responds to 52 epitopes and CD4 cells respond to 57 epitopes across the spike protein, so that a few mutations in the variants cannot knock out such a robust and in-breadth T cell response. Indeed, a recent paper showed that mRNA vaccines were 97.4 percent effective against severe COVID-19 disease in Qatar, even when the majority of circulating virus there was from variants of concern (B.1.351 and B.1.1.7).
REASON #7: Coronaviruses Don't Mutate Quickly Like Influenza, Which Requires Annual Booster Shots
Coronaviruses are RNA viruses, like influenza and HIV (which is actually a retrovirus), but do not mutate as quickly as either one. The reason that coronaviruses don't mutate very rapidly is that their replicating mechanism (polymerase) has a strong proofreading mechanism: If the virus mutates, it usually goes back and self-corrects. Mutations can arise with high rates of replication when transmission is very frequent -- as has been seen in recent months with the emergence of SARS-CoV-2 variants during surges. However, the COVID-19 virus will not be mutating like this when we tamp down transmission with mass vaccination.
In conclusion, I and many of my infectious disease colleagues expect the immunity from natural infection or vaccination to COVID-19 to be durable. Let's put discussion of boosters aside and work hard on global vaccine equity and distribution since the pandemic is not over until it is over for us all.
The "Making Sense of Science" podcast features interviews with leading medical and scientific experts about the latest developments and the big ethical and societal questions they raise. This monthly podcast is hosted by journalist Kira Peikoff, founding editor of the award-winning science outlet Leaps.org.
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Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.