Notes on Engineering Health, November 2021: Surprise Billing: A Personal Story

Jonathan Friedlander, PhD

Jonathan Friedlander, PhD

November 30, 2021

Accounts of absurd or tragic interactions with the American healthcare system are common. A growing sub-genre of these stories involves surprise billing—many such overwhelming experiences have been recounted from trips to the ER to the result of routine medical testing. Today, on the eve of Thanksgiving, I am sharing a personal story that encapsulates some of the most infuriating idiosyncrasies of our current American system.

My wife and I have a healthy two-and-a-half-year-old son. My wife is now pregnant again and followed by a competent team of MDs and midwives at our local hospital. Being over 35 years old, she was advised not only to take a cell-free fetal DNA test to make sure there were no chromosomal abnormalities in the fetus, but also to be screened for some 274 genetic conditions that could be passed on to the baby. Because she tested positive for a potentially risky allele, her doctor recommended that I take a similar screening test to make sure that I did not carry the same variation, which thankfully I did not.

All seemed well, but did not end. A few weeks after my test, the lab that conducted the screening billed me for a little over $1,200. To ascertain how they reached this amount, I visited my insurer’s website. There, I learned that the screening company initially listed the price of the test as over $12,000, for a test most akin to the type of analysis a company like 23&me performs for $100. My gallant insurers negotiated down to a price of about $2,300 for the test, of which they agreed to cover nearly $1,100, leaving me to pay the rest. There is no documentation for how our insurance cut down the list price by more than 80%, but such negotiated rates are common practice. These kinds of backdoor negotiations over billing have attracted bipartisan political attention and Congress has recently pushed for more transparency.

To learn more about the American medical insurance system, I called my insurer and spoke to a polite and extremely helpful representative. She, somewhat confused by my story, first explained that they processed it as an out-of-network expense—although recommended by an in-network hospital with a blood draw performed by an in-network provider. She then revealed that the reason my bill was so high was because the test was performed out-of-state. To be honest, living in Cambridge, MA—arguably the world capital of the biotech industry—I was surprised that a seemingly simple test had to be sent to California for processing and that there were no alternative services that could have been done locally which would have prevented me from having to pay anything at all. The insurance representative finally advised me to always inquire about who performs any test to avoid surprise billings in the future.

There are a lot of disconcerting aspects to this story: the seemingly arbitrary pricing of these procedures, the lack of transparency on what is and is not considered in-network, the lack of choice (sometimes touted as the supreme attribute of any healthcare system), and the onus put on the patients to question procedures prescribed by trusted medical staff. Not only should patients—often under medical deadlines of their own—make minute inquiries about whether in-network testing can turn into out-of-network testing, but apparently they should also inquire about the final laboratory destinations of their samples! However, the most significant aspect of this story is probably how utterly commonplace  it is. Although this story is specific to higher-risk pregnancies, surprise billing affects nearly everyone, especially people with less expansive coverage. Although some startups are trying (sometimes successfully) to reduce surprise billing, fixing the issue has proven incredibly difficult for private companies. There seems to be some political will to eliminate such a complex problem, including laws protecting patients, but pushback from providers is fierce and the perfect solution is still elusive.

Jonathan Friedlander, PhD

First Five
First Five is our list of essential media for the month which spans a range of content including scientific papers, books, podcasts, and videos. For our full list of interesting media in health, science, and technology, updated regularly, follow us on Twitter or Instagram.

1/ Treading Water
While the pandemic has had many people feeling like they were treading water to stay afloat, recent research has found that to actually (rather than metaphorically) avoid drowning the so-called “egg-beater” technique is the most economic, both energetically and cognitively. Watch here to learn this potentially life-saving technique.

2/ Re-purposing
Surprisingly often, drugs developed for one condition can be effectively repurposed for an entirely different indication. In a recent example of this phenomenon, researchers at Johns Hopkins have found that a drug first developed to treat Alzheimer's disease, schizophrenia and sickle cell disease reduces obesity and fatty liver in mice and improves their heart function -- without changes in food intake or daily activity. Oddly enough, the proposed mechanism involves an enzyme that is a “cousin” to the protein blocked by drugs such as Viagra.

3/ Antiaging Diets: Separating Fact From Fiction
Science recently published a review article that attempts to reconcile the effects of various diets proposed to delay or reverse aging. The paper in particular interrogates the effects of diets involving various forms of caloric restriction, fasting, and ketosis. It likely will not come as a surprise that the authors conclude, “Despite mainstream popularization of some of these diets, many questions remain about their efficacy outside of a laboratory setting.”

4/ Food Safety, or Not
This recent piece from Pro Publica may have you think twice about your next serving of crispy fried chicken and to continue fasting instead.

5/ Sleeping With The Fishes, or Not
As movies buffs may recall from The Godfather, “Luca Brasi sleeps with the fishes.” If one avoids getting whacked by the Mob (or eating a bad chicken sandwich), do we have any indication of what else could lead to a long life? A paper in Science indicates that paying attention to the fishes may be good idea:

Fish have wide variations in life span even within closely related species. One such example is the rockfish species found along North Pacific coasts, which have life spans ranging from 11 to more than 200 years. Kolora et al. sequenced and performed a genomic analysis of 88 rockfish species, including long-read sequencing of the genomes of six species (see the Perspective by Lu et al.). From this analysis, the authors unmasked the genetic drivers of longevity evolution, including immunity and DNA repair-related pathways. Copy number expansion in the butyrophilin gene family was shown to be positively associated with life span, and population historical dynamics and life histories correlated differently between long- and short-lived species. These results support the idea that inflammation may modulate the aging process in these fish.

Digitalis Commons
Public-Interest Technologies for Better Health

Digitalis Commons is a non-profit that partners with groups and individuals striving to address complex health problems by building public-interest technology solutions that are frontier-advancing, open-access, and scalable.

As algorithms continue to run more and more of our lives, important considerations of what this means for society need more attention. A recent Perspective article in Nature takes on the issue of “Measuring  algorithmically infused societies.” The authors write,

It has been the historic responsibility of the social sciences to investigate human societies. Fulfilling this responsibility requires social theories, measurement models and social data. Most existing theories and measurement models in the social sciences were not developed with the deep societal reach of algorithms in mind. The emergence of ‘algorithmically infused societies’—societies whose very fabric is co-shaped by algorithmic and human behaviour—raises three key challenges: the insufficient quality of measurements, the complex consequences of (mis)measurements, and the limits of existing social theories.

It is worth reading the whole piece and thinking about its implications for how we should build public-interest technologies.

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