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Healthcare ‘quality’ is broken. Here’s how to fix it.

By OWEN TRIPP

For decades, quality in healthcare has been defined on industry terms — not people’s terms. New technology and innovative health plan designs are finally changing that.

People know quality when they see it, and they are definitely not seeing it in healthcare. Fifty-six percent of Americans rate the quality of care as “poor” or “fair,” and 90% believe we’re overpaying for it. Likewise, 80% of employers — collectively the largest purchasers of healthcare in the country — say that higher-quality care is a top priority for their workforce.

And yet, the U.S. healthcare system remains a global leader; a lack of know-how or quality control isn’t the problem. The problem is the wide gap between how the healthcare industry has historically defined quality and how quality is experienced by the people actually receiving and paying for care.

For the past 75 years, healthcare quality has been shaped by a grab bag of federal agencies, accrediting bodies, medical organizations, health insurers, and — more recently — consumer-focused ratings outfits ranging from U.S. News & World Report to Zocdoc. Though many pay lip service to patient experience, none has clearly defined quality — or explained it intuitively enough — to help individuals make smarter healthcare decisions based on their clinical and financial context.

Healthcare needs to move beyond narrow metrics and top doc lists to create a dynamic, value-driven view of quality that consistently connects people to the best care for them, where and when they need it — and ideally, even before they know they need it. Too often, “quality” equates to some numbers on a dashboard, when it needs to be more like a combination of GPS and driver-assist technology: guiding people to their health goals, keeping them in the highest-quality lane, and nudging them if they start to drift.

This was always the vision (for some of us). But we simply haven’t had the right mix of technology and system-wide connectivity to bring it to life. Now we do.

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Officers Eat Last

By KIM BELLARD

A New York Times interview with Rep. Jake Auchincloss (D – Mass) by Bret Stephens caught my attention. I am somewhat familiar with Mr. Stephens from his various pieces in NYT; he is definitely a conservative, but in the old, pre-MAGA sense where it meant you worried about spending but you didn’t hate people who weren’t like you. Rep. Auchincloss, on the other hand, was unfamiliar to me, but the headline of the interview – The Democrat Who Makes Me Listen – proved apt.

For me, the final line the interview summed everything up. Rep. Auchincloss is a Marine veteran, having served in Afghanistan. Mr. Stephens asked: “Final question. If there is one thing you learned in the Marine Corps which every American should know, what is it?” Rep. Auchincloss’s reply was succinct, to the point, and highly instructive: “Officers eat last.”

“Officers eat last” – wow. That’s a philosophy I can buy into. That’s a credo I hope I can live up to. That’s a slogan for a political movement I could get behind.

Of course, I’m not just talking about literally only Marine officers, and I’m not just talking about eating. I’m sure Rep. Auchincloss intended that it was a life lesson that should be applied broadly. I.e., people in authority should make sure the people they are responsible for get taken care of before they take care of themselves. I don’t think that attitude is solely responsible for the esteemed Marine esprit de corps, but it’s got to be part of it.

The trouble is, we don’t see much of that attitude in the rest of America. When Congress failed to pass a budget and millions of federal workers went without paychecks, they (and their staffs) kept getting paid. When the White House went slashing various budgets, it didn’t eliminate White House jobs.

If you want to keep your blood pressure under control, don’t even ask how generous the Congressional retirement package is. Suffice it to say that, if you are one of the few workers who still qualify for a defined benefit pension, it is almost certainly less than theirs. Don’t get me started on how members of Congress seem to get richer – a lot richer – while in office, possibly due to insider trading loopholes.

According to Gallup, only 10% of Americans approve of the job Congress is doing, with 86% disapproving, but they don’t care. They get paid anyway, and most House seats aren’t competitive, so most incumbents are in little danger of getting voted out.

This is no “officers eat last.”

It’s not just politicians.

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New Podcast on Primary Care

This is one of those “Coming Soon” announcements. I spent the weekend with the wonderful gang from what I affectionately call Camp Claudia Cult, a group of mostly Californian policy wonks led by Claudia Williams, who these days is at the UC Berkeley School of public health. As you may have noticed I’ve been spending much of my time writing and talking about the notion of Concierge Care for All. Most of the group at the Health Collab wants to blow up/burn down the current system and replace it with a primary care-driven system. And there’s lots of discussion and planning on how to do that. But there is so much happening in innovation in primary care that I thought we need a dedicated channel to discuss it.

Since the sunsetting of the THCB gang, I’ve been doing lots of tech interviews, but not many about how care works. So now I will. If you want to get involved or be interviewed, please email me — Matthew Holt

Don’t Bury The Lead – AI Assisted Measures of Thymic Health Point to a “Fountain of Youth.”

By MIKE MAGEE

In its final summary of the landmark paper in Nature this past month, the authors led with this statement: “This study underscores the highly personalized nature of thymic health and emphasizes the previously unrecognized possible critical role of maintaining thymic health to preserve an agile, adaptive immune response that will accommodate long-term well-being and longevity.”

The articles clinical significance was rapidly rebroadcast by a range of popular science publications like Scientific American. Its March 18th headline read “This overlooked organ may be more vital for longevity than scientists realized.”  Mass General publications trumpeted, “Long Dismissed in Adult Health, the Thymus May Be Critical for Longevity and Cancer Treatment.” And global outlets went a step further with “Once dismissed as biologically obsolete after adolescence, the thymus is now being reclassified as a central regulator of immune aging, with new evidence linking its health to survival, cancer resistance, and how the human body ages itself.”

In their own Abstract, the authors of the Nature publication were somewhat more reserved, and yet the message is still remarkably consequential. They write, “These findings reposition the thymus as a central regulator of immune-mediated ageing and disease susceptibility in adulthood, highlighting its potential as a target for preventive and regenerative strategies to promote healthy ageing and longevity.”

But what intrigued me in the case above was barely mentioned by reviewers so excited by the primary clinical findings. My question was, “How did they measure thymic functionality?” The short answer is, they measured it with the help of an AI deep learning system.

As the authors explained, “In this study, we investigated the impact of thymic functionality, here called thymic health, in adults… For quantification of thymic health, we developed a deep learning system using an independent dataset of 5,674 individuals to determine compositional radiographic characteristics of the thymus as a proxy for its functionality. The system takes a CT scan as input and provides the automatic continuous thymic health estimate as output….We applied the system to prospectively collected data from a total of 27,612 individuals from two cohorts, including 2,581 participants in the FHS and 25,031 participants in the NLST… For outcome analyses, participants were categorized as low, average or high thymic health based on the bottom 25%, middle 50% and top 25% of the population.”

This new methodology to demonstrate different levels of thymic functionality turned out to be groundbreaking when cross-referenced with decades long longitudinal databases. Association with cardiovascular disease and lung cancer; history of smoking, obesity, and high HDL levels; disabilities, morbidity and mortality; sex and age all reinforced that prolonged functionality of the thymus correlated with both health and longevity.

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Ben Salter, SafeRide Health

Ben Salter is the Chief Product Officer of SafeRide Health. They are one of the biggest players in non-emergency medical transport (NEMT) working predominantly for health plans that have Medicaid or Medicare Advantage members. Getting people to their appointments is a big deal–think disabled or sick people who will miss clinical appointments if they can’t get there. Ben showed a demo of how their system works, what it looks like for call center staff or members using it directly, and how that then actually gets someone a ride to where they need to go–Matthew Holt

CRUSHing Lab Fraud: Three Myths that Derail Real Reform

Leeza Osipenko
Ekaterina Cleary
Julie Egginton

By JULIE EGGINGTON, EKATERINA CLEARY & LEEZA OSIPENKO

When CMS issued its Request for Information under the Comprehensive Regulations to Uncover Suspicious Healthcare (CRUSH) initiative in February, it zeroed in on a long-festering problem: fraud, waste, and abuse in laboratory testing, especially in genetic and molecular diagnostics.

The laboratory industry will respond. And when it does, its arguments will sound polished, familiar, and deeply reassuring. They will also be either disingenuous or unproven.

If policymakers want this effort to succeed, they should be prepared to handle three claims that have long shielded problematic practices from meaningful oversight.

Claim 1: Fraud, waste and abuse is limited to a few bad actors

Expect labs to argue that fraud, waste, and abuse is rare, isolated, and already addressed through enforcement actions. The narrative will feature a handful of egregious cases, presented as outliers in an otherwise trustworthy ecosystem.

But the problem is not a few rotten apples. It is the orchard’s design.

Take “code stacking” for example, in which laboratories bill multiple individual genetic test codes rather than a single panel code, often inflating reimbursement. In one analysis, laboratories used between 1 and 12 billing codes for hereditary cancer panels with the same indications for testing, with estimated average charges ranging from $679 to $8,589 for ostensibly comparable tests. The repetition of these behaviors across companies suggests systemic incentives, not isolated misconduct.

Ample Medicare billing data, whistleblower cases, and Department of Justice settlements point to patterns, not anomalies: high-volume genetic panels ordered with little clinical justification, molecular pathology tests billed under grab bag and overly permissive billing codes, and aggressive marketing and patient harvesting practices targeting vulnerable populations.

A key driver is opacity. Many laboratory-developed tests (LDTs) are marketed under similar or identical names despite meaningful differences in design, accuracy, and intended use. To a clinician or payer, they appear interchangeable. In reality, they are not.

This naming ambiguity allows lower-quality tests to ride the coattails of better-validated ones, while still commanding reimbursement. Fraud, in this context, is not always a dramatic act. It is often embedded in routine billing.

Claim 2: Precision medicine advanced by genetics is worth the cost due to improved patient outcomes

The second argument will appeal to aspiration. Labs will emphasize that genetic testing is the backbone of precision medicine and therefore a worthwhile investment for CMS, despite the ballooning costs.

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Bribery, Corruption and the American Health Care Way

By MATTHEW HOLT

These days you just have to wonder about the greed and corruption that is going on all around. Senator Dick Blumenthal is one of many who’ve been pointing out the naked corruption in the Trump family–Qatari jets, memecoins, Trump’s son being on the board of so many defense and prediction market companies you can’t keep it straight. Issac Saul has tried to detail it all, but reading just the cryptocurrency part of his piece has me spinning. And we’re nowhere near assessing the naked corruption of so many others in the administration. Kristi Noem, despite being fired, is still living in her government house, and has not had to answer for routing some of a totally unnecessary $220m ad campaign to a company that her friends own. The company was incidentally established a whole 8 days before it got the contract.

So it’s a little absurd to be worrying about fraud and corruption in health care. But apparently HHS is. At least Oz and RFK Jr are going on about Somalis defrauding Medicaid and Armenians running fake hospices in California. (Let’s not even consider the optics of a Turkish citizen with close ties to the Erdogan regime criticizing Armenians–I mean the genocide was over a century ago!)

But of course, fraud and corruption in health care has been going on forever. Back in 2011 a Florida man was convicted of Medicare fraud to the tune of tens of millions and got a 50 year sentence. Don’t be surprised that Trump commuted his sentence. And that’s just one of thousands and thousands of cases, mostly by providers inventing fake patients to defraud Medicare or Medicaid.

But the ones who get convicted and go to jail are the amateurs.

If you’re a big company in health care, you fight with lawyers and you settle. For example, every big pharma company has settled for things like off-label promotion of their drugs. GSK paid $3bn, Pfizer over $2bn, J&J over $2bn. In fact back in the 2000s THCB had a regular correspondent called The Industry Veteran who basically suggested that whistleblowing in qui tam suits inside big pharma was the way to wealth and fame.  And of course HCA in its days when it was run by Rick Scott – now (somehow not a) convicted felon as well as Florida senator – settled for $1.7bn. This was all back in the 1990s and early 2000s, but it’s all still going on.

The venue though may have moved. Risk adjustment in Medicare Advantage has become one of the biggest venues for fraud. The key here is that the DOJ and HHS found that while Medicare Advantage plans were upcoding their patients, and therefore getting paid more for them, they weren’t actually delivering more services.

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Come help save democracy!

Tuesday, May 5 | 8-9 pm ET, 5-6 pm PT 

Protecting Healthcare and Our Democracy: A conversation with Mandy Cohen, Former Director, Centers for Disease Control and Prevention PLUS former NC Governor Roy Cooper & Dr. Donald Berwick, IHI founder/former president

REGISTER & DONATE

A matching fund of $70,000 is in effect for this event!

This supports the Movement Voter Project which makes grassroot investments to organize to support democracy at the local level.

I’m a co-host–Matthew Holt

Bevey Miner, Consensus Cloud Solutions

This is a transcript of my HIMSS interview with Bevey Miner, EVP Healthcare Strategy & Policy at Consensus Cloud Solutions. Usually I’d show the video but in this case my fancy new microphone didn’t work so you’d only hear a one sided conversation. Luckily Youtube’s transcript somewhat came to the rescue–Matthew Holt

Matthew: Another THCB Spotlight, I am here with Bevey Minor who a year ago I interviewed as Consensus Cloud Solutions and now your sign says eFax. So, what the hell happened?

Bevey: Interesting question, Matthew. The company is Consensus Cloud Solutions. And the company’s always been Consensus Cloud Solutions since we spun off and went public ourselves. You’ll notice at our booth we’ve got the eFax brand — it’s eFax by Consensus Cloud Solutions. The reason we are showing up as eFax is because this year at HIMSS we really wanted to set the record straight: digital cloud faxing is not the problem with interoperability. Paper faxes are, but digital cloud faxing is not the problem.

The problem is all this unstructured data — all the unstructured data that happens with faxes, with scanned images, with TIFF images. All that unstructured data can’t be queried. It can’t be part of TEFCA. You can’t query what you can’t find.

Cloud faxing is send and receive all day long, and we do that very well and have been doing it for 27 years. About three years ago, we introduced an intelligent extraction solution. That solution doesn’t even have to start with the fax, but it allows the “find” piece to actually become the critical thing that we need to do. CMS defines interoperability as send, receive, find, and integrate. Fax technology handles send and receive all day long, but can’t find. So once we introduced a “find and intelligent extraction” solution, we can fire up TEFCA.

I’ve talked to a lot of regulators, including Dr. Thomas Keane and Amy Gleason with the CMS Align networks. You can’t ignore this pile of unstructured data and just assume the industry is going to go magically everything’s on FHIR. We’re all using FHIR because all of this stuff has really important patient information in it.

What we want to solve in the industry is: don’t say we have to axe the digital cloud fax. Let’s axe the paper fax machine. Digital cloud faxing isn’t going away — in fact, it’s growing, especially as we get rural health off of paper fax machines. The next level of maturity is digital cloud faxing. From there, once it’s digital, now you can do all sorts of things with it.

When we introduced electronic health records during meaningful use — I was at Allscripts at the time — our dream was that we would take this paper record and transform it into an electronic health record, so we could just get rid of the paper. Once we did that and there were discrete data elements in that EHR, we could do population health, clinical decision support, efficacy, all sorts of things — because there are discrete data elements now inside that electronic health record. That’s what a digital fax will do with the capability to do intelligence on top of it.

So we want to make the industry understand that the fax is not the problem. Extracting it and getting rid of all that unstructured data is the solution.

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It’s Got a Good Beat and You Can Kill It

By KIM BELLARD

Most of us can identify dogs from cats just by the sounds they make. We could probably even separate a dog’s bark from a wolf’s howl. If you are a nature lover, you might be able to identify different species of birds by their calls.  If you are a cetologist, you might be able to separate the vocalizations whales make versus those dolphins make. Across the animal world, we’ve learned the different sounds that different species make, which has been useful in our survival.

But did you ever wonder if you can identify, say, e coli from other bacteria?

It turns out that you can, thanks to research at Delft University of Technology (TU Delft) in the Netherlands. Four years ago, they showed that bacteria made noise, which was, in itself, a startling finding (admit it: would you have ever guessed that?). They used a thin layer of graphene to create a graphene “drum” small enough to fit a single bacterium. Team member Cees Dekker observed: “What we saw was striking! When a single bacterium adheres to the surface of a graphene drum, it generates random oscillations with amplitudes as low as a few nanometers that we could detect. We could hear the sound of a single bacterium!”

The team used this finding to accomplish an important purpose: to find out if bacteria were resistant to specific antibiotics. If an antibiotic was applied and the sound continued; it hadn’t worked. If the sounds stopped, the bacteria had been killed.

The team wasted no time in creating a start-up – SoundCell – to commercialize the finding. It promised to identify the “right” antibiotic in one hour, rather than subjecting patients to rounds of different antibiotics in search of one the bacteria wasn’t resistant to.

The team isn’t resting on their laurels. Some of them got to wondering, huh, I wonder if different bacteria make different sounds. And, their latest research shows, not only do they but, through machine learning, those different species can be distinguished. Team lead Farbod Alijani says. “With this new study, we take a significant leap forward: we show that each bacterial species has its own nanomotion signature.”

Mind. Blown.

The researchers focused on three bacteria that are common in hospital settings: E. coli, S. aureus (which causes staph infections) and K. pneumoniae (which causes pneumonia). They tested two different machine learning models; one correctly classified the bacteria 87% of the time, and the other 88% of the time.

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