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Year: 2017

Five Radiology Artificial Intelligence Companies That Somebody Should Build and Invest In

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By HUGH HARVEY

I’ve previously written comprehensively on where to invest in Radiology AI, and how to beat the hype curve precipice the field is entering. For those that haven’t read my previous blog, my one line summary is essentially this:

“Choose companies with a narrow focus on clinically valid use cases with large data sets, who are engaged with regulations and haven’t over-hyped themselves …”

The problem is… hardly any investment opportunities in Radiology AI like this actually exist, especially in the UK. I thought it’s about time I wrote down my ideas for what I’d actually build (if I had the funding), or what companies I would advise VC’s to invest in (if they existed).

Surprisingly, none of the companies actually interpret medical images – I’ll explain why at the end!

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Orbiting ORBITA

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By ANISH KOKA, MD

I’m sitting amidst a number of cardiologists to go over the most recent trials presented at the interventional cardiology conference in Denver.  The cardiology fellow presenting goes quickly through the hors de oeuvres until finally getting to the main course – ORBITA.

ORBITA sought to test the very foundations interventional cardiology was built on – the simple idea that opening a stenosed coronary artery was good for patients.  The trial was a double blind randomized control trial of patients with tightly stenosed arteries who either had a stent placed or had a sham procedure.  Before the results are presented, the lay media headlines from cardiobrief, the New York Times, and the Atlantic are presented to guffaws from the audience.  The indignant smirks are audible as the accompanying editorial remarks from Rita Redberg and David Brown are displayed :

”The results of ORBITA show unequivocally that there are no benefits for PCI compared with medical therapy for stable angina, even when angina is refractory to medical therapy.”

The trial results follow – no statistically significant difference in the primary outcome of exercise time increment between sham and stent, and no difference in angina between the two groups.  The meat of the presentation involves the limitations of the trial that make the trial inapplicable – 200 patients total, 6 week follow up, the underlying heterogeneity of the patient angiograms that were randomized, and the wide confidence intervals of the primary outcome that swallowed the actual effect size.  Two different angiograms were shown to the audience from the ORBITA appendix.

The images demonstrate two ‘blockages’.  To the eye, at least, one appears tighter than the other.  The audience was polled on each image – everyone voted to stent the tighter blockage and medically manage the lesser of the blockages.  It could be all perception but I could feel the relief in the room as ORBITA was being made irrelevant.  The implication clearly was that some angiograms used to show the lack of benefit from stents would not have needed stenting in the first place.

There was no real challenge to the presenter save for one:

“One of the authors – Rita Redberg – is very sharp – why do you think she wrote that editorial?”

There was no good answer – the presenter shrugged and muttered something about an anti-interventional cardiology bias.

It was at that moment that I realized why cardiologists were having such trouble with  ORBITA – we were arguing like puritans.   Everyone in the room already ‘knew’ stents worked.  This was an exercise in bias confirmation when what was needed was an examination of the source of bias.  Faced with the ultimate epistemological challenge we were resorting to authority to dismiss findings we didn’t like.  Now I think cardiologists have authority with good reason, and certainly ORBITA may have limitations inherent in any small randomized control trial that’s performed, but we can do a better job answering the fundamental question raised here that relates to the primary evidence opening a narrowed artery actually can relieve angina.

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Could Artificial Intelligence Destroy Radiology by Litigation Claims?

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By HUGH HARVEY, MD

We’ve all heard the big philosophical arguments and debate between rockstar entrepreneurs and genius academics – but have we stopped to think exactly how the AI revolution will play out on our own turf?

At RSNA this year I posed the same question to everyone I spoke to: What if radiology AI gets into the wrong hands? Judging by the way the crowds voted with their feet by packing out every lecture on AI, radiologists would certainly seem to be very aware of the looming seismic shift in the profession – but I wanted to know if anyone was considering the potential side effects, the unintended consequences of unleashing such a disruptive technology into the clinical realm?

While I’m very excited about the prospect and potential of algorithmic augmentation in radiological practice, I’m also a little nervous about more malevolent parties using it for predatory financial gains.

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The Evolution of Precision Health

HEALTH 2.o WIDGET

Health 2.0 sat down with Linda Molnar to discuss the evolution of Precision Health, the imperatives at stake in a fast-paced field, and empowerment through big data. Linda has over 20 years in the field of Life Sciences and is responsible for a number of initiatives that further the field with start-ups, the feds, and for investors.

Her current endeavor is leading the upcoming Technology for Precision Health Summit in San Francisco alongside Health 2.0. “We’re never going to pull together all of this disparate data from disparate sources in a meaningful (i.e. clinically actionable) way, unless we talk about it” she says. “The Summit is an attempt to bring together the worlds of Precision Medicine and Digital Healthcare to realize the full potential of a predictive and proactive approach to maintaining health”.

Check out the full interview here.
As a bonus, save 25% off the standard admission to the Technology for Precision Health Summit by using discount code TPH25. Register here!

How the Republican Tax Cuts Will Impact the Health Care System

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By STEVEN FINDLAY

The U.S. tax system and health care are deeply intertwined. The Republican tax bills hurtling through Congress would make significant changes in this relationship.

The proposed changes, primarily a large cut in the corporate tax rate from 35 to 20 percent, would benefit health care (and most other) companies.

But none of the changes would, in the long run, benefit consumers, the public good, or public health. The major components of the proposed legislation are dangerously ill-conceived and ill-timed in the context of the overall economy and in particular health care policy and spending, which is projected to comprise 20 percent of the nation’s economy in 2025, up from 18.3 percent today.

That’s a difference and increase that reflects several trillion dollars of “additional” health care spending over the next decade. Amid this projected rise, the Trump administration and congressional Republicans propose to reduce the rate of growth of overall federal government spending and shift a sizable portion of health spending to other government entities and programs. These include the Pentagon, national security, homeland security, infrastructure projects, and—most notable in the context of the tax bills—a tax cut for corporations and upper income Americans.

It doesn’t and won’t add up—unless two (unlikely) things happen: (1) the economy grows at twice to three times the rate most economists predict and (2) the rate of growth in health spending is dramatically constrained.

Absent both, the Republican tax bills will cause the annual federal budget deficit and the nation’s long-term debt to balloon even more than already forecast.

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On THCB

By THE EDITORS

Valuing Value-Based Payment
By ANISH KOKA, MD

Why “Precision Health” May Not Be the Precise Word

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By DAVID SHAYWITZ, MD

The appeal of precision medicine is the promise that we can understand disease with greater specificity and fashion treatments that are more individualized and more effective.

A core tenet (or “central dogma,” as I wrote in 2015) of precision medicine is the idea that large disease categories – like type 2 diabetes – actually consist of multiple discernable subtypes, each with its own distinct characteristics and genetic drivers. As genetic and phenotypic research advances, the argument goes, diseases like “type 2 diabetes” will go the way of quaint descriptive diagnoses like “dropsy” (edema) and be replaced by more precisely defined subgroups, each ideally associated with a distinct therapy developed for that population.

In 2015, this represented an intuitively appealing idea in search of robust supporting data (at least outside oncology).

In 2017, this represents an intuitively appealing idea in search of robust supporting data (at least outside oncology).

The gap between theory and data has troubled many researchers, and earlier this year, a pair of cardiologists from the Massachusetts General Hospital (MGH) and the Broad Institute, Sek Kathiresan and Amit V. Khera, wrote an important – and I’d suggest underappreciated – commentary in the journal Circulation that examined this very disconnect, through the lens of coronary artery disease (CAD).

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Practicing Medicine While Black
(Part II)

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By KIP SULLIVAN

Managed care advocates see quality problems everywhere and resource shortages nowhere. If the Leapfrog Group, the Medicare Payment Advisory Commission, or some other managed care advocate were in charge of explaining why a high school football team lost to the New England Patriots, their explanation would be “poor quality.”

If a man armed with a knife lost a fight to a man with a gun, ditto: “Poor quality.” And their solution would be more measurement of the “quality,” followed by punishment of the losers for getting low grades on the “quality” report card and rewards for the winners. The obvious problem – a mismatch in resources – and the damage done to the losers by punishing them would be studiously ignored.

This widespread, willful blindness to the role that resource disparities play in creating ethnic and income disparities and other problems, and the concomitant widespread belief that all defects in the US health care system are due to insufficient “quality,” is difficult to explain. I will attempt to lay out the rudiments of an explanation in this essay.

In my first article in this two-part series, I presented evidence demonstrating that “pay-for-performance” (P4P) and “value-base purchasing” (VBP) (rewarding and punishing providers based on crude measures of cost and quality) punish providers who treat a disproportionate share of the poor and the sick.

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Who Owns Your FitBit Data? Biometric Data Privacy Problems

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By JASON CHUNG

The following blog post is adapted from a talk the author gave at the “Data Privacy in the Digital Age” symposium on October 26th sponsored by the U.S. Department of Health and Human Services.

Today, I’ll be focusing on the data privacy issues posed by sports wearables, which I define to include both elite systems such as WHOOP or Catapult and more consumer-oriented products such as Fitbits, and why the U.S. needs an integrated federal regulatory framework to address the privacy challenges posed by private entities commercializing biometric data.

Sports wearables have evolved from mere pedometers to devices that monitor heart rate and sleep, tell athletes how to maximize recovery, and even track food intake and sexual activity – all uploaded to the cloud.

These technologies are now ubiquitous and have wide appeal to consumers – in fact, I’m wearing a Fitbit right now.

But these devices raise several key problems for consumers that are not yet being adequately addressed by the U.S. legal and regulatory system.

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The P.A. Problem: Who You See and What You Get

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By NIRAN AL-AGBA, MD

Recently, the New York Times published an article on excessive costs incurred by mid-level providers over-treating benign skin lesions. According to the piece, more than 15% of biopsies billed to Medicare in 2015 were done by unsupervised PA’s or Nurse Practitioners. Physicians across the country are becoming concerned mid-levels working independently without proper specialty training. Dr. Coldiron, a dermatologist, was interviewed by the Times and said, “What’s really going on is these practices…hire a bunch of P.A.’s and nurses and stick them out in clinics on their own. And they’re acting like doctors.”

They are working “like” doctors, yet do not have training equivalent to physicians. As a pediatrician, I have written about a missed diagnosis of an infant by an unscrupulous midlevel provider who embellished his pediatric expertise. This past summer, astute physician colleagues came across an independent physician assistant, Christie Kidd, PA-C, boldly referring to herself as a “dermatologist.” Her receptionist answers the phone by saying “Kidd Dermatology.”

The Doctors, a daytime talk show, accurately referred to Ms. Kidd on a May 7, 2015 segment as a “skin care specialist.” However, beauty magazines are not held to the same high standard; the dailymail.com, a publication in the UK, captioned a picture of “Dr. Christie Kidd”, as the “go-to MD practicing in Beverly Hills.”

The article shared how Ms. Kidd treats the Kardashian-Jenner family, “helping them to look luminous in their no-make-up selfies.”

While most of us cannot grasp the distress caused by not appearing luminous in no-makeup-selfies, this is significantly concerning for Kendall Jenner. At the tender age of 21, she inaccurately referred to Ms. Kidd as her “life-changing dermatologist.” Cosmopolitan continues the charade, publishing an article on the Jenner family “dermatologist.”

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