Mr. Smith’s pneumonia was clinically shy. He didn’t have a fever. His white blood cells hadn’t increased. The only sign of an infection, other than his cough, was that his lung wasn’t as dark as it should be on the radiograph. The radiologist, taught to see, noticed that the normally crisp border between the heart and the lung was blurred like ink smudged on blotting paper. Something that had colonized the lungs was stopping the x-rays.
Hundred and twenty-five years ago, Wilhelm Conrad Roentgen, a German physicist and the Rector at the University of Wurzburg, made an accidental discovery by seeing something he wasn’t watching. Roentgen was studying cathode rays – invisible forces created by electricity. Using a Crookes tube, a pear-shaped vacuum glass tube with a pair of electrodes, Roentgen would fire the cathode rays from one end by an electric jolt. At the other end, the rays would leave the tube through a small hole, and generate colorful light on striking fluorescent material placed near the tube.
By then photography and fluorescence had captured literary and scientific imagination. In Arthur Conan Doyle’s Hound of the Baskervilles, the fire-breathing dog’s jaw had been drenched in phosphorus by its owner. Electricity and magnetism were the new forces. Physicists were experimenting in the backwaters of the electromagnetic spectrum without knowing where they were.
On November 8th, 1895, when after supper Roentgen went to his laboratory for routine experiments, something else caught Roentgen’s eyes. Roentgen closed the curtains. He wanted his pupils maximally dilated to spot tiny flickers of light. When he turned the voltage on the Crookes tube, he noticed that a paper soaked in barium platinocyanide on a bench nine feet away flickered. Cathode rays traveled only a few centimeters. Also, he had covered the tube with heavy cardboard to stop light. Why then did the paper glow?
In a physician WhatsApp group, a doctor posted he had fever of 101° F and muscle ache, gently confessing that it felt like his typical “man flu” which heals with rest and scotch. Nevertheless, he worried that he had coronavirus. When the reverse transcription polymerase chain reaction (RT-PCR) for the virus on his nasal swab came back negative, he jubilantly announced his relief.
Like Twitter, in WhatsApp emotions quickly outstrip facts. After he received a flurry of cheerful emojis, I ruined the party, advising that despite the negative test he assume he’s infected and quarantine for two weeks, with a bottle of scotch.
It’s conventional wisdom that the secret sauce to fighting the pandemic is testing for the virus. To gauge the breadth of the response against the pandemic we must know who and how many are infected. The depth of the response will be different if 25% of the population is infected than 1%. Testing is the third way, rejecting the false choice between death and economic depression. Without testing, strategy is faith-based.
Our reliance on testing has clinical precedence – scarcely any decision in medicine is made without laboratory tests or imaging. Testing is as ingrained in medicine as the GPS is in driving. We use it even when we know our way home. But tests impose a question – what’ll you do differently if the test is negative?
That depends on the test’s performance and the consequences of being wrong. Though coronavirus damages the lungs with reckless abandon, it’s oddly a shy virus. In many patients, it takes three to four swabs to get a positive RT-PCR. The Chinese ophthalmologist, Li Wenliang, who originally sounded the alarm about coronavirus, had several negative tests. He died from the infection.
Despite an area under the ROC curve of 1, Cassandra’s
prophesies were never believed. She neither hedged nor relied on retrospective
data – her predictions, such as the Trojan war, were prospectively validated. In
medicine, a new type of Cassandra has emerged –
one who speaks in probabilistic tongue, forked unevenly between the
probability of being right and the possibility of being wrong. One who, by conceding
that she may be categorically wrong, is technically never wrong. We call these
new Minervas “predictions.” The Owl of Minerva flies above its denominator.
Deep learning (DL) promises to transform the prediction
industry from a stepping stone for academic promotion and tenure to something
vaguely useful for clinicians at the patient’s bedside. Economists studying AI believe that AI is revolutionary,
revolutionary like the steam engine and the internet, because it better predicts.
Recently published in Nature, a sophisticated DL algorithm was able to predict acute kidney injury (AKI), continuously, in hospitalized patients by extracting data from their electronic health records (EHRs). The algorithm interrogated nearly million EHRS of patients in Veteran Affairs hospitals. As intriguing as their methodology is, it’s less interesting than their results. For every correct prediction of AKI, there were two false positives. The false alarms would have made Cassandra blush, but they’re not bad for prognostic medicine. The DL- generated ROC curve stands head and shoulders above the diagonal representing randomness.
The researchers used a technique called “ablation analysis.”
I have no idea how that works but it sounds clever. Let me make a humble
prophesy of my own – if unleashed at the bedside the AKI-specific, DL-augmented
Cassandra could unleash havoc of a scale one struggles to comprehend.
Leaving aside that the accuracy of algorithms trained
retrospectively falls in the real world – as doctors know, there’s a difference
between book knowledge and practical knowledge – the major problem is the
effect availability of information has on decision making. Prediction is
fundamentally information. Information changes us.
How easy is it for physicians to choose wisely and reject low value care? Who decides what’s wise and what’s unwise? In this episode Saurabh Jha (aka @RogueRad) speaks with William Sullivan MD JD. Dr. Sullivan is an emergency physician and an attorney specializing in healthcare issues. Dr. Sullivan represents physicians and has published many articles on legal aspects of medicine. He is a past president of the Illinois College of Emergency Physicians and a past chair and current member of the American College of Emergency Physicians’ Medical Legal Committee.
On Episode 3 of HardCore Health, Jess & I start off by discussing all of the health tech companies IPOing (Livongo, Phreesia, Health Catalyst) and talk about what that means for the industry as a whole. Zoya Khan discusses the newest series on THCB called, “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?”, which follows & discuss the legislation being passed on data privacy and protection in Congress today. We also have a great interview with Paul Johnson, CEO of Lemonaid Health, an up-and-coming telehealth platform that works as a one-stop-shop for a virtual doctor’s office, a virtual pharmacy, and lab testing for patients accessing their platform. In her WTF Health segment, Jess speaks to Jen Horonjeff, Founder & CEO of Savvy Cooperative, the first patient-owned public benefit co-op that provides an online marketplace for patient insights. And last but not least, Dr. Saurabh Jha directly address AI vendors in health care, stating that their predictive tools are useless and they will not replace doctors just yet- Matthew Holt
Matthew Holt is the founder and publisher of The Health Care Blog and still writes regularly for the site.
Recently, my niece gingerly
confided that she was going to study engineering rather than medicine. I was
certain she’d become a doctor – so deep was her love for biology and her
deference to our family tradition. But she calculated, as would anyone with
common sense, that with an engineering degree and an MBA, she’d be working for
a multinational company making a comfortable income by twenty-eight. If she
stuck with tradition and altruism, as a doctor she’d still be untrained and
preparing for examinations at twenty-eight.
Despite the truism in India that
doctors are the only professionals never at risk of starving, the rational case
for becoming a physician never was strong. Doctors always needed a dose of the
irrational, an assumption of integrity and an unbridled goodwill to keep going.
Once, doctors commanded both the mystery of science and the magic of
metaphysics. As medicine became for-profit, the metaphysics slowly disappeared.
Indians are becoming more
prosperous. They’re also less fatalistic and expect less from their gods and
more from their doctors. In the beginning they treated their doctors as gods, now
they see that doctors have feet of clay, too. Doctors, who once outsourced the
limitations of medicine to the will of Gods, summarized by the famous Bollywood
line “inko dawa ki nahin dua ki zaroorat hai” (patient needs prayers not
drugs), now must internalize medicine’s limitations. And there are many –
medicine is still an imperfect science, a stubborn art, often an optimistic breeze
fighting forlornly against nature’s implacable gale.
In 1999, the Institute of Medicine (IOM) in their landmark report – To Err is Human – estimated that the number of deaths from medical errors is 44 ,000 to 98, 000. The report ushered the Quality and Safety Movement, which became a dominant force in all hospitals. Yet the number of deaths from medical errors climbed. It is now touted to be the 3rd leading cause of death. How easy is it to precisely quantify the number of deaths from medical errors? Not many physicians challenged the methodologies of the IOM report. Some feared that they’d be accused of “making excuses for doctors.” Many simply didn’t have a sufficient grip on statistics of measurement sciences. One exception was Rodney Hayward – who was then an early career researcher, a measurement scientist, who studied how sensitive the estimates of medical errors were to a range of assumptions.
Rod Hayward a Professor of Public Health and Internal Medicine at the University of Michigan and Co-Director of the Center for Practice Management and Outcomes Research at the Ann Arbor VA HSR&D. He received his training in health services research as a Robert Wood Johnson Clinical Scholar at UCLA and at the RAND Corporation, Santa Monica. His current and past work includes studies examining measurement of quality, costs and health status, environmental and educational factors affecting physician practice patterns, quality improvement, and physician decision making. His current work focuses on quality measurement and improvement for chronic diseases, such as diabetes, hypertension and heart disease.
Listen to their conversation on Radiology Firing Line Podcast here.
Can we reduce over diagnosis by re-naming disease to less anxiety-provoking makes? For example, if we call a 4.1 cm ascending aorta “ecstasia” instead of “aneurysm” will there be less over-treatment? In this episode of Radiology Firing Line Podcast, Saurabh Jha (aka @RogueRad) discusses over diagnosis with Ian Amber, a musculoskeletal radiologist at Georgetown University, Washington.
What does it take to create a decision rule? In this episode of Radiology Firing Line podcast Saurabh Jha (@RogueRad) has a discussion with Robert W. Yeh MD MBA about the deep thought and complex statistics involved in creating a decision rule to guide therapy which have narrow risk-benefit calculus, specifically a rule for how long patients should continue dual anti-platelet therapy after percutaneous coronary intervention. They also discuss the motivation behind the legendary, and satirical, parachute RCT published in the recent Christmas edition of the BMJ, which delighted satirists all over the world.
In this episode of Radiology Firing Line Podcast, I speak with Bishal Gyawali MD, PhD. Dr. Gyawali obtained his medical degree from Kathmandu. He received a scholarship to pursue a PhD in Japan. Dr. Gyawali’s work focuses on getting cheap and effective treatment to under developed parts of the world. Dr. Gyawali is an advocate for evidence-based medicine. He has published extensively in many high impact journals. He coined the term “cancer groundshot.” He was a research fellow at PORTAL. He is currently a scientist at the Queen’s University Cancer Research Institute in Kingston, Ontario.