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False Negative: Testing’s Catch-22

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By SAURABH JHA, MD

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.

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Artificial Intelligence vs. Tuberculosis – Part 2

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By SAURABH JHA, MD

This is the part two of a three-part series. Catch up on Part One here.

Clever Hans

Preetham Srinivas, the head of the chest radiograph project in Qure.ai, summoned Bhargava Reddy, Manoj Tadepalli, and Tarun Raj to the meeting room.

“Get ready for an all-nighter, boys,” said Preetham.

Qure’s scientists began investigating the algorithm’s mysteriously high performance on chest radiographs from a new hospital. To recap, the algorithm had an area under the receiver operating characteristic curve (AUC) of 1 – that’s 100 % on multiple-choice question test.

“Someone leaked the paper to AI,” laughed Manoj.

“It’s an engineering college joke,” explained Bhargava. “It means that you saw the questions before the exam. It happens sometimes in India when rich people buy the exam papers.”

Just because you know the questions doesn’t mean you know the answers. And AI wasn’t rich enough to buy the AUC.

The four lads were school friends from Andhra Pradesh. They had all studied computer science at the Indian Institute of Technology (IIT), a freaky improbability given that only hundred out of a million aspiring youths are selected to this most coveted discipline in India’s most coveted institute. They had revised for exams together, pulling all-nighters – in working together, they worked harder and made work more fun.

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Artificial Intelligence vs. Tuberculosis, Part 1

By SAURABH JHA, MD

Slumdog TB

No one knows who gave Rahul Roy tuberculosis. Roy’s charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai’s weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed.

Historically TB, or “consumption” as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy.

TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB’s infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.

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The Rise and Rise of Quantitative Cassandras

By SAURABH JHA, MD

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.

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Radiology Firing Line | Choosing Wisely, Wisely

By SAURABH JHA, MD

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.

Listen to our conversation here.

Saurabh Jha is a contributing editor to THCB and host of Radiology Firing Line Podcast of the Journal of American College of Radiology, sponsored by Healthcare Administrative Partner

HardCore Health Podcast| Episode 3, IPOs, Privacy, & more!

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.

India’s Mob Problem

By SAURABH JHA, MD

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.

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Was the IOM estimate of medical errors correct?

By SAURABH JHA

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.

Saurabh Jha (aka @RogueRad) speaks with Professor Hayward for the Firing Line Podcast about his research in JAMA published in 2001 – Estimating Hospital Deaths Due to Medical Errors: Preventability Is in the Eye of the Reviewer. It was a landmark publication of the time, and its objective methods have stood the test of time.

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.

A Rose by Another Name

By SAURABH JHA, MD

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.

Predictions and Parachutes

By SAURABH JHA, MD

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.

Listen to their conversation here.