Against surgical excellence

How inept decision models are targeting surgical practice

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A Vox.com piece about decision-making caught my attention this morning.

The story was compelling.  A 12-year-old boy had intractable seizures from a leaking vascular malformation in the brain.  A first neurosurgeon would not operate and recommended radiation therapy instead.  The patient’s mother sought another opinion from a Mayo Clinic neurosurgeon who was adamant that an operation should be undertaken.  The second surgeon surgeon was undeniably right.  The patient is now a bright, fully-functional researcher at the University of California San Francisco.

So far, so good?  Not so, according to Vox.  That there should be a smart mom making a smart decision, and a smart doctor carrying out a successful surgery is apparently a problem.

Why?  Because the more cautious surgeon had a different opinion and, had the mom compliantly accepted his recommendation, the child could have been worse off.  Variability in judgment, as always, is the enemy.

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Book review: How Doctors Think

By Kathryn Montgomery

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Kathryn Montgomery’s How Doctors Think: Clinical Judgment and the Practice of Medicine is an excellent book that was brought to my attention by Dr. James Gaulte in the comment section of my post on phronesis.  Indeed, much of Montgomery’s monograph deals with the Aristotelian concept of practical wisdom applied to clinical decision-making.

The author is Professor of Medical Humanities and Bioethics and Professor of Medicine at Northwestern University Feinberg School of Medicine.  Her book is too rich to cover deeply in a short review, but I’d like to highlight some of its major strengths as well as a few minor weaknesses.

In the first part of the book, Montgomery thoroughly demolishes the notion that medicine is applied science.

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The devolution of evidence-based medicine

A chronicle of the demise of clinical judgment

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Anish Koka recently wrote a great piece entitled “In Defense of Small Data” that was published on The Health Care Blog.

While many doctors remain enamored with the promise of Big Data or hold their breath in anticipation of the next mega clinical trial, Koka skillfully puts the vagaries of medical progress in their right perspective.  More often than not, Koka notes, big changes come from astute observations by little guys with small data sets.

In times past, an alert clinician would make advances using her powers of observation, her five senses (as well as the common one) and, most importantly, her clinical judgment.  He would produce a case series of his experiences, and others could try to replicate the findings and judge for themselves.

Today, this is no longer the case.  We live in the era of “evidence-based medicine,” or EBM, which began about fifty years ago.  Reflecting on the scientific standards that the medical field has progressively imposed on itself over the last few decades, I can make out that demands for better scientific methodology have ratcheted up four levels:

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Why I don’t “believe in science”

A response to John Mandrola, with reactions to the SPRINT trial as a case in point

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A few days ago, cardiologist and master blogger John Mandrola wrote a piece that caught my attention. More precisely, it was the title of his blog post that grabbed me: “To Believe in Science Is To Believe in Data Sharing.”

Mandrola wrote about a proposal drafted by the International Committee of Medical Journal Editors (ICMJE) that would require authors of clinical research manuscripts to share patient-level data as a condition for publication. The data would be made available to other researchers who could then perform their own analyses, publish their own papers, etc.

The ICMJE proposal is obviously controversial, raising thorny questions about whether “data” are the kinds of things that can be subject to ownership and, if so, whether there are sufficient ethical or utilitarian grounds to demand that data be “forked over,” so to speak, for others to review and analyze.

Now all of that is of great interest, but I’d like to focus attention on the idea that conditions Mandrola’s endorsement of data sharing. And the question I have is this: Should we believe in science?

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The physician as entrepreneur

Warning: not a post about "disruptive innovators"

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Frank Knight, risk and uncertainty

In this article, I wish to introduce the reader to the theory of entrepreneurship advanced by Frank Knight (1885-1972), and show that the common, everyday work of the physician could be considered a form of entrepreneurial activity in the Knightian sense.

Knight was an influential American economist.  He is best known for his book Risk, Uncertainty, and Profit in which he proposed to distinguish risk and uncertainty as follows:

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Phronesis

5 must-read articles about what good medical decisions are all about

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I told you before that I would do my best to avoid bringing up phronesis, but a superb essay by Lisa Rosenbaum, national correspondent at the New England Journal of Medicine, is forcing my hand.

In “The Paternalism Preference — Choosing Unshared Decision Making,”  Rosenbaum calls into question the gradual shifting of the burden of decision-making onto patients in the name of informed consent and autonomy.

The essay begins by examining the issue from the patient’s perspective, but Rosenbaum’s reflection then turns to the role of the physician.  She remarks:

But science cannot answer a question at the core of our professional identities: As information-empowered patients assume greater responsibility for choices, do we assume less?

The answer to that question has to do with our understanding of what constitutes good medical decision-making.  Clearly, the prevailing notion assumes that good medical decisions come after a rational approximation of an objective biological reality, a “predictive analysis” that forms the core of “evidence-based medicine.”

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How experts really decide

(Hint: they don't follow guidelines)

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I opened my last post with a question I never came around to really answer: How should doctors make decisions?

That wasn’t an oversight.  To try to provide an answer seemed daunting, plus I wouldn’t have resisted the urge to wax philosophical about praxeology or phronesis.  And how sexy is that?  Surely my Alexa ranking would have suffered!

Perhaps sensing my predicament, Dr. Saurabh Jha tactfully suggested a book which I have since ordered and read.  (And what a great call that was.  Thank you, @RogueRad!)  The book is Streetlights and Shadows: Searching for the Keys to Adaptive Decision Making by Gary Klein.

According to his Wikipedia entry, Klein is a cognitive psychologist credited with pioneering the field of naturalistic decision-making, a research endeavor where people’s decisions are examined in real life setting, not under contrived laboratory experiments.

The book, published in 2011 by The MIT Press, summarizes the fruits of his research.  It’s a captivating work.

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Make decisions, not calculations

How should doctors deal with uncertainty?

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How should doctors make decisions?

In the last post, we reviewed the conceptual difficulty of applying risk estimates derived from population studies to individuals, and I proposed that medical decisions should NOT be modeled on the following scheme:

  • Step 1: weigh the probability of future pluses and minuses.
  • Step 2: make a decision on the basis of “probability of pluses > probability of minuses.”

Yet that scheme implicitly serves as a rationale for pay-for-performance measures and justifies legal impositions on the content of informed consent procedures (e.g., precise disclosure of risks, probability of benefit, risks of alternative treatments, etc.).

Today, I will add a some perspectives that I think buttress my case.

Disclaimer:   This discussion is not meant to entice you to violate the standard of care, to forego the established procedures for informed consent, to incite you into civil disobedience, or to put your medical license at risk!

Let’s start with two anecdotes:

A few years ago, the New York Times detailed the extraordinary story of Michael DeBakey’s aortic repair operation.

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Risky decisions

Pay-for-performance, shared decisions, and the science of risk

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I had a sort of epiphany about risk and medical decision-making last Thursday evening, even though I was particularly tired and in a state of mental fog.

I had been thinking about two recent articles dealing with risk prediction, but I also was preoccupied with risky career decisions I have made, and how these are affecting me and my family (I am pursuing a quixotic version of the “triple threat:” independent physician, apprentice schoolman, blogger).  So risk was very much on my mind.

One of the articles I had read was a recent viewpoint in JAMA by Allan Sniderman, Ralph d’Agostino, and Michael Pencina titled “The role of the physician in the era of predictive analytics.”  The other was a response to that article written by Bill Gardner at the Incidental Economist blog.

In their paper, Sniderman et al. discuss an important difficulty in the medical science of risk prediction.  The difficulty is apparent when one contrasts the notion of population risk with the notion of individual risk.

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Skills, Knowledge, and Prediction

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The following letter to the editor was published in the American Journal of Cardiology in response to an excellent article by George Diamond and Sanjay Kaul who highlighted the limitations of quantitative methods for achieving relevant “risk-stratification” at the individual level.  Comments made by these authors prompted me to reflect on the tension between the appeal of quantitative methods and the value of unquantifiable clinical skills.  I hope you will find these remarks stimulating.

In the March 15, 2012, issue of The American Journal of Cardiology, Diamond and Kaul1 provided an insightful analysis of the complex relation between risk stratification schemes and therapeutic decision making. The investigators clearly identified some of the reasons why predicting response to treatment at the individual level is difficult. However, they conclude their report with a caution against “wholesale abandonment of evidence-based guidelines in favor of idiosyncratic clinical judgment,” which, in their opinion, runs the risk of “intellectual gerrymandering” and “wasteful utilization of high-cost technology.”

Proponents of quantitative methods of clinical assessment frequently portray critics as Luddites ready to “jettison” objective evaluation in favor of personal opinion rooted solely in clinical experience.2 This is an unfair characterization.

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