Fwd: [IMPROVEDX] IOM report is released - Diagnosis in actual practice

Julianne Nemes Walsh nemeswalsh at GMAIL.COM
Sun Oct 11 10:52:15 UTC 2015


Hello Jason,

The best example I could use would be a recent case I encountered in my
primary care practice.  I had a 3.5 month old presented with an 8 hour
history of intermittent crying episodes where he would scream and cry for
several minutes, unconsolable when held, and then within a few minutes he
would calm.  He had been at daycare most of the day.  He presented with
mother. He would breastfeed intermittently but about 40% of usual and
refused to do so in the office. He had episodes of vomiting when he got
upset and the vomit was either breastmilk or clear, non bloody and non
bilious.  He had no fever, no recent illness, no family members ill, he had
no bloody stools, he was happy in between episodes.  He cried when
examining him but with distraction I was able to get a descent examination
of his abdomen and the remainder of his body and it was completely
negative.  He had tears and wet diapers and was well hydrated with nl heart
rate. Given his history one of the most worrisome diagnostic concerns was
pyloric stenosis, intermittent volvulus.  I took a diagnostic time out, my
colleague and I looked at probabilities of this diagnosis at this age,
given the history and examination findings, they were low but possible.  I
shared the probabilities with the mother, sent the child home with clearly
written instructions of when to return or go to an ER. I  instructed the
mother to bring him home into his usual setting, attempt to feed him, and I
had a call to the family in the am. I also shared the diagnostic
uncertainty  with the family. The infant did well and fed well at home
without any further vomiting, crying episodes.   The use of probabilities
was helpful in this situation, it avoided over diagnosis, over treatment,
over expenditure of costly tests.  The importance of cognitively forcing a
differential diagnosis, then thinking of probabilities,utilizing diagnostic
time outs and including the family in the process, as well as the inclusion
of close follow up are all equally important in our efforts to improve
accuracy.

Julianne Nemes Walsh

On Thu, Oct 8, 2015 at 10:08 AM, Jason Maude <
Jason.Maude at isabelhealthcare.com> wrote:

> Although this is a very stimulating debate, I am struggling to understand
> how relevant it actually is to the diagnosis of individual patients as a
> key additional variable will always be the personal consequences of a wrong
> decision.  The key difference with a probabilistic approach in life
> assurance or similar versus diagnosis of a particular patient has to be the
> consequences of getting it wrong. This means that nobody is likely to
> follow a purely probabilistic approach if they know the patient might die
> if they didn’t check for something even if it was a lower probability. The
> odds of winning the lottery are ludicrously bad but because the prize is so
> big (upside consequences) people still try their luck. Personal
> consequences will always seriously affect rational calculations of
> probability.
>
>
> Jason Maude
> Founder and CEO Isabel Healthcare
>
>
> From: "Jain, Bimal P.,M.D." <BJAIN at PARTNERS.ORG>
> Reply-To: Society to Improve Diagnosis in Medicine <
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>, "Jain, Bimal P.,M.D." <
> BJAIN at PARTNERS.ORG>
> Date: Thursday, 8 October 2015 12:13
> To: "IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG" <
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
>
> Subject: Re: [IMPROVEDX] Fwd: [IMPROVEDX] IOM report is released -
> Diagnosis in actual practice
>
> We can only comment on and critically evaluate material that is published.
> I find it simply amazing that a probabilistic approach in which probability
> is evidence has not been employed in even a single amidst hundreds of
> published CPCs and clinical problem solving exercises. Dr. Brush dismisses
> CPCs as artificial, pedagogical exercises employing System 2 thinking over
> days or weeks. This is all the more reason to employ a probabilistic
> approach as the discussants then have plenty of time to estimate prior
> probabilities and calculate posterior probabilities. This is not done
> simply because this approach has not been found useful for diagnosis. Some
> time back, I carefully examined 50 consecutive CPCs in NEJM from July 2013
> to OCTOBER 2014. I found the word probability mentioned only once in these
> 50 CPCs. If Dr. Brush thinks this approach is suitable only for System 1
> thinking in diagnosis ,Croskerry has pointed out the danger of such
> thinking in causing diagnostic errors. At present, the emperor does not
> appear to have any clothes with regard to probabilistic approach to
> diagnosis in these exercises. What is  needed ,I think are head to head
> observational or experimental studies comparing usual to probabilistic
> approach in real patients.
>
>
>
> The adage ‘Common things are common’ is useful only in indicating chance
> of a disease in a given patient. Certainly, we should look for a common
> disease first as it has the greatest chance of being found. The problem
> arises when a frequency or probability is taken as evidence for a disease.
> There is little doubt in my mind, diagnostic errors due to failure to
> suspect a disease in patients with atypical presentation in studies of
> Hardeep Singh and John Ely arose from interpreting low prior probability as
> absence of evidence for the disease.
>
>
>
> In discussion about STEMI, Dr. Brush rightly deals with all patients with
> STEMI regardless of  prior probability in the same manner by taking them
> all for cardiac cath. His accuracy rate of acute MI of 85 percent in these
> patients is close to the rate of 90 percent in my paper. If he were to
> analyze his data he would find the majority of patients with acute MI to
> have intermediate or high prior probability.
>
>
>
> I refer the Central Limit Theorem with regard to distribution of prior
> probability which is a continuous variable.
>
>
>
> The main problem with a probabilistic approach is that it takes
> probability as evidence in a given individual patient while it is true only
> in groups of patients. There is no proof that it improves diagnosis in
> actual practice. Its use appears to have become a dogma which is hindering
> efforts to reduce diagnostic errors. It is only by looking at diagnosis in
> actual practice such as in studies of H. Singh and J. Ely and analyzing
> results without putting on probabilistic  glasses that we shall make
> progress.
>
>
>
> I mention three examples from history of science of dogmatic beliefs
> hindering progress which was made only when phenomena as they occur were
> analyzed.
>
> 1.       Since the time of Plato, the belief in planetary orbits being
> circular due to perfection of a circle as a geometrical figure. All
> contrary observations were explained away by drawing circles(epicycles)
> within circles. It was only two thousand years later that Kepler determined
> the orbit of Mars to be an ellipse when he actually observed and analyzed
> its movement.
>
> 2.       Since the time of Aristotle, every movement was believed to
> require a mover. Contrary observations such as flight of an arrow were
> explained away in an absurd manner. Again, about two thousand years later,
> the true law of motion, that it is change in motion and not motion itself
> that requires a force was discovered when Galileo observed and analyzed
> actual motion of rolling balls.
>
> 3.       And nearer to our age, there was a widespread belief in Absolute
> Time since Newton declared it to exist in the 17th century. It was only
> in early 20 the century this belief was overthrown by Einstein by his
> insightful analysis of actual time in terms of clocks and trains.
>
>
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>
> Bimal
>
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>
> Bimal P Jain MD
>
> Pulmonary-Critical Care
>
> North Shore Medical Center
>
> Lynn MA 01904
>
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> *From:* John Brush [mailto:jebrush at ME.COM <jebrush at ME.COM>]
> *Sent:* Saturday, October 03, 2015 8:58 AM
> *To:* IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> *Subject:* Re: [IMPROVEDX] Fwd: [IMPROVEDX] IOM report is released -
> Diagnosis in actual practice
>
>
>
> I’m afraid that I can’t agree with Dr. Jain’s argument. I think his
> argument is circular, difficulty to follow, and selectively self-serving.
>
>             We have an adage in medicine: “Common things are common.”
> Otherwise, every diagnostic exercise would become a wild goose chase,
> leading us to look into every remote possibility every time. Having said
> that, I can also say that if we collect cases over time, uncommon things
> become common. Someone somewhere will eventually win the lottery. Uncommon
> diagnoses do occur eventually. But the exceptions should not define the
> rules.
>
>             The STEMI case that Dr. Jain presents proves my point. I am
> in interventional cardiologist who frequently takes patients with suspected
> STEMI to the cath lab for intervention. I have been getting direct feedback
> on these cases for about 25 years. I can tell you that there is a false
> positive rate of about 15% among STEMI alerts that are taken to the cath
> lab (numerous reports in the literature confirm that estimate). We allow
> that false positive rate because we make a subjective calculation of
> expected value. Even if a patient has a relatively low initial prior
> probability of STEMI, like Dr. Jain’s example, we don’t want to miss a
> serious diagnosis like a STEMI. The EKG findings change the probability
> estimate and make a STEMI quite plausible in such a patient. In a patient
> like Dr. Jain’s example, we know that there is about a 50-50 chance of
> finding an occluded artery, which is certainly high enough to activate the
> cath lab. And sure enough, over time, 50% is about the frequency that we
> find in such patients.
>
>             Dr. Jain references central limit theorem. That theorem
> applies to probability for a continuous variable, and states that for any
> distribution, the sample means of repeated samples will become a normal
> distribution. I’m not sure I follow his argument that it applies to a
> probability distribution of categorial variables. A diagnostic category is
> a countable variable. Kolmogorov’s principles, however, do apply. The
> probabilities of all of the possibilities do add up to one, if they are all
> independent. General knowledge of these probability principles can help us
> organize our thinking.
>
>             When we see a patient with chest pain in the ED, we start to
> narrow the sample space by asking questions and making observations. For
> example, we can eliminate the possibility of a stab wound very quickly by
> noticing that there is no knife in the chest. Through early hypothesis
> generation, we narrow the range of possibilities to the point were we can
> start the process of iterative hypothesis testing. We have at our disposal
> many possible tests that we can perform. We can send a troponin, do a CT
> scan for dissection, do a stress echo, go directly to the cath lab, etc. We
> can’t do all of these tests at the same time, and we probably don’t want to
> do every test on every patient. So how do we decide what test to do first?
> We do a little mental calculation of the subjective probabilities, which
> gives us an idea of the expected value of each test. We don’t want to miss
> a diagnosis with serious consequences, like MI or dissection, so an EKG and
> CXR are done on virtually everyone, regardless of the prior probability.
> But we narrow the sample space as we hone in on the correct diagnosis. We
> don’t want to narrow the search prematurely, and we use a differential
> diagnosis to help us guard against jumping to conclusions. All of this is
> guided by some notion of relative probabilities.
>
>             Dr. Jain talks about the CPC method of diagnosis. This is a
> useful pedagogical exercise, where an expert can expound on clinical
> medicine, but it is very artificial, as compared to real world practice. An
> expert may spend days or weeks preparing a CPC discussion. His/her main
> goals are to not miss the diagnosis, and to eloquently discuss all of the
> possibilities. It is almost purely System 2 thinking. In the real world,
> with time constraints and uncertainty, we have to employ System 1’s
> intuition. It is helpful, however, if we calibrate our intuition through
> knowledge of the relative strength of evidence and the base rates of
> various diagnostic possibilities. I think that having an intuitive sense of
> probability is the essence of experiential knowledge. Savvy clinicians make
> good bets.
>
>             The fundamental assumption of evidence based medicine is that
> the frequencies that we measure in populations of patients can be applied
> to an individual patient. The measured frequencies from our aggregated
> experience, or from the reports in the literature inform us on how we
> should think about an individual. Single event or single patient
> probability then becomes a degree of belief, which is then modified by
> additional information that we gain through diagnostic testing. In fact,
> the sensitivity and specificity of diagnostic tests are defined using a
> frequency notion of probability. They are cumulative probabilities,
> depending on where we draw the line of demarcation. Some tests, like an
> x-ray for a broken arm, are so compelling that they lead to absolute
> certainty. Other tests, like EKGs, stress tests, troponins, etc, don’t have
> perfect operating characteristics, however, and we are left with a
> probability estimate for each diagnostic possibility that is somewhere
> between 0 and 1. Usually we get to a point of certainty, but sometimes,
> through adductive reasoning, we are left with the most plausible diagnosis,
> but never really know for sure.
>
>             I hate to drag the listserv through this back and forth
> again, but to me, Dr. Jain’s arguments seem to counter what we have been
> taught about evidence-based medicine, but also run counter to principles of
> cognitive psychology. Without some intuitive idea of probability and
> likelihood, we would be totally adrift in clinical medicine, so I just
> can’t let this go.
>
> John
>
>
>
> John E. Brush, Jr., M.D., FACC
>
> Professor of Medicine
>
> Eastern Virginia Medical School
>
> Sentara Cardiology Specialists
>
> 844 Kempsville Road, Suite 204
>
> Norfolk, VA 23502
>
> 757-261-0700
>
> Cell: 757-477-1990
>
> jebrush at me.com
>
>
>
>
>
>
>
> On Oct 2, 2015, at 2:45 PM, Mark Graber <mark.graber at IMPROVEDIAGNOSIS.ORG>
> wrote:
>
>
>
> Note and manuscript forwarded on behalf of Dr Bimal Jain.
>
>
> ------------------------------
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> Moderator:David Meyers, Board Member, Society for Improving Diagnosis in
> Medicine
>
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>
> <Bimal Jain - The Role of Probability in Diagnosis.docx>
>
>
>
>
>
> *From:* Jain, Bimal P.,M.D.
> *Sent:* Thursday, October 01, 2015 1:54 PM
> *To:* 'Mark Graber'
> *Subject:* RE: [IMPROVEDX] IOM report is released - Diagnosis in actual
> practice
>
>
>
> Hi Mark and all,
>
>
>
> It is important to understand how diagnosis is performed in actual
> practice as a correct diagnosis is made after all  85 percent of the time
> in practice. To reduce diagnostic errors, we need to know if the method in
> practice needs to be improved or whether certain deviations from it need to
> be eliminated. The most puzzling issue in this regard is the role that
> probability plays or does not play in diagnosis. The puzzle arises because
> a probabilistic approach has been prescribed for a long time, but it does
> not appear to be employed in practice when we look at published CPCs and
> clinical problem solving exercises. Does this disparity imply that a
> probabilistic approach is not suitable for diagnosis in actual practice?
> This is certainly possible as diagnosis is performed in a given, individual
> patient with the aim of determining a disease correctly in that particular
> patient. And probability, as is well known has been employed most
> successfully in practice in areas such as epidemiology and life insurance
> business where the focus is on accuracy of prediction in a large group of
> persons, not on prediction in a given individual person.
>
> If we look closely, we note that a strict probabilistic approach in which
> a probability represents evidence may actually increase diagnostic errors
> specially in patients with atypical presentations by encouraging the
> cognitive bias of representativeness and inhibiting comprehensive
> differential diagnosis (discussed in attached paper).
>
> I have put together my thoughts on this subject in the attached paper ‘The
> role of probability in diagnosis’. Please review and comment on it. Thanks.
>
>
>
> Bimal
>
>
>
>
>
> Bimal P Jain MD
>
> Pumonary-Critical Care
>
> North shore Medical CENTER
>
> Lynn MA 01904
>
>
>
>
>
>
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> To unsubscribe from IMPROVEDX: click the following link:
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> Moderator:David Meyers, Board Member, Society for Improving Diagnosis in
> Medicine
>
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Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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