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

Vipindas Chengat syncopesystem at GMAIL.COM
Wed Oct 7 01:13:42 UTC 2015


I agree with both view points. :)

As Dr. Jain points out, there's always been a problem of taking a Bayesian
approach while trying to use epidemiological data. We’d never go to a
patient's room armed with a logarithmic table of probabilities. Instead, we
work to understand the relation between symptoms and diagnoses from a
patho-physiological perspective. We never know what the exact base rate is.
In the example he gave, 7% is the prevalence of CAD in that population but
we can't say for sure that it is the same probability for patients who come
to an ER with acute chest pain.

At the same time, we definitely use probabilities, too, as Dr. Brush points
out. There is no way we can practice evidence based medicine without
considering probabilities and likelihood ratios.

Keeping all this in mind, I designed an algorithm (licensed to Physician
Cognition) to work based on both pattern recognition AND Bayesian
principles. It is designed to improve its results continuously from both
user feedback and data analysis. I would like to provide it free of charge
for any research projects on the issues raised in this thread and at the
recent DEM conference. With over half a million lines of code, it can map
almost every relation that is useful to make a clinical diagnosis.

I am uncomfortable talking about my own project here. But since my team and
I have already done enough heavy lifting to move a mountain, I would be
doing patients a disservice if I stood by and let a group of expert and
committed clinicians try to reinvent a wheel that can’t be built without a
big team over a long time.

The algorithm is available for free at beta.physiciancognition.com. Faster,
simpler mobile apps will be available in a few weeks. For any combination
of unlimited numbers of symptoms, signs, labs, medications, and histories,
the software, after asking crucial questions, lists differential diagnoses
in the order of relevance and recommends further questions designed to
narrow down the differentials. By answering a few questions, the list and
the order changes considerably. It works just the same way as we clinicians
do, minus the cognitive error and bias. It’s a great tool for avoiding
under- or over-utilization, as well as for empowering and teaching NPs,
PAs, and residents.

You will sometimes find less than perfect results from the algorithm. There
is a feedback video in the top center (after log-in) that explains how
users can take action that will result in improving the system, for
everyone, within days or hours. I believe that is how we scalably improve
both medicine and healthcare globally.


*Vipindas Chengat, MD FACP**  |  *Chairman, Physician Cognition, Inc.
  —————————————————————————————————
  Mobile: +1 (773) 575-3550
  Email: Vipin at PhysicianCognition.Com <Vipin at PhysicianCognition.com>
  Website: PhysicianCognition.Com <http://physiciancognition.com/>



On Sat, Oct 3, 2015 at 7:58 AM, John Brush <jebrush at me.com> wrote:

> 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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> <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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> Moderator:David Meyers, Board Member, Society for Improving Diagnosis in
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>
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>
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> http://www.improvediagnosis.org/
>






Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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