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

Ely, John john-ely at UIOWA.EDU
Wed Oct 14 14:58:58 UTC 2015


If I have a room with 100 people complaining of sore throat in it and 20 of them have strep throat and I select one person randomly from that room, the probability that that person has strep throat is 20%.  So in that sense, the probability in the population is the same as the probability in the individual.

I think we use the word "likelihood" to mean the same thing as probability.  For example, a likelihood ratio is the ratio of two probabilities (not two odds, not two frequencies, but two probabilities).  Also, The American Heritage Dictionary of the English Language defines likelihood as "The state of being probable; probability."

John Ely, MD
University of Iowa

From: Jain, Bimal P.,M.D. [mailto:BJAIN at PARTNERS.ORG]
Sent: Tuesday, October 13, 2015 3:14 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] [IMPROVEDX] IOM report is released - Diagnosis in actual practice

In response to Dr. Brush, when a CPC discussant says ' the most likely diagnosis is ' he does not mean ' the most probable diagnosis is' in the sense of a disease having the highest frequency in a series or group of similar patients. What he means is that a disease has the greatest likelihood in that the given presentation or a test result has been observed to have the highest frequency in this disease compared to other diseases. He makes a statement of likelihood, not one of probability. The two concepts are completely different as a disease is fixed in a likelihood but is a random variable in probability. Despite this difference they are often mistaken for each other. Even the great Ronald Fisher thought likelihood was a probability when he first introduced it in 1913. However, he soon realized his mistake and described its properties clearly as we them today in his landmark paper Mathematical Foundations of Statistics in 1922.

If we look carefully at the process of diagnosis in CPCs as I have done in 50 CPCs, we note that it proceeds by employing likelihood as evidence. An extensive differential diagnosis is initially created and the likelihood of each disease is estimated from given data. The disease with the highest likelihood is then put forth as the most likely diagnosis. One would think that if 'most likely' is the same as 'most probable' as Dr. Brush suggest, then the phrase ' most probable' would be used, if he is correct in at least a few among the hundreds of CPCs that have been conducted. But this phrase has never been used, to the best of my knowledge in any CPC.

Furthermore, a prior probability is not estimated and used as prior evidence, a hallmark of the probabilistic approach in any7 CPC. This is not done, I suggest, because the prior probability only indicates the distribution of a disease in the population from which the given patient has been drawn. As this distribution has no bearing as evidence for or against a disease in the given patient, a prior probability is not estimated.

There is universal agreement that probability is highly accurate in representing evidence in terms of frequencies or averages in populations or large groups of similar objects or persons.. Therefore it has been extensively employed in areas such as epidemiology, life insurance business, stock portfolio management, statistical mechanics. Incidentally, some excellent comments on application of probability are to be found in writings of some great figures in statistical mechanics which include Maxwell, Boltzmann, Gibbs, Ehrenfests, Einstein. It is in employing probability to represent evidence in a single, given instance that problems arise. Here is Charles Sanders Peirce weighing in on this issue. 'Any individual inference must be either true or false.....in reference to a single case, considered in itself, probability can have no meaning.'

There are at least four other methods of statistical inference besides the probabilistic approach in which evidence is represented by a measure other than a probability. These are:

1.       Fisher's likelihood method, briefly discussed above and championed in recent years by AWF Edwards, Richard Royall.

2.       Fisher's test of significance method.

3.       Neyman's error probabilities (Types 1 and 2) method championed recently by Deborah Mayo.

4.       Neyman's confidence interval method.

One wonders why the founders of EBM (Sackett et al) chose probability as evidence and not some other measure for evidence in evidence based diagnosis. I believe, we have a clue to their thinking in this regard in the title of Sackett's well known book Clinical Epidemiology. It is possible they were so impressed by the accuracy of probability in representing evidence as a stable frequency in a population that they chose to represent evidence by a probability in the entirely different domain of a  given individual patient in which diagnosis is performed. It is amazing how rapidly and widely this was accepted without any proof of its validity. For example, I am still waiting for the first CPC or clinical problem solving exercise in which a disease is diagnosed correctly from a probability obtained by estimating a prior probability and calculating a posterior probability. Furthermore, a probabilistic approach, in my view promotes diagnostic error in some patients. Thus what is cognitive bias of representativeness in which only diseases with prototypical features (high prior probabilities) are suspected and diseases with atypical variants (low prior probabilities) ignored but a form of probabilistic reasoning in diagnosis.

What needed to be done and was not done by the founders, in my view, was to develop evidence based diagnosis in a constructive manner. This means starting with a close study and analysis of diagnosis in actual practice. A close examination of even a few CPCs would have revealed to them that evidence is not assessed by a probability in actual practice. They would have noted that probability behaves very differently in a given patient compared to in a population. In the latter, a higher probability always corresponds to a greater frequency compared to a lower probability which always corresponds to a lower frequency. In a given, individual patient, on the other hand, a higher as well as a lower probability may correspond to presence of a disease. If this initial analysis were done, it is possible, evidence in diagnosis may not have been represented by a probability.

In any case, there is such strong dogmatic belief in correctness of probability is evidence that even a suggestion to the contrary evokes the same incredulity and astonishment that were displayed by  the Aristotelians when they learnt that uniform motion does not require a mover which is described by Galileo in his great book  Discorsi or Discourses on two new sciences.

It is well to remember that many longstanding incorrect beliefs such as planetary orbits being circular, every motion requiring a mover, time being absolute were overthrown and corrections made only by careful study and analysis of actual processes related to them. Similarly, I believe, progress in improving diagnosis will be made only by studying and analyzing the process of diagnosis in actual practice. My own view, based on careful study of CPCs is that evidence in diagnosis in actual practice is represented by likelihood and not by probability. This needs to be confirmed or disproved by further study and analysis of actual diagnosis.

In the end, regarding this discussion, I can do no better than leave the last word for Einstein in the following long quote:
Concepts that have proven useful in ordering things easily achieve such an authority over us that we forget their earthly origins and accept them as unalterable givens. Thus they come to be stamped as 'necessities of thought', 'a priori givens' etc. The path of scientific advance is often made impassable for a long time through such errors. For that reason, it is by no means an idle game if we become practiced in analyzing the long commonplace concepts and exhibiting those circumstances upon which their justification and usefulness depend, how they have grown up individually out of the givens of experience. By these means, their all too great authority will be broken. They will be removed if they cannot be legitimized, corrected if their correlation with given things be far too superfluous, replaced by others if a new system can be established that we prefer for whatever reason.

I make no apologies for this very long email as the discussion of diagnosis, which is a complicated topic with many strands, requires many well thought out arguments that can only be expressed by detailed statements.

I would like to thank Mark Graber for providing a forum in ListServ for this important discussion.


Bimal



Bimal P Jain MD
Pulmonary-CriticalCare
NorthShore Medical Center
Lynn MA 01904















From: John Brush [mailto:jebrush at ME.COM]
Sent: Friday, October 09, 2015 1:16 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] [IMPROVEDX] IOM report is released - Diagnosis in actual practice

            Mark Ebell's insightful email points to the absurdity of not thinking probabilistically. The probabilities that skilled clinicians use in an intuitive fashion in practice are derived from scientific evidence and experience (experiential knowledge). I am a big believer in evidence-based medicine. Without the scientific underpinnings for our clinical activities, and explicit acknowledgement of where the science is lacking, we would be adrift.
            In David Sackett's original BMJ article on evidence-based medicine, he promoted the use of the best available scientific evidence "especially from patient centred clinical research into the accuracy and precision of diagnostic tests." He included with his book a nomogram that a clinician could use to incorporate likelihood ratios for diagnostic tests with prior probabilities to yield a posterior probability for a single patient undergoing a single test. To him and other EBM founders, probability was the very foundation of statistical inference and evidence-based medicine.
            In my book, I stated "We can make highly accurate actuarial predictions for populations, but we have trouble even comprehending what probability means for a single patient or event." I think this email trail points to the difficulty of thinking about probability in medicine. I am concerned that Dr. Jain's paper only adds to the confusion. I would submit that in every one of the CPCs that Dr. Jain refers to, the discussant makes a statement like, "I think the most likely diagnosis is...." The statement "most likely" is another way of saying "most probable." Dr. Jain's statement that probability is only a theoretical consideration and is not used in the practice of medicine is, I think, absurd. Not acknowledging the uncertainty in medicine through some statement of probability (which is simply a way to quantify uncertainty) leads to an illusion of certainty, arrogance on the part of the practitioner, and unrealistic patient and family expectations.
            In my book, it took me 5 chapters to fully develop the idea of probability and how it should be used to think about the diagnosis and treatment individual patients. This is tough to think about and comprehend and I think it can be misrepresented in an email listserv.
            For excellent reading on probability, I would suggest Ian Hacking's "An Introduction to Probability and Inductive Logic" or Gerd Gigerenzer's "Risk Savvy: How to Make Good Decisions" or "Calculated Risks: How to Know When Numbers Deceive You."
            Ian Hacking and Gerd Gigerenzer participated in a year long sabbatical with other philosophers, scientists, and cognitive psychologists where they explored the meaning of probability. Hacking has also written "The Taming of Chance" and "The Emergence of Probability." Girgerenzer has also written "The Empire of Chance: How Probability Changed Science and Everyday Life" for more in-depth reading.
            In clinical medicine, we use conditional probability every day, but because the exact numbers are only estimates, we actually use a heuristic called anchoring and adjusting. We use reason every day, but because the ultimate truth may be unknown, we use a type of reasoning called abductive reasoning. (My spell-checker incorrectly changed the spelling to adductive reasoning in my prior email.) Abductive reasoning, described by Charles Saunders Peirce, is "reasoning toward the most plausible hypothesis." When we start the diagnostic process, we are dealing with multiple hypotheses (plausible conjectures). We work through the process toward the most plausible hypothesis and, again, the term "most plausible" implies some concept of relative probability. With abductive reasoning, we blend both inductive reasoning and causal reasoning to make an argument (meaning a logical statement) that combines both probability and pathophysiologic rationale.
            As W. Edwards Deming said, "if you don't understand the process of what you are doing, you don't know what your are doing." It is important for clinicians to have a better understanding of the process of making a diagnosis. By developing and using good habits based on a deep understanding of process, the clinician will have the best chance of making the correct diagnosis, as reliably as humanly possible.
            My apologies about the long email, but I am very serious and passionate about improving the quality of medical decisions.
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<mailto:jebrush at me.com>



On Oct 8, 2015, at 2:54 PM, Mark H Ebell <ebell at UGA.EDU<mailto:ebell at UGA.EDU>> wrote:

So, I should order a chest CT for every patient with cough, to rule out lung cancer.

And I should order a stress thallium for every single 25 year old with chest pain that appears to be musculoskeletal, so I don't miss the rare MI.

And of course I should get a CT or MRI for every patient with a headache, to not miss the rare (and generally untreatable) CNS cancer.

Do you realize the cost and harm of this approach? The complications of invasive tests and biopsies and follow-up that go nowhere? The false alarms? Radiation?

But at least you won't successfully sue me. I guess that's all that matters.

Mark

-
Mark H. Ebell MD, MS
Professor of Epidemiology
University of Georgia
Editor, Essential Evidence
Deputy Editor, American Family Physician
ebell at uga.edu<mailto:ebell at uga.edu>


From: Phillip Benton
Reply-To: Society to Improve Diagnosis in Medicine, "pgbentonmd at AOL.COM<mailto:pgbentonmd at AOL.COM>"
Date: Thursday, October 8, 2015 at 1:17 PM
To: "IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>"
Subject: Re: [IMPROVEDX] Fwd: [IMPROVEDX] IOM report is released - Diagnosis in actual practice

I am an experienced  physician-attorney (Medicine 54 Years, Law 44 years) teaching Medical Malpractice at a top tier law school for almost 20 years.
Relevant to this discussion is the fact that missed or delayed diagnosis tops the list of causes for awards to injured plaintiffs at mediation or in jury trials. Accounts of serious medical error by the Institute of Medicine (1999) and the Journal of Public Safety (Sept, 2013) document missed or delayed diagnosis as a leading cause of preventable harm. The IOM estimate of up to 98,000 preventable deaths per year means one every 5 minutes 22 seconds; Dr. John James' evidence-based 2013 update of up to 440,000 preventable deaths per year equals one every 72 seconds.
One standard approach by the plaintiff''s attorney is to  have the defendant physician or defense expert witnesses agree that it is important to make a differential diagnosis, and then to first rule out the most serious and dangerous diagnoses on that list. In other words it is not the odds but it is the stakes that matter most. Greater experience of a physician may move this process from System II (rational) toward System I (intuitive) thinking, but the point is that juries (i.e., patients) routinely agree that you should always deal with  the most important things first.
A common expression heard when an uncommon disease is misdiagnosed is that "When you hear hoofbeats you think of horses, not zebras." The savvy attorney will then then ask "And how do you tell the difference? (pause) You look!"  Adequate testing to first rule out life-threatening conditions, treatable if caught early, may often allow a successful defense.

Phillip G Benton, MD, JD
Atlanta Georgia

-----Original Message-----
From: Bob Latino <blatino at RELIABILITY.COM<mailto:blatino at RELIABILITY.COM>>
To: IMPROVEDX <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>
Sent: Thu, Oct 8, 2015 10:55 am
Subject: Re: [IMPROVEDX] Fwd: [IMPROVEDX] IOM report is released - Diagnosis in actual practice
I am not a physician nor a clinician, so I come at this issue basically from the perspective of a patient.

When the physician becomes the patient, what is the expectation of them towards their care provider in terms of their diagnosis?

Physicians themselves would obviously be more critical of their peer's diagnosis when their lives are involved, because they are 'insiders' and know the probing questions to ask about how the diagnosis was derived.  What are those questions?  What should the non-clinical patient be asking of their doctors when they provide a diagnosis?

I am in the investigation business and work in aviation, nuclear power, military and other potentially life-threatening businesses.  Many in these businesses have to make spur of the moment decisions (diagnosing the problem) and then quickly act on it.

I will take pilots for instance.  I know healthcare has taken an interest in Crew Resource Management (CRM) from the training that pilots receive about effective cockpit communications and teamwork.  They too have to quickly make a diagnosis and act on it accordingly.

The difference between a pilot and a doctor in these situations is that the pilot and crew's lives are at stake (along with the passengers) as well, based on the accuracy of their diagnosis, decisions and actions.

Given this informative debate about probabilities and looking at situations/patients singularly versus as a population, how does a pilot make their quick assessment versus a doctor and their diagnosis?  Does the fact the pilot's life is at stake differ in their decision as opposed to a doctor, whose life is not likely at stake based on their decision?  Does it matter? Should it?


Robert J. Latino, CEO
Reliability Center, Inc.
1.800.457.0645
blatino at reliability.com<mailto:blatino at reliability.com>
www.reliability.com<http://www.reliability.com/>

From: Jason Maude [mailto:Jason.Maude at ISABELHEALTHCARE.COM<mailto:Jason.Maude at ISABELHEALTHCARE.COM?>]
Sent: Thursday, October 08, 2015 10:09 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] Fwd: [IMPROVEDX] IOM report is released - Diagnosis in actual practice

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<mailto:BJAIN at PARTNERS.ORG>>
Reply-To: Society to Improve Diagnosis in Medicine <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>>, "Jain, Bimal P.,M.D." <BJAIN at PARTNERS.ORG<mailto:BJAIN at PARTNERS.ORG>>
Date: Thursday, 8 October 2015 12:13
To: "IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>" <IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto: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.


Bimal


Bimal P Jain MD
Pulmonary-Critical Care
North Shore Medical Center
Lynn MA 01904


















From: John Brush [mailto:jebrush at ME.COM]
Sent: Saturday, October 03, 2015 8:58 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto: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<mailto:jebrush at me.com>



On Oct 2, 2015, at 2:45 PM, Mark Graber <mark.graber at IMPROVEDIAGNOSIS.ORG<mailto: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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