Missed and Erroneous Diagnoses Common in Primary Care Visits

Georjean Marie Parrish georjean.parrish at GMAIL.COM
Mon Jan 6 09:00:17 UTC 2014


What about PCP that completely overlooks documented test results and does
not question the proposed treatment plan recommended by a specialist when
it is questionable. Shouldn't the information at least be shared with the
patient so they can make an INFORMED decision.  After all they are the
Customer.


On Thu, Jan 2, 2014 at 6:16 PM, Alan Morris <Alan.Morris at imail.org> wrote:

>   I agree.
>
>  Whether one used a Bayesian, Fuzzy Logic, or Production rule approach,
> the knowledge engineering effort will require articulation of the
> thresholds (bayesian probability, fuzzy value, or production rule) that
> identify the initiation of treatment.  We have done this for mechanical
> ventilation, intravenous fluid, and blood glucose management.  Some propose
> Fuzzy logic or neural networks as means better matching the computer
> program rules with clinical thought processes – but this does not change
> the challenge of identifying when treatment begins.
>
>  Many physicians are reluctant to proceed with rule generation because
> they want to be assured the "rules are right."  This is an artificial
> barrier to progress.  One only need require that the rules are reasonable.
>  Finally, use of such decision-support tools requires that participating
> clinicians be willing to abandon personal styles of care.  This is a is
> step, but necessary and quite feasible.  A good starting point is a serious
> discussion of human cognitive limitations.  Physicians, like most humans,
> seem to think they can do more cognitively than seems humanly possible.
>  Have  a nice day.
>
>     Alan H. Morris, M.D.
> Professor of Medicine
> Adjunct Prof. of Medical Informatics
> University of Utah
>
>  Director of Research
> Director Urban Central Region Blood Gas and Pulmonary Laboratories
> Pulmonary/Critical Care Division
> Sorenson Heart & Lung Center - 6th Floor
> Intermountain Medical Center
> 5121 South Cottonwood Street
> Murray, Utah  84157-7000, USA
>
>  Office Phone: 801-507-4603
> Mobile Phone: 801-718-1283
> Fax: 801-507-4699
> e-mail: alan.morris at imail.org
> e-mail: alanhmorris at gmail.com
>
>   From: <Carroll>, Thomas <Thomas_Carroll at URMC.Rochester.edu>
> Date: Thursday, January 2, 2014 8:38 AM
> To: Society to Improve Diagnosis in Medicine <
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>, "Alan H. Morris, MD" <
> alan.morris at imail.org>
> Subject: RE: [IMPROVEDX] Missed and Erroneous Diagnoses Common in Primary
> Care Visits
>
>   I have been following this discussion with interest.  While it is
> certinally possible that I just missed it, I have not seen any discussion
> of testing and treatment thresholds.  In my opinion, referring to the act
> of "making the diagnosis" in a absolute sense misses the important point
> that uncertainty always exists.  In the case of a broken arm the
> uncertainty is extremely low, but (at least in principle) still not
> absolutely zero.  In the real world it is not impossible that I'm looking
> at the wrong xray...
>
> Dr. Brush mentioned that using a strictly Bayesian approach we would never
> be able to diagnosis a rare disease.  I agree that we may never be able to
> reach 100% certainty, but that is not necessary as, in my view, the primary
> reason to "make a diangosis" is to decide whether or not to treat an
> individual patient for that particular condition (there, however, are other
> reasons, determination of prognosis, etc., but I think the point stands).
>
> So, whether a condition is rare, common, or otherwise, if the probability
> of that condition in the particular patient exceeds the treatment threshold
> (and is above the testing threshold and so cannot be disproven with further
> testing), then the "diagnosis is made" insofar as treatment is concerned
> whether the probability of the disase is 5% or 95%.
>
>
>
> Thomas M. Carroll M.D., Ph.D.
> Assistant Professor, General Medicine & Palliative Care
>
> University of Rochester
> thomas_carroll at urmc.rochester.edu
> Pager 5-1616 #3872
>
> General Medicine Office 585-275-7424
>
> Palliative Care Office 585-273-1154
>
>  ------------------------------
> *From:* Alan Morris [Alan.Morris at IMAIL.ORG]
> *Sent:* Wednesday, January 01, 2014 7:18 PM
> *To:* IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> *Subject:* Re: [IMPROVEDX] Missed and Erroneous Diagnoses Common in
> Primary Care Visits
>
>    Interesting comments continue.  I think the recent exchanges in the
> discussion confound 3 different constructs:
>
>    1. Probability (frequentist) based on credible data.  This probability
>    provides the foundation for evidence-based medical decision-making.
>    2. Personal opinion about probability (when no credible data are
>    available).  This probability provides the foundation for independent
>    clinician decisioin-making that sometimes justifies the eponym "art of
>    medicine."
>    3. Signal-to-noise ratio that determines the need for additional
>    information and for decision-support.
>       1.  A compound fracture is such a strong signal that background
>       noise is unimportant. The phenotypic signal-to-noise ratio is so high that
>       virtually all clinicians will come to the same diagnostic conclusion.   No
>       further information is needed.
>       2. On the other hand, substernal chest pain due to coronary
>       ischemia and substernal chest pain due to esophagitis may be so similar
>       that the phenotypic signal-to-noise ratio is low.  In the absence of
>       further information, unaided clinician decision-makers are likely to to
>       make the wrong decision.
>
>  Have  a nice day.
>
>     Alan H. Morris, M.D.
> Professor of Medicine
> Adjunct Prof. of Medical Informatics
> University of Utah
>
>  Director of Research
> Director Urban Central Region Blood Gas and Pulmonary Laboratories
> Pulmonary/Critical Care Division
> Sorenson Heart & Lung Center - 6th Floor
> Intermountain Medical Center
> 5121 South Cottonwood Street
> Murray, Utah  84157-7000, USA
>
>  Office Phone: 801-507-4603
> Mobile Phone: 801-718-1283
> Fax: 801-507-4699
> e-mail: alan.morris at imail.org
> e-mail: alanhmorris at gmail.com
>
>   From: John Brush <jebrush at ME.COM>
> Reply-To: Society to Improve Diagnosis in Medicine <
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>, John Brush <jebrush at ME.COM>
> Date: Wednesday, January 1, 2014 7:06 AM
> To: "IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG" <
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
> Subject: Re: [IMPROVEDX] Missed and Erroneous Diagnoses Common in Primary
> Care Visits
>
>   Thanks, Bimal, for a very interesting discussion.
>         You seem to be tenaciously holding on to the frequentist notion of
> probability. Many statisticians agree with you and argue that this is the
> only way to think about probability. But I think that we, as clinicians,
> need to use both the frequentist notion and the personal notion of
> probability to properly apply our experience to the care of individual
> patients.
>         We can use the frequentist notion to look back at our experience,
> and learn from the variability that we observe. But we also have to go to
> work each day and take care of unique patents one at a time. We have to
> place these patients into diagnostic categories, and our challenge is to do
> that correctly and in a timely fashion.
>         I agree that a Bayesian approach is not always necessary. A broken
> arm is a broken arm. And if you always strictly used a pure Bayesian
> approach, you would never diagnose a rare disease. I also agree that some
> clinical findings are such strong evidence that they dominate any
> probabilistic assessment. Some findings are pathognomonic. But very few
> tests have a positive likelihood ratio of greater than 10. We need a
> Bayesian approach to guide us when the diagnosis is obscure, the signs and
> symptoms are ambiguous, and the available tests are imperfect.
>         Try this: The next time you see a patient who you think has a
> pulmonary embolus, ask yourself, “am I absolutely certain of the
> diagnosis?” If your answer is no, then ask yourself “on a scale of 1 to 10,
> how certain am I of the diagnosis?” I think you would agree that it is
> reasonable to pin yourself down with that question, to determine your level
> of certainty. With the personal or Bayesian notion of probability, you are
> doing essentially the same thing. Rather than a scale of 1 to 10, you are
> using a scale of 0 to 1. The Bayesian approach that I suggested in my last
> email gives you a way to arrive at that number in a logical and systematic
> way that takes advantage of all of the information that you have.
>         One other thought: Imagine that you are a cardiologist like me and
> many patients are sent to you to determine if they have CAD. You have
> noticed over the years, given your referral patterns, that you ultimately
> diagnose CAD in about half of the patients who are referred to you. Lets
> say that you see 10 patients every day. Over the years, you observe that on
> many days, 5 of the 10 patients have CAD. Other days, it’s 4 or 6. On rare
> days it's 1 or 9, and almost never will it be 0 or 10. What you can do is
> create a binomial distribution that shows how patients add up over time. So
> single patients with a binary outcome can be turned into a frequency
> distribution. See the graph below (if it gets through the listserv).
>
>
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>         People have been arguing about the meaning of probability for over
> 300 years. Most people have only a cursory understanding of the meaning of
> probability. And most of our cognitive errors (base rate neglect,
> availability, representativeness, regression to the mean, etc) are really
> just miscalculations of probability estimates.
>         The reality is that we practice a messy, uncertain world where we
> have to use inductive reasoning and we are forced to use probability. I
> think we should spend more time in medical education helping people
> understand these concepts. I don’t claim to fully understand probability.
> But I’ve wrestled with it a lot and I think that discussions like this one
> are really useful and important.
>
> Happy New Year to all!
> 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 Dec 31, 2013, at 10:49 AM, Bimal Jain <bjain at PARTNERS.ORG> wrote:
>
> Hi John, I congratulate you on your excellent book which I enjoyed
> immensely. I found it to be very well written and highly informative. It
> should be read by every practising physician.
> I would like to make a few cautionary remarks about role of probability in
> clinical diagnosis.
> 1. A probability, whether prior or posterior symbolises a distribution or
> frequency in a series of similar patients while clinical diagnosis seeks to
> determine a disease correctly in a given, individual patient.
> 2. Therefore, while probabilistic considerations help move the diagnostic
> process forward, a probability cannot be considered evidence from which a
> disease is diagnosed in a given patient.
> 3. Thus, it is entirely reasonable to test for a disease with high prior
> probability first, as he is drawn from a series of patients in most of whom
> the disease is present. Therefore, the chance of this disease being found
> in our patient is high.
> 4.What would be inappropriate, I suggest, would be to equate high prior
> probability with high prior evidence and use it as such for diagnosis.
> 5. This occurs, I suggest in patients with low prior prob. when a disease
> is ruled out (declared absent) without testing. This has been reported in
> several cases of acute MI being missed in healthy young women with atypical
> chest pain.
> 6. Evidence in a given patient, I believe, is best (and perhaps only)
> measured by a likelihood ratio. It is customary to diagnose a disease
> definitively only if a test result with LR of 10 or higher is observed,
> regardless of prior prob. ( diagnosis of acute MI from acute Q wave and ST
> elevation EKG changes, LR 13).
> 7. It is well known from experience, any given disease occurs in different
> patients with clinical presentations and therefore prior prob. which vary
> over a wide range. Our goal as clinicians is to diagnose a disease
> correctly in a given, individual patient regardless of prior prob.
> 8. In practice, therefore, the correct approach, when confronted by a
> patient with symptoms, I suggest, is to look upon a presentation as a
> problem to be resolved and not as evidence for a certain disease.
> 9. The presentation functions as a clue which makes us suspect several
> diseases. The order in which we test them may be determinedby prior prob.
> as discussed earlier.
> 10.Some test results, at least, such as acute EKG changes (LR 13),
> positive chest CT angiogram (LR 21), positive venous ultrasound (LR 19)
> lead to definitive diagnosis of their repective diseases, acute MI,
> pulmonary embolism, DVT, regardless of prior prob.
> 11. This suggest, diagnosis of some diseases at least is not performed in
> a Bayesian manner in actual practice.
> 12. I believe, it is important to record diagnostic errors in registries
> as I suggested earlier, so we can study these cases and learn why errors
> were made. Was a disease not suspected because of its low prior prob. or
> was an inappropriate test emplyed to diagnose or rule out a disease?
> 13. Till such registries are formed, we can present and discuss such cases
> in our forum hwere.
> 14. I think we need more observational and experimental studies of
> diagnosis in actual practice to ascertain the method which minimises error.
> 15. In my view, a strictly Bayesian appproach is not such a method.
>
> Bimal P Jain MD
> Pulm.-Crit.Care
> Northshore Med. Center (Union)
>
>
>
>
>
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