Missed and Erroneous Diagnoses Common in Primary Care Visits

Karen Cosby kcosby40 at GMAIL.COM
Mon Dec 23 22:51:17 UTC 2013

As I've listened to this discussion I'm struck that there is an assumption
that we make specific diagnoses with  absolute standards and therefore, the
diagnosis must always be accurate.  In primary care, and emergency
medicine, we rarely have tools refined well enough to make many diagnoses
with a high degree of certainty or specificity.  Mostly we make estimates
of likelihood of disease based on epidemiology (we worry about influenza
during flu season, we consider carbon monoxide poisoning at the start of
cold weather), and assess patient risk profiles (a older male with poorly
controlled hypertension and and long smoking history makes me worry a lot
more about aneurysm; a young male with no past medical history not).  We
look for indications for intervention, thresholds for action.  But we are
often not so concerned about making an exact and highly refined specific
diagnosis than we are about ruling out things we can do something about.
 We and the public are highly invested in making diagnoses and being
accurate, but in fact, most diagnoses are at best estimates of disease
likelihood and estimates of risk.  There is always a broader differential,
even if it's a possibility of an atypical presentation and/or uncommon
condition.  Also, we tend to have this discussion without much
consideration for the pragmatic aspects of medical care.  When I first
trained in medicine, the standard of diagnosis for pneumonia was a culture
from a quality sputum sample.  We've since learned that most sick patients
can't wait for treatment (and specific diagnosis) to depend upon culture
results.  We may not have the exact diagnosis, but are good enough to treat
the majority of patients.  I think part of our struggle is to determine
what end point we want to perfect: just diagnosis, or are we really just
talking about optimizing outcomes.  Front line clinicians really have to
function with assessments of probability.  When we review cases of
diagnostic error in retrospect, we often feel secure and convinced of the
one final diagnosis.  But that same case could have had many other
explanations for the same presentation.  We fool ourselves into thinking
that the case as presented has the final complete and perfect answer, when
in fact, I've seen cases critiqued and judged harshly only to have the
patient to return with another final diagnosis.  Maybe a diagnosis should
just be considered a placeholder that serves us well until it is replaced
with another, better, more refined label.  Just like theories in science, a
theory is assumed until it is disproven, but is itself never actually

On Mon, Dec 23, 2013 at 1:23 PM, Bimal Jain <bjain at partners.org> wrote:

> Your point about prior probability is well taken. The point I am trying to
> make is  that with any presentation with any estimated prior probability,
> we have no evidence for presence or absence of a suspected disease in a
> given, individual patient. I would have no objection to assigning a prior
> probability of 0.5 to every patient with a suspected disease regardless of
> presentation. This would lead to a post test probability of disease of 90
> percent in every patient in whom a test result with likelihood ratio of 10
> is observed. This post test test probability will actually correspond to
> our experience as I discuss below.
> It is well known from experience any given disease , acute myocardial
> infarction for example occurs in different patients with varying
> presentations and therefore varying prior probabilities. As a presentation
> is constituted by combination of a number of independent factors such as
> symptoms, age, sex, risk factors,etc. we can expect the prior probability
> to be distributed normally in patients with disease encountered by us. (In
> PIOPED study on diagnosis of pulmonary embolsism, 68 percent patients with
> pulmonary enbolsm had mid range prior probabilities JAMA 263: 1990,
> 2753-2759) The average prior probability in these patients will then be
> close to 0.5. Observation of a test result with likelihood ratio of 10 in
> these patients will lead to an average post test probability of 90 percent
> indicating the test result diagnoses diease correctly in 90 percent
> patients. This high accuracy was actually observed in a large series of
> patients in whom acute Q wave and ST elevatio EKG changes with likelihood
> ratio of 13 diagnosed acute MI correctly in 90 percent patients (Rude et al
> Am J Card 52: 1983, 936-942). The problem with the standard Bayesian
> approach in which a prior probability is estimated from presentation in a
> given patient is that it leads to a diagnosis sometimes which is wrong from
> a clinical standpoint. This is seen in the following case, discussed in a
> clinical problem solvig exercise(Pauker et al NEJM 326:1992, 688-691). A 40
> year old healthy woman without any cardiac risk factor presents with highly
> atypical chest pain and is found to hav acute Q wave, ST elevation EKG
> changes.Her prior probability estimaterd to be 7 percent of MI is combined
> with LR of EKG changes of 13 to yield a post test probability of 50
> percent. The Bayesian diagnosis of MI being indeterminate is in sharp
> contrast to near certain diagnosis of MI from EKG changes alone by
> discussing physician.
> Moderator: Lorri Zipperer Lorri at ZPM1.com, Communication co-chair, Society
> for Improving Diagnosis in Medicine
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