Missed and Erroneous Diagnoses Common in Primary Care Visits

Mahajan, Prashant PMahajan at DMC.ORG
Mon Dec 23 23:57:16 UTC 2013

Well said Karen.
Trying to determine prevalence of diagnostic errors in acute care settings is even more difficult. The time constraints, along with evolving illness, lack of available information to facilitate a more comprehensive differential diagnoses, liability concerns, unreasonable expectations from patients (parents of patients) and often absence of a reference standard make it impossible to actually determine whether an error occurred or whether illness is still evolving.
I have to often defend my colleagues during M&M conferences where everything seems obvious after the event has occurred that ED care is looked upon and often designated as suboptimal.
I am wondering if explicitly stating a differential diagnoses with the available information will be useful or will it just expose the provider to more liability if he/she is unable to comprehensively list all differential diagnoses.
This has been a great discussion and Happy Holidays to all

Prashant Mahajan MD MPH MBA
Division Chief and Research Director
Pediatric Emergency Medicine
Professor of Pediatrics & Emergency Medicine
Director Center for Quality and Innovation
Carman & Ann Adams Department of Pediatrics
Children's Hospital of Michigan
Detroit, MI - 48201
Phone: 313-745-5260
Fax: 313-993-7166
Email: pmahajan at dmc.org and mahajan at comcast.net
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From: Karen Cosby [mailto:kcosby40 at GMAIL.COM]
Sent: Monday, December 23, 2013 5:51 PM
Subject: Re: [IMPROVEDX] Missed and Erroneous Diagnoses Common in Primary Care Visits

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 proven.

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

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